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Sample records for gene agamous identified

  1. Regulatory elements of the floral homeotic gene AGAMOUS identified by phylogenetic footprinting and shadowing.

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    Hong, R. L., Hamaguchi, L., Busch, M. A., and Weigel, D.

    2003-06-01

    OAK-B135 In Arabidopsis thaliana, cis-regulatory sequences of the floral homeotic gene AGAMOUS (AG) are located in the second intron. This 3 kb intron contains binding sites for two direct activators of AG, LEAFY (LFY) and WUSCHEL (WUS), along with other putative regulatory elements. We have used phylogenetic footprinting and the related technique of phylogenetic shadowing to identify putative cis-regulatory elements in this intron. Among 29 Brassicaceae, several other motifs, but not the LFY and WUS binding sites previously identified, are largely invariant. Using reporter gene analyses, we tested six of these motifs and found that they are all functionally important for activity of AG regulatory sequences in A. thaliana. Although there is little obvious sequence similarity outside the Brassicaceae, the intron from cucumber AG has at least partial activity in A. thaliana. Our studies underscore the value of the comparative approach as a tool that complements gene-by-gene promoter dissection, but also highlight that sequence-based studies alone are insufficient for a complete identification of cis-regulatory sites.

  2. Gene : CBRC-AGAM-07-0029 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0029 Novel U A UNKNOWN LCN2_LACLA 2e-08 21% ref|NP_241318.1| lantibiotic mersa...cidin modifying enzyme [Bacillus halodurans C-125] dbj|BAB04171.1| lantibiotic mersacidin modifying

  3. Cloning and characterization of prunus serotina AGAMOUS, a putative flower homeotic gene

    Science.gov (United States)

    Xiaomei Liu; Joseph Anderson; Paula Pijut

    2010-01-01

    Members of the AGAMOUS subfamily of MADS-box transcription factors play an important role in regulating the development of reproductive organs in flowering plants. To help understand the mechanism of floral development in black cherry (Prunus serotina), PsAG (a putative flower homeotic identity gene) was isolated...

  4. Functional diversification of AGAMOUS lineage genes in regulating tomato flower and fruit development.

    Science.gov (United States)

    Pan, Irvin L; McQuinn, Ryan; Giovannoni, James J; Irish, Vivian F

    2010-06-01

    AGAMOUS clade genes encode MADS box transcription factors that have been shown to play critical roles in many aspects of flower and fruit development in angiosperms. Tomato possesses two representatives of this lineage, TOMATO AGAMOUS (TAG1) and TOMATO AGAMOUS-LIKE1 (TAGL1), allowing for an analysis of diversification of function after gene duplication. Using RNAi (RNA interference) silencing, transgenic tomato lines that specifically down-regulate either TAGL1 or TAG1 transcript accumulation have been produced. TAGL1 RNAi lines show no defects in stamen or carpel identity, but show defects in fruit ripening. In contrast TAG1 RNAi lines show defects in stamen and carpel development. In addition TAG1 RNAi lines produce red ripe fruit, although they are defective in determinacy and produce ectopic internal fruit structures. e2814, an EMS- (ethyl methane sulphonate) induced mutation that is temperature sensitive and produces fruit phenotypes similar to that of TAG1 RNAi lines, was also characterized. Neither TAG1 nor TAGL1 expression is disrupted in the e2814 mutant, suggesting that the gene corresponding to the e2814 mutant represents a distinct locus that is likely to be functionally downstream of TAG1 and TAGL1. Based on these analyses, possible modes by which these gene duplicates have diversified in terms of their functions and regulatory roles are discussed.

  5. A genetic screen for leaf movement mutants identifies a potential role for AGAMOUS-LIKE 6 (AGL6) in circadian-clock control.

    Science.gov (United States)

    Yoo, Seung Kwan; Hong, Sung Myun; Lee, Jong Seob; Ahn, Ji Hoon

    2011-03-01

    The circadian clock in plants regulates many important physiological and biological processes, including leaf movement. We have used an imaging system to genetically screen Arabidopsis seedlings for altered leaf movement with the aim of identifying a circadian clock gene. A total of 285 genes were selected from publicly available microarrays that showed an expression pattern similar to those of the Arabidopsis core oscillator genes. We subsequently isolated 42 homozygous recessive mutants and analyzed their leaf movements. We also analyzed leaf movements of activation tagging mutants that showed altered flowering time. We found that agl6-1D plants, in which AGAMOUS-LIKE 6 (AGL6) was activated by the 35S enhancer, showed a shortened period of leaf movement as well as a high level of ZEITLUPE (ZTL) expression, reduced amplitude of LATE ELONGATED HYPOCOTYL (LHY) expression, and arrhythmic TIMING OF CAB EXPRESSION1 (TOC1)/CIRCADIAN CLOCK ASSOCIATED1 (CCA1) expression. A shortened period of leaf movement was also seen in 35S-AGL6-myc plants, although 35S-amiRAGL6 plants, transgenic plants overexpressing an artificial miRNA (amiR) targeting AGL6, showed unaltered leaf movement. The amplitude of CHLOROPHYLL A/B BINDING PROTEIN 2 (CAB2) expression, a circadian output gene, was also reduced in agl6-1D plants. Taken together, these results suggest that AGL6 plays a potential role in the regulation of the circadian clock by regulating ZTL mRNA level in Arabidopsis.

  6. Defining the role of the MADS-box gene, Zea agamous like1, in maize domestication

    Science.gov (United States)

    Genomic scans for genes that show the signature of past selection have been widely applied to a number of species and have identified a large number of selection candidate genes. In cultivated maize (Zea mays ssp. mays) selection scans have identified several hundred candidate domestication genes...

  7. Role for the banana AGAMOUS-like gene MaMADS7 in regulation of fruit ripening and quality.

    Science.gov (United States)

    Liu, Juhua; Liu, Lin; Li, Yujia; Jia, Caihong; Zhang, Jianbin; Miao, Hongxia; Hu, Wei; Wang, Zhuo; Xu, Biyu; Jin, Zhiqiang

    2015-11-01

    MADS-box transcription factors play important roles in organ development. In plants, most studies on MADS-box genes have mainly focused on flower development and only a few concerned fruit development and ripening. A new MADS-box gene named MaMADS7 was isolated from banana fruit by rapid amplification of cDNA ends (RACE) based on a MADS-box fragment obtained from a banana suppression subtractive hybridization (SSH) cDNA library. MaMADS7 is an AGAMOUS-like MADS-box gene that is preferentially expressed in the ovaries and fruits and in tobacco its protein product localizes to the nucleus. This study found that MaMADS7 expression can be induced by exogenous ethylene. Ectopic expression of MaMADS7 in tomato resulted in broad ripening phenotypes. The expression levels of seven ripening and quality-related genes, ACO1, ACS2, E4, E8, PG, CNR and PSY1 in MaMADS7 transgenic tomato fruits were greatly increased while the expression of the AG-like MADS-box gene TAGL1 was suppressed. Compared with the control, the contents of β-carotene, lycopene, ascorbic acid and organic acid in transformed tomato fruits were increased, while the contents of glucose and fructose were slightly decreased. MaMADS7 interacted with banana 1-aminocyclopropane-1-carboxylic acid (ACC) oxidase gene 1 (MaACO1) and tomato phytoene synthase gene (LePSY1) promoters. Our results indicated that MaMADS7 plays an important role in initiating endogenous ethylene biosynthesis and fruit ripening.

  8. Transcriptional Activity of the MADS Box ARLEQUIN/TOMATO AGAMOUS-LIKE1 Gene Is Required for Cuticle Development of Tomato Fruit.

    Science.gov (United States)

    Giménez, Estela; Dominguez, Eva; Pineda, Benito; Heredia, Antonio; Moreno, Vicente; Lozano, Rafael; Angosto, Trinidad

    2015-07-01

    Fruit development and ripening entail key biological and agronomic events, which ensure the appropriate formation and dispersal of seeds and determine productivity and yield quality traits. The MADS box gene Arlequin/tomato Agamous-like1 (hereafter referred to as TAGL1) was reported as a key regulator of tomato (Solanum lycopersicum) reproductive development, mainly involved in flower development, early fruit development, and ripening. It is shown here that silencing of the TAGL1 gene (RNA interference lines) promotes significant changes affecting cuticle development, mainly a reduction of thickness and stiffness, as well as a significant decrease in the content of cuticle components (cutin, waxes, polysaccharides, and phenolic compounds). Accordingly, overexpression of TAGL1 significantly increased the amount of cuticle and most of its components while rendering a mechanically weak cuticle. Expression of the genes involved in cuticle biosynthesis agreed with the biochemical and biomechanical features of cuticles isolated from transgenic fruits; it also indicated that TAGL1 participates in the transcriptional control of cuticle development mediating the biosynthesis of cuticle components. Furthermore, cell morphology and the arrangement of epidermal cell layers, on whose activity cuticle formation depends, were altered when TAGL1 was either silenced or constitutively expressed, indicating that this transcription factor regulates cuticle development, probably through the biosynthetic activity of epidermal cells. Our results also support cuticle development as an integrated event in the fruit expansion and ripening processes that characterize fleshy-fruited species such as tomato.

  9. Progress Report for DOE DE-FG03-98ER20317 ''Regulation of the floral homeotic gene AGAMOUS'' Current and Final Funding Period: September 1, 2002, to December 31, 2002

    Energy Technology Data Exchange (ETDEWEB)

    Weigel, D.

    2003-03-11

    OAK-B135 Results obtained during this funding period: (1) Phylogenetic footprinting of AG regulatory sequences Sequences necessary and sufficient for AGAMOUS (AG) expression in the center of Arabidopsis flowers are located in the second intron, which is about 3 kb in size. This intron contains binding sites for two transcription factors, LEAFY (LFY) and WUSCHEL (WUS), which are direct activators of AG. We used the new method of phylogenetic shadowing to identify new regulatory elements. Among 29 Brassicaceae, several other motifs, but not the LFY and WUS binding sites previously identified, are largely invariant. Using reporter gene analyses, we tested six of these motifs and found that they are all functionally important for activity of AG regulatory sequences in A. thaliana. (2) Repression of AG by MADS box genes A candidate for repressing AG in the shoot apical meristem has been the MADS box gene FUL, since it is expressed in the shoot apical meristem and since an activated version (FUL:VP16) leads to ectopic AG expression in the shoot apical meristem. However, there is no ectopic AG expression in full single mutants. We therefore started to generate VP16 fusions of several other MADS box genes expressed in the shoot apical meristem, to determine which of these might be candidates for FUL redundant genes. We found that AGL6:VP16 has a similar phenotype as FUL:VP16, suggesting that AGL6 and FUL interact. We are now testing this hypothesis. (3) Two candidate AG regulators, WOW and ULA Because the phylogenetic footprinting project has identified several new candidate regulatory motifs, of which at least one (the CCAATCA motif) has rather strong effects, we had decided to put the analysis of WOW and ULA on hold, and to focus on using the newly identified motifs as tools. We conduct ed yeast one-hybrid screen with two of the conserved motifs, and identified several classes of transcription factors that can interact with them. One of these is encoded by the PAN gene

  10. Isolation and characterization of an AGAMOUS homologue from cocoa

    NARCIS (Netherlands)

    Chaidamsari, T.; Sugiarit, H.; Santoso, D.; Angenent, G.C.; Maagd, de R.A.

    2006-01-01

    We report the cloning of a cDNA from TcAG, an AG (Arabidopsis thaliana MADS-box C-type transcription factor gene AGAMOUS) homologue from cocoa (Theobroma cacao L.). TcAG was in the cocoa flower expressed primarily in stamens and ovaries, comparable to AG in Arabidopsis. Additionally, we found that T

  11. The Anopheles gambiae odorant binding protein 1 (AgamOBP1 mediates indole recognition in the antennae of female mosquitoes.

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    Harald Biessmann

    Full Text Available Haematophagous insects are frequently carriers of parasitic diseases, including malaria. The mosquito Anopheles gambiae is the major vector of malaria in sub-Saharan Africa and is thus responsible for thousands of deaths daily. Although the role of olfaction in A. gambiae host detection has been demonstrated, little is known about the combinations of ligands and odorant binding proteins (OBPs that can produce specific odor-related responses in vivo. We identified a ligand, indole, for an A. gambiae odorant binding protein, AgamOBP1, modeled the interaction in silico and confirmed the interaction using biochemical assays. RNAi-mediated gene silencing coupled with electrophysiological analyses confirmed that AgamOBP1 binds indole in A. gambiae and that the antennal receptor cells do not respond to indole in the absence of AgamOBP1. This case represents the first documented instance of a specific A. gambiae OBP-ligand pairing combination, demonstrates the significance of OBPs in odor recognition, and can be expanded to the identification of other ligands for OBPs of Anopheles and other medically important insects.

  12. Isolation and characterization of the AGAMOUS homologous gene NTAG in Chinese narcissus (Narcissus tazetta var. Chinensis Roem)

    Institute of Scientific and Technical Information of China (English)

    Wang Zheng-ke; Gao Jian; Li Lu-bin; Peng Zhen-hua

    2006-01-01

    Amaryllidaceae, a monocot plant family, consists of many important ornamental bulb flower species. Chinese narcissus (Narcissus tazetta var. chinensis Roem), its flowers developed at high temperatures and bloomed at lower temperatures during the Chinese Spring Festival, is a traditional Chinese flower with high economic and ornamental value. To study its flower development,a full length cDNA containing MADS box domain from narcissus was isolated by a reverse transcription polymerase chain reaction (RT-PCR) with degenerate oligo-nucleotide primers derived from a conserved MADS- and K-box domain sequence. The 5' and the 3'regions of the gene were amplified using the PCR protocol for the rapid amplification of cDNA ends (RACE). The 690 bp open reading frame encodes 230 amino acid residues. A comparison of the deduced amino acid sequence of NTAG with the sequence of other MADS box proteins showed 91.3% amino acid identities with HAG (Hyacinthus orientalis). Sequence analysis and alignment showed significant similarity with other AG homologues. RNA blot analysis indicated that the narcissus NTAG gene was expressed only in reproductive organs, especially in stamens and carpels. These results indicated that the NTAG gene was an AG homologue and that the AG genes appeared to be structurally and functionally conserved between dicots and monocots.

  13. NIH Researchers Identify OCD Risk Gene

    Science.gov (United States)

    ... News From NIH NIH Researchers Identify OCD Risk Gene Past Issues / Summer 2006 Table of Contents For ... and Alcoholism (NIAAA) have identified a previously unknown gene variant that doubles an individual's risk for obsessive- ...

  14. Exon: CBRC-AGAM-05-0001 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0001 ATATCGCTGCACAATcaccaccatcaccatcaccaccatcatcatcaccacAGCTACCATGGGGC...GCCGGGCAGTGGCGCCCCCTCCcagcagcagcagcagcagcagcagcaccagGCGTCCCAGCAGACGTCGGGCGGCCGGCACCACCACCACCACCAGGGCAGCAGCCG

  15. Exon: CBRC-AGAM-05-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0045 ATTCGACTTCAGCTCGAGGAGGAAAAGGTTTCGATTCGATCTGTGACCGATGCACGGATCAagcatcagcagcagcagcatcagcagcag...catcagcagcatcagcagcatcagcagcatcagcagcagcagcatcagcagcagcagcatcagcagcagcagcatcagcagcagcagcatcagctgcagcagcag...catcagctgcagcagcatcatcagcatcatcagcatcatcagcatcagcagcatcagcagcagcagcagcagcagcagcagcagcagcatcagcatcagcatcagcatcagcatcagcaacagcaCAAGCAGAAACAGTGTACGATAGCA ...

  16. Exon: CBRC-AGAM-07-0076 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0076 caacagaaccaacagcttcagcagcagcagctgcaacagaaactacagcagcagcagcagcagcagcagcaacagaaccgacagcttcag...cagcagcagctgcaacagaaactgcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagctgcagcagcagcag...cagcggcagcagcagcagcagcagcaacagcagcagcagcagcagcagcagcagcagcagcagcaAAGCCTACA ...

  17. Exon: CBRC-AGAM-05-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0045 CCCCCAcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcagcag...cagcagcagcagcagcagcagcagcagcagcagcagcaacaccagcagcagcggcagcgcctgccgcagcgacagcagcagcagcaacaacaccagTAGCGGCCATATGCTACACAGGCGCAGCGCCGTGA ...

  18. Exon: CBRC-AGAM-02-0020 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CCCTCGTAGTTCCGGTCCGGACCACCACCCATCGGCGGCCGGTTGTTGCGCCGGTTGTAGTCCCGCTGTGGCCGCTcgcctccctcgccgccg...gtgcaccctcgcggcccattccacggccaccgccggcgccccggccgccaccgccgcGACCTCCTCTCGAGCGGCCCAATCCACCCCG...cgttgccatcgtcctcctcgccaccgtcggcgccagcgttgccgccCTTCTCCACCTTCGGGTTCGTTTTCCGGTCCAGGGCCACCATCTTGTCCCACTGGCTCGAGTCTTCGCCCTCGCCCG...CBRC-AGAM-02-0020 ATGCTCGTCGTCCACCTTCGGCGCAGCCGGAGCATGCTGCTTGGCCGATCGCGGGTTCGGTCTGCTGTACTTGGTGTTGTTCATGCCG...cgatcctcgtagttgttgtatcggccgcctccggtaccttcgccgccgccctcgccgcctcgatagttgttctcgccgccgccgtactcgcgccgtccgccgcccg

  19. NCBI nr-aa BLAST: CBRC-AGAM-01-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0052 ref|YP_334692.1| putative NAD-specific glutamate dehydrogenase encoded in antisense...ic glutamate dehydrogenase encoded in antisense gene pair with dnaKJ [Burkholderia pseudomallei 1710b] YP_334692.1 2e-04 28% ...

  20. Gene expression profiling: can we identify the right target genes?

    Directory of Open Access Journals (Sweden)

    J. E. Loyd

    2008-12-01

    Full Text Available Gene expression profiling allows the simultaneous monitoring of the transcriptional behaviour of thousands of genes, which may potentially be involved in disease development. Several studies have been performed in idiopathic pulmonary fibrosis (IPF, which aim to define genetic links to the disease in an attempt to improve the current understanding of the underlying pathogenesis of the disease and target pathways for intervention. Expression profiling has shown a clear difference in gene expression between IPF and normal lung tissue, and has identified a wide range of candidate genes, including those known to encode for proteins involved in extracellular matrix formation and degradation, growth factors and chemokines. Recently, familial pulmonary fibrosis cohorts have been examined in an attempt to detect specific genetic mutations associated with IPF. To date, these studies have identified families in which IPF is associated with mutations in the gene encoding surfactant protein C, or with mutations in genes encoding components of telomerase. Although rare and clearly not responsible for the disease in all individuals, the nature of these mutations highlight the importance of the alveolar epithelium in disease pathogenesis and demonstrate the potential for gene expression profiling in helping to advance the current understanding of idiopathic pulmonary fibrosis.

  1. Bioinformatics methods for identifying candidate disease genes

    NARCIS (Netherlands)

    Driel, M.A. van; Brunner, H.G.

    2006-01-01

    With the explosion in genomic and functional genomics information, methods for disease gene identification are rapidly evolving. Databases are now essential to the process of selecting candidate disease genes. Combining positional information with disease characteristics and functional information i

  2. Bioinformatics methods for identifying candidate disease genes

    Directory of Open Access Journals (Sweden)

    van Driel Marc A

    2006-06-01

    Full Text Available Abstract With the explosion in genomic and functional genomics information, methods for disease gene identification are rapidly evolving. Databases are now essential to the process of selecting candidate disease genes. Combining positional information with disease characteristics and functional information is the usual strategy by which candidate disease genes are selected. Enrichment for candidate disease genes, however, depends on the skills of the operating researcher. Over the past few years, a number of bioinformatics methods that enrich for the most likely candidate disease genes have been developed. Such in silico prioritisation methods may further improve by completion of datasets, by development of standardised ontologies across databases and species and, ultimately, by the integration of different strategies.

  3. Identifying novel genes contributing to asthma pathogenesis

    NARCIS (Netherlands)

    Holloway, John W.; Koppelman, Gerard H.

    2007-01-01

    Purpose of review To illustrate recent examples of novel asthma genes such as those encoding G-protein-coupled receptor for asthma susceptibility, filaggrin and tenascin-C, and to describe the process that is needed to translate these findings to the clinic. Recent findings Many hundreds of studies

  4. Gene expression analysis identifies global gene dosage sensitivity in cancer

    DEFF Research Database (Denmark)

    Fehrmann, Rudolf S. N.; Karjalainen, Juha M.; Krajewska, Malgorzata;

    2015-01-01

    expression. We reanalyzed 77,840 expression profiles and observed a limited set of 'transcriptional components' that describe well-known biology, explain the vast majority of variation in gene expression and enable us to predict the biological function of genes. On correcting expression profiles...... for these components, we observed that the residual expression levels (in 'functional genomic mRNA' profiling) correlated strongly with copy number. DNA copy number correlated positively with expression levels for 99% of all abundantly expressed human genes, indicating global gene dosage sensitivity. By applying...

  5. Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

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    Victor M. Bii

    2016-10-01

    Full Text Available Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types.

  6. 'Omics' approaches in tomato aimed at identifying candidate genes ...

    African Journals Online (AJOL)

    adriana

    2013-12-04

    Dec 4, 2013 ... identifying all the components of a single biological system is within our means; however, assigning ... discovery of new candidate genes/QTLs and/or to assign ... identify putative genes involved in their genetic control .... for adaptation to different environments. ..... provides insights into fleshy fruit evolution.

  7. Using biologically interrelated experiments to identify pathway genes in Arabidopsis

    OpenAIRE

    Kim, Kyungpil; Jiang, Keni; Teng, Siew Leng; Feldman, Lewis J.; Huang, Haiyan

    2012-01-01

    Motivation: Pathway genes are considered as a group of genes that work cooperatively in the same pathway constituting a fundamental functional grouping in a biological process. Identifying pathway genes has been one of the major tasks in understanding biological processes. However, due to the difficulty in characterizing/inferring different types of biological gene relationships, as well as several computational issues arising from dealing with high-dimensional biological data, deducing ge...

  8. A predictive approach to identify genes differentially expressed

    Science.gov (United States)

    Saraiva, Erlandson F.; Louzada, Francisco; Milan, Luís A.; Meira, Silvana; Cobre, Juliana

    2012-10-01

    The main objective of gene expression data analysis is to identify genes that present significant changes in expression levels between a treatment and a control biological condition. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating credibility intervals from predictive densities which are constructed using sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained indicate that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a publicly available data set on Escherichia coli bacteria.

  9. Identifying cancer genes from cancer mutation profiles by cancer functions

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    It is of great importance to identify new cancer genes from the data of large scale genome screenings of gene mutations in cancers. Considering the alternations of some essential functions are indispensable for oncogenesis, we define them as cancer functions and select, as their approximations, a group of detailed functions in GO (Gene Ontology) highly enriched with known cancer genes. To evaluate the efficiency of using cancer functions as features to identify cancer genes, we define, in the screened genes, the known protein kinase cancer genes as gold standard positives and the other kinase genes as gold standard negatives. The results show that cancer associated functions are more efficient in identifying cancer genes than the selection pressure feature. Furthermore, combining cancer functions with the number of non-silent mutations can generate more reliable positive predictions. Finally, with precision 0.42, we suggest a list of 46 kinase genes as candidate cancer genes which are annotated to cancer functions and carry at least 3 non-silent mutations.

  10. Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

    Science.gov (United States)

    Wolen, Aaron R; Miles, Michael F

    2012-01-01

    For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

  11. Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes

    NARCIS (Netherlands)

    J. Molenaar (Jan); J. Koster (Jan); D. Zwijnenburg (Danny); P. van Sluis (Peter); L.J. Valentijn (Linda); I. van der Ploeg (Ida); M. Hamdi (Mohamed); J. van Nes (Johan); B.A. Westerman (Bart); J. van Arkel (Jennemiek); M.E. Ebus; F. Haneveld (Franciska); A. Lakeman (Arjan); L. Schild (Linda); P. Molenaar (Piet); P. Stroeken (Peter); M.M. van Noesel (Max); I. Øra (Ingrid); J.P. di Santo (James); H.N. Caron (Huib); E.M. Westerhout (Ellen); R. Versteeg (Rogier)

    2012-01-01

    textabstractNeuroblastoma is a childhood tumour of the peripheral sympathetic nervous system. The pathogenesis has for a long time been quite enigmatic, as only very few gene defects were identified in this often lethal tumour. Frequently detected gene alterations are limited to MYCN amplification (

  12. Rice Transcriptome Analysis to Identify Possible Herbicide Quinclorac Detoxification Genes

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    Wenying eXu

    2015-09-01

    Full Text Available Quinclorac is a highly selective auxin-type herbicide, and is widely used in the effective control of barnyard grass in paddy rice fields, improving the world’s rice yield. The herbicide mode of action of quinclorac has been proposed and hormone interactions affect quinclorac signaling. Because of widespread use, quinclorac may be transported outside rice fields with the drainage waters, leading to soil and water pollution and environmental health problems.In this study, we used 57K Affymetrix rice whole-genome array to identify quinclorac signaling response genes to study the molecular mechanisms of action and detoxification of quinclorac in rice plants. Overall, 637 probe sets were identified with differential expression levels under either 6 or 24 h of quinclorac treatment. Auxin-related genes such as GH3 and OsIAAs responded to quinclorac treatment. Gene Ontology analysis showed that genes of detoxification-related family genes were significantly enriched, including cytochrome P450, GST, UGT, and ABC and drug transporter genes. Moreover, real-time RT-PCR analysis showed that top candidate P450 families such as CYP81, CYP709C and CYP72A genes were universally induced by different herbicides. Some Arabidopsis genes for the same P450 family were up-regulated under quinclorac treatment.We conduct rice whole-genome GeneChip analysis and the first global identification of quinclorac response genes. This work may provide potential markers for detoxification of quinclorac and biomonitors of environmental chemical pollution.

  13. Identifying glioblastoma gene networks based on hypergeometric test analysis.

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    Vasileios Stathias

    Full Text Available Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA. To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM. We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers.

  14. Gene-based Association Approach Identify Genes Across Stress Traits in Fruit Flies

    DEFF Research Database (Denmark)

    Rohde, Palle Duun; Edwards, Stefan McKinnon; Sarup, Pernille Merete;

    approach grouping variants accordingly to gene position, thus lowering the number of statistical tests performed and increasing the probability of identifying genes with small to moderate effects. Using this approach we identify numerous genes associated with different types of stresses in Drosophila...

  15. Sleeping Beauty mouse models identify candidate genes involved in gliomagenesis.

    Science.gov (United States)

    Vyazunova, Irina; Maklakova, Vilena I; Berman, Samuel; De, Ishani; Steffen, Megan D; Hong, Won; Lincoln, Hayley; Morrissy, A Sorana; Taylor, Michael D; Akagi, Keiko; Brennan, Cameron W; Rodriguez, Fausto J; Collier, Lara S

    2014-01-01

    Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP) compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma.

  16. Tracing evolutionary footprints to identify novel gene functional linkages.

    Directory of Open Access Journals (Sweden)

    Yong Chen

    Full Text Available Systematic determination of gene function is an essential step in fully understanding the precise contribution of each gene for the proper execution of molecular functions in the cell. Gene functional linkage is defined as to describe the relationship of a group of genes with similar functions. With thousands of genomes sequenced, there arises a great opportunity to utilize gene evolutionary information to identify gene functional linkages. To this end, we established a computational method (called TRACE to trace gene footprints through a gene functional network constructed from 341 prokaryotic genomes. TRACE performance was validated and successfully tested to predict enzyme functions as well as components of pathway. A so far undescribed chromosome partitioning-like protein ro03654 of an oleaginous bacteria Rhodococcus sp. RHA1 (RHA1 was predicted and verified experimentally with its deletion mutant showing growth inhibition compared to RHA1 wild type. In addition, four proteins were predicted to act as prokaryotic SNARE-like proteins, and two of them were shown to be localized at the plasma membrane. Thus, we believe that TRACE is an effective new method to infer prokaryotic gene functional linkages by tracing evolutionary events.

  17. The genetics of alcoholism: identifying specific genes through family studies.

    Science.gov (United States)

    Edenberg, Howard J; Foroud, Tatiana

    2006-09-01

    Alcoholism is a complex disorder with both genetic and environmental risk factors. Studies in humans have begun to elucidate the genetic underpinnings of the risk for alcoholism. Here we briefly review strategies for identifying individual genes in which variations affect the risk for alcoholism and related phenotypes, in the context of one large study that has successfully identified such genes. The Collaborative Study on the Genetics of Alcoholism (COGA) is a family-based study that has collected detailed phenotypic data on individuals in families with multiple alcoholic members. A genome-wide linkage approach led to the identification of chromosomal regions containing genes that influenced alcoholism risk and related phenotypes. Subsequently, single nucleotide polymorphisms (SNPs) were genotyped in positional candidate genes located within the linked chromosomal regions, and analyzed for association with these phenotypes. Using this sequential approach, COGA has detected association with GABRA2, CHRM2 and ADH4; these associations have all been replicated by other researchers. COGA has detected association to additional genes including GABRG3, TAS2R16, SNCA, OPRK1 and PDYN, results that are awaiting confirmation. These successes demonstrate that genes contributing to the risk for alcoholism can be reliably identified using human subjects.

  18. Identifying gene regulatory network rewiring using latent differential graphical models.

    Science.gov (United States)

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-09-30

    Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.

  19. A reexamination of the North American Crepis agamic complex and comparison with the findings of Babcock and Stebbins' classic biosystematic monograph.

    Science.gov (United States)

    Sears, Christopher J; Whitton, Jeannette

    2016-07-01

    Babcock and Stebbins coined the term agamic complex in their 1938 monograph of the North American Crepis agamic complex. Despite the historical role that this complex holds in the evolutionary literature, it has not been reexamined in over 75 years. We present a thorough reevaluation of the complex to test hypotheses proposed by Babcock and Stebbins about its origins and spread, the relationships of diploids, and the nature and origins of polyploids. We used flow cytometry to infer ploidy of roughly 600 samples spanning the morphological and taxonomic diversity of the complex and a phylogenetic analysis of plastid DNA variation to infer maternal relationships among diploids and to infer maternal origins of polyploids. We identified populations of all seven recognized diploids plus one new lineage. Phylogenetic analysis of plastid DNA variation in diploids revealed a well-resolved, but moderately supported phylogeny, with evidence for monophyly of the North America Crepis agamic complex and no evidence of widespread homoploid hybridization. Polyploids showed evidence of multiple origins and a pattern of frequent local co-occurrence consistent with repeated colonization of suitable sites. Our findings agree broadly with the distribution and variation of ploidy within and among species described by Babcock and Stebbins. One key difference is finding support for monophyly of North American species, and refuting their hypothesis of polyphyly. Our results provide an explicit phylogenetic framework for further study of this classic agamic complex. © 2016 Botanical Society of America.

  20. Isolation and characterization of an AGAMOUS homolog from Fraxinus pennsylvanica

    Science.gov (United States)

    Ningxia Du; Paula M. Pijut

    2010-01-01

    An AGAMOUS homolog (FpAG) was isolated from green ash (Fraxinus pennsylvanica) using a reverse transcriptase polymerase chain reaction method. Southern blot analysis indicated that FpAG was present as a single-copy sequence in the genome of green ash. RNA accumulated in the reproductive tissues (female...

  1. A 6-gene signature identifies four molecular subgroups of neuroblastoma

    Directory of Open Access Journals (Sweden)

    Kogner Per

    2011-04-01

    Full Text Available Abstract Background There are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB; Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis. Results The present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples. Four distinct clusters were identified by Principal Components Analysis (PCA in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p ALK, BIRC5, and PHOX2B, and was significantly associated with higher tumour stage, poor outcome and poor survival compared to the Type 1-corresponding favourable group (INSS stage 4 and/or dead of disease, p Conclusions Based on expression profiling we have identified four molecular subgroups of neuroblastoma, which can be distinguished by a 6-gene signature. The fourth subgroup has not been described elsewhere, and efforts are currently made to further investigate this group's specific characteristics.

  2. Cancer therapeutic target genes identified on chromosome 20q

    Directory of Open Access Journals (Sweden)

    Editorial Office

    2016-08-01

    Full Text Available An integrated quantitative genome data analysis was recently able to pinpoint 18 genes on human chromosome 20q that could potentially serve as novel molecular targets for cancer therapy. Researchers Antoine M Snijders and Jian-Hua Mao from Lawrence Berkeley National Laboratory’s Biological Systems and Engineering Division in Berkeley, California, United States, in their study published by the journal Advances in Modern Oncology Research (AMOR sought to compare the amounts of individual mRNAs – messenger RNAs that specify the amino acid sequence of the protein products of gene expression – in cancerous human tissues with corresponding normal tissues. The duo conducted a meta-analysis of genes on chromosome 20q that are found to be consistently upregulated across different human tumor types, while collecting gene transcript data of normal and tumor tissues across 11 different tumor types including brain, breast, colon, gastric, head and neck, liver, lung, ovarian, cervix, pancreas, and prostate cancers. “We calculated the differential expression of all 301 genes present on chromosome 20q for which gene transcript data was available. We then filtered for genes that were upregulated in tumors by at least 1.5 fold (p < 0.05 in seven or more tumor types,” they said. The resulting analysis identified 18 genes – some such as AURKA, UBE2C, TPX2, FAM83D, ZNF217, SALL4 and MMP9 have been previously known to potentially cause cancer. The 18-gene signature is revealed by the study to have robustly elevated levels across human cancers. “We observed significant association of our signature with disease-free survival in all 18 independent data… These data indicated that our signature is broadly predictive for disease-free survival, independent of tumor type,” the researchers said. In certain cases, Snijders and Mao found that increased gene expression was associated with better prognosis. “For example, the increased expressions of MMP9 and

  3. Contemporary Approaches for Identifying Rare Bone Disease Causing Genes

    Institute of Scientific and Technical Information of China (English)

    Charles R.Farber; Thomas L.Clemens

    2013-01-01

    Recent improvements in the speed and accuracy of DNA sequencing, together with increasingly sophisti-cated mathematical approaches for annotating gene networks, have revolutionized the field of human genetics and made these once time consuming approaches assessable to most investigators. In the field of bone research, a particularly active area of gene discovery has occurred in patients with rare bone disorders such as osteogenesis imperfecta (OI) that are caused by mutations in single genes. In this perspective, we highlight some of these technological advances and describe how they have been used to identify the genetic determinants underlying two previously unexplained cases of OI. The widespread availability of advanced methods for DNA sequencing and bioinformatics analysis can be expected to greatly facilitate identification of novel gene networks that normally function to control bone formation and maintenance.

  4. Functional epigenomics identifies genes frequently silenced in prostate cancer.

    Science.gov (United States)

    Lodygin, Dimitri; Epanchintsev, Alexey; Menssen, Antje; Diebold, Joachim; Hermeking, Heiko

    2005-05-15

    In many cases, silencing of gene expression by CpG methylation is causally involved in carcinogenesis. Furthermore, cancer-specific CpG methylation may serve as a tumor marker. In order to identify candidate genes for inactivation by CpG methylation in prostate cancer, the prostate cancer cell lines LNCaP, PC3, and Du-145 were treated with 5-aza-2' deoxycytidine and trichostatin A, which leads to reversion of epigenetic silencing. By microarray analysis of 18,400 individual transcripts, several hundred genes were found to be induced when compared with cells treated with trichostatin A. Fifty re-expressed genes were selected for further analysis based on their known function, which implied a possible involvement in tumor suppression. Twelve of these genes showed a significant degree of CpG methylation in their promoters. Six genes were silenced by CpG methylation in the majority of five analyzed prostate cancer cell lines, although they displayed robust mRNA expression in normal prostate epithelial cells obtained from four different donors. In primary prostate cancer samples derived from 41 patients, the frequencies of CpG methylation detected in the promoter regions of these genes were: GPX3, 93%; SFRP1, 83%; COX2, 78%; DKK3, 68%; GSTM1, 58%; and KIP2/p57, 56%. Ectopic expression of SFRP1 or DKK3 resulted in decreased proliferation. The expression of DKK3 was accompanied by attenuation of the mitogen-activated protein kinase pathway. The high frequency of CpG methylation detected in the promoters of the identified genes suggests a potential causal involvement in prostate cancer and may prove useful for diagnostic purposes.

  5. Identifying genes for neurobehavioural traits in rodents: progress and pitfalls

    Directory of Open Access Journals (Sweden)

    Amelie Baud

    2017-04-01

    Full Text Available Identifying genes and pathways that contribute to differences in neurobehavioural traits is a key goal in psychiatric research. Despite considerable success in identifying quantitative trait loci (QTLs associated with behaviour in laboratory rodents, pinpointing the causal variants and genes is more challenging. For a long time, the main obstacle was the size of QTLs, which could encompass tens if not hundreds of genes. However, recent studies have exploited mouse and rat resources that allow mapping of phenotypes to narrow intervals, encompassing only a few genes. Here, we review these studies, showcase the rodent resources they have used and highlight the insights into neurobehavioural traits provided to date. We discuss what we see as the biggest challenge in the field – translating QTLs into biological knowledge by experimentally validating and functionally characterizing candidate genes – and propose that the CRISPR/Cas genome-editing system holds the key to overcoming this obstacle. Finally, we challenge traditional views on inbred versus outbred resources in the light of recent resource and technology developments.

  6. Identifying genes for neurobehavioural traits in rodents: progress and pitfalls.

    Science.gov (United States)

    Baud, Amelie; Flint, Jonathan

    2017-04-01

    Identifying genes and pathways that contribute to differences in neurobehavioural traits is a key goal in psychiatric research. Despite considerable success in identifying quantitative trait loci (QTLs) associated with behaviour in laboratory rodents, pinpointing the causal variants and genes is more challenging. For a long time, the main obstacle was the size of QTLs, which could encompass tens if not hundreds of genes. However, recent studies have exploited mouse and rat resources that allow mapping of phenotypes to narrow intervals, encompassing only a few genes. Here, we review these studies, showcase the rodent resources they have used and highlight the insights into neurobehavioural traits provided to date. We discuss what we see as the biggest challenge in the field - translating QTLs into biological knowledge by experimentally validating and functionally characterizing candidate genes - and propose that the CRISPR/Cas genome-editing system holds the key to overcoming this obstacle. Finally, we challenge traditional views on inbred versus outbred resources in the light of recent resource and technology developments. © 2017. Published by The Company of Biologists Ltd.

  7. Identifying disease feature genes based on cellular localized gene functional modules and regulation networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Min; ZHU Jing; GUO Zheng; LI Xia; YANG Da; WANG Lei; RAO Shaoqi

    2006-01-01

    Identifying disease-relevant genes and functional modules, based on gene expression profiles and gene functional knowledge, is of high importance for studying disease mechanisms and subtyping disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology, we propose an approach to selecting functional modules enriched with differentially expressed genes, and identifying the feature functional modules of high disease discriminating abilities. Using the differentially expressed genes in each feature module as the feature genes, we reveal the relevance of the modules to the studied diseases. Using three datasets for prostate cancer, gastric cancer, and leukemia, we have demonstrated that the proposed modular approach is of high power in identifying functionally integrated feature gene subsets that are highly relevant to the disease mechanisms. Our analysis has also shown that the critical disease-relevant genes might be better recognized from the gene regulation network, which is constructed using the characterized functional modules, giving important clues to the concerted mechanisms of the modules responding to complex disease states. In addition, the proposed approach to selecting the disease-relevant genes by jointly considering the gene functional knowledge suggests a new way for precisely classifying disease samples with clear biological interpretations, which is critical for the clinical diagnosis and the elucidation of the pathogenic basis of complex diseases.

  8. Expression Profiling Identifies Candidate Genes for Fiber Yield and Quality

    Institute of Scientific and Technical Information of China (English)

    LLEWELLYN D J; MACHADO A; AI-GHAZI Y; WU Y; DENNIS E S

    2008-01-01

    @@ Gene expression profiling at early stages (0~2 DPA) of fiber development in Gossypiurn hirsuturn identified a number of transcription factors which were down regulated in fiberless mutants relative to wild type controls and which could play a role in controlling early fiber development.Chief among these was GhMYB25,a Mixta-like MYB gene.Transgenic GhMYB25-silenced cotton showeddramatic alterations in fiber initiation and the timing of rapid fiber elongation,reduction in trichomes on other parts of the plant,a delay in lateral root growth,and a reduction in seed production due toreduced fertilization efficiency.

  9. Using SCOPE to identify potential regulatory motifs in coregulated genes.

    Science.gov (United States)

    Martyanov, Viktor; Gross, Robert H

    2011-05-31

    SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data. In this article, we utilize a web version of SCOPE to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs and has been used in other studies. The three algorithms that comprise SCOPE are BEAM, which finds non-degenerate motifs (ACCGGT), PRISM, which finds degenerate motifs (ASCGWT), and SPACER, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from

  10. [Key effect genes responding to nerve injury identified by gene ontology and computer pattern recognition].

    Science.gov (United States)

    Pan, Qian; Peng, Jin; Zhou, Xue; Yang, Hao; Zhang, Wei

    2012-07-01

    In order to screen out important genes from large gene data of gene microarray after nerve injury, we combine gene ontology (GO) method and computer pattern recognition technology to find key genes responding to nerve injury, and then verify one of these screened-out genes. Data mining and gene ontology analysis of gene chip data GSE26350 was carried out through MATLAB software. Cd44 was selected from screened-out key gene molecular spectrum by comparing genes' different GO terms and positions on score map of principal component. Function interferences were employed to influence the normal binding of Cd44 and one of its ligands, chondroitin sulfate C (CSC), to observe neurite extension. Gene ontology analysis showed that the first genes on score map (marked by red *) mainly distributed in molecular transducer activity, receptor activity, protein binding et al molecular function GO terms. Cd44 is one of six effector protein genes, and attracted us with its function diversity. After adding different reagents into the medium to interfere the normal binding of CSC and Cd44, varying-degree remissions of CSC's inhibition on neurite extension were observed. CSC can inhibit neurite extension through binding Cd44 on the neuron membrane. This verifies that important genes in given physiological processes can be identified by gene ontology analysis of gene chip data.

  11. Identifying candidate driver genes by integrative ovarian cancer genomics data

    Science.gov (United States)

    Lu, Xinguo; Lu, Jibo

    2017-08-01

    Integrative analysis of molecular mechanics underlying cancer can distinguish interactions that cannot be revealed based on one kind of data for the appropriate diagnosis and treatment of cancer patients. Tumor samples exhibit heterogeneity in omics data, such as somatic mutations, Copy Number Variations CNVs), gene expression profiles and so on. In this paper we combined gene co-expression modules and mutation modulators separately in tumor patients to obtain the candidate driver genes for resistant and sensitive tumor from the heterogeneous data. The final list of modulators identified are well known in biological processes associated with ovarian cancer, such as CCL17, CACTIN, CCL16, CCL22, APOB, KDF1, CCL11, HNF1B, LRG1, MED1 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

  12. A 6-gene signature identifies four molecular subgroups of neuroblastoma

    LENUS (Irish Health Repository)

    Abel, Frida

    2011-04-14

    Abstract Background There are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB); Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA) and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK) was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis. Results The present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples). Four distinct clusters were identified by Principal Components Analysis (PCA) in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples) using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p < 0.05, one-way ANOVA test). PCA clusters p1, p2, and p3 were found to correspond well to the postulated subtypes 1, 2A, and 2B, respectively. Remarkably, a fourth novel cluster was detected in all three independent data sets. This cluster comprised mainly 11q-deleted MNA-negative tumours with low expression of ALK, BIRC5, and PHOX2B, and was significantly associated with higher tumour stage, poor outcome and poor survival compared to the Type 1-corresponding favourable group (INSS stage 4 and\\/or dead of disease, p < 0.05, Fisher\\'s exact test). Conclusions Based on expression profiling we have identified four molecular subgroups of neuroblastoma, which can be distinguished by a 6-gene signature. The fourth subgroup has not been described elsewhere, and efforts are currently made to further investigate this group\\'s specific characteristics.

  13. A whole genome RNAi screen identifies replication stress response genes.

    Science.gov (United States)

    Kavanaugh, Gina; Ye, Fei; Mohni, Kareem N; Luzwick, Jessica W; Glick, Gloria; Cortez, David

    2015-11-01

    Proper DNA replication is critical to maintain genome stability. When the DNA replication machinery encounters obstacles to replication, replication forks stall and the replication stress response is activated. This response includes activation of cell cycle checkpoints, stabilization of the replication fork, and DNA damage repair and tolerance mechanisms. Defects in the replication stress response can result in alterations to the DNA sequence causing changes in protein function and expression, ultimately leading to disease states such as cancer. To identify additional genes that control the replication stress response, we performed a three-parameter, high content, whole genome siRNA screen measuring DNA replication before and after a challenge with replication stress as well as a marker of checkpoint kinase signalling. We identified over 200 replication stress response genes and subsequently analyzed how they influence cellular viability in response to replication stress. These data will serve as a useful resource for understanding the replication stress response.

  14. Sleeping Beauty mouse models identify candidate genes involved in gliomagenesis.

    Directory of Open Access Journals (Sweden)

    Irina Vyazunova

    Full Text Available Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma.

  15. Sleeping Beauty Mouse Models Identify Candidate Genes Involved in Gliomagenesis

    Science.gov (United States)

    Vyazunova, Irina; Maklakova, Vilena I.; Berman, Samuel; De, Ishani; Steffen, Megan D.; Hong, Won; Lincoln, Hayley; Morrissy, A. Sorana; Taylor, Michael D.; Akagi, Keiko; Brennan, Cameron W.; Rodriguez, Fausto J.; Collier, Lara S.

    2014-01-01

    Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP) compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma. PMID:25423036

  16. Gene-based Association Approach Identify Genes Across Stress Traits in Fruit Flies

    DEFF Research Database (Denmark)

    Rohde, Palle Duun; Edwards, Stefan McKinnon; Sarup, Pernille Merete

    Identification of genes explaining variation in quantitative traits or genetic risk factors of human diseases requires both good phenotypic- and genotypic data, but also efficient statistical methods. Genome-wide association studies may reveal association between phenotypic variation and variation...... at nucleotide level, thus potentially identify genetic variants. However, testing million of polymorphic nucleotide positions requires conservative correction for multiple testing which lowers the probability of finding genes with small to moderate effects. To alleviate this, we apply a gene based association...... approach grouping variants accordingly to gene position, thus lowering the number of statistical tests performed and increasing the probability of identifying genes with small to moderate effects. Using this approach we identify numerous genes associated with different types of stresses in Drosophila...

  17. A sequence-based approach to identify reference genes for gene expression analysis

    Directory of Open Access Journals (Sweden)

    Chari Raj

    2010-08-01

    Full Text Available Abstract Background An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. Moreover, reference genes suitable in one context (e.g. lung cancer may not be suitable in another (e.g. breast cancer. Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. A caveat here is the requirement for transcript normalization prior to analysis, and measurements obtained are relative, not absolute. Alternatively, as sequencing-based technologies provide digital quantitative output, absolute quantification ensues, and reference gene identification becomes more accurate. Methods Serial analysis of gene expression (SAGE profiles of non-malignant and malignant lung samples were compared using a permutation test to identify the most stably expressed genes across all samples. Subsequently, the specificity of the reference genes was evaluated across multiple tissue types, their constancy of expression was assessed using quantitative RT-PCR (qPCR, and their impact on differential expression analysis of microarray data was evaluated. Results We show that (i conventional references genes such as ACTB and GAPDH are highly variable between cancerous and non-cancerous samples, (ii reference genes identified for lung cancer do not perform well for other cancer types (breast and brain, (iii reference genes identified through SAGE show low variability using qPCR in a different cohort of samples, and (iv normalization of a lung cancer gene expression microarray dataset with or without our reference genes, yields different results for differential gene expression and subsequent analyses. Specifically, key established pathways in lung

  18. Strategies to identify long noncoding RNAs involved in gene regulation

    Directory of Open Access Journals (Sweden)

    Lee Catherine

    2012-11-01

    Full Text Available Abstract Long noncoding RNAs (lncRNAs have been detected in nearly every cell type and found to be fundamentally involved in many biological processes. The characterization of lncRNAs has immense potential to advance our comprehensive understanding of cellular processes and gene regulation, along with implications for the treatment of human disease. The recent ENCODE (Encyclopedia of DNA Elements study reported 9,640 lncRNA loci in the human genome, which corresponds to around half the number of protein-coding genes. Because of this sheer number and their functional diversity, it is crucial to identify a pool of potentially relevant lncRNAs early on in a given study. In this review, we evaluate the methods for isolating lncRNAs by immunoprecipitation and review the advantages, disadvantages, and applications of three widely used approaches – microarray, tiling array, and RNA-seq – for identifying lncRNAs involved in gene regulation. We also look at ways in which data from publicly available databases such as ENCODE can support the study of lncRNAs.

  19. Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

    Science.gov (United States)

    Jiang, Bing; Li, Shuwen; Jiang, Zhi

    2017-01-01

    Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding. PMID:28232943

  20. Identified Circadian Rhythm Genes of Ciliary Epithelium with Differential Display

    Institute of Scientific and Technical Information of China (English)

    Yanxia Li; Dongcheng Lu; Jian Ge; Yanna Li; Yehong Zhuo; Sears ML

    2001-01-01

    Purpose:To identify differential genes expressed in the rabbit ciliary epithelium duringthe circadian cycle of aqueous flow.Methods: Total RNA from ciliary epithelium of rabbits at 8AM (light on 1 hour) and8PM(light off 1 hour) were compared by differential display reverse transcription-polymerase chain reaetion(DD RT-PCR), using 6 % denaturing polyacrylamide electro-phoresis, choose differential display bands, cut and reamplify with the same primer, cloneand sequence. Search the database of Genbank, prolong them with 5' RACE and 3'RACE technique then clone, sequence and search database of Genbank.Results: 93 Significant differences gene expression were detected between light on andlight off in the rabbit ciliary epithelium.Conclusion: Differential display is a powerful tool to screen differentially expressedgenes in circadian rhythm of ciliary epithelium.

  1. Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

    Directory of Open Access Journals (Sweden)

    Bert van der Zwaag

    Full Text Available The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD, and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.

  2. Identifying genes that mediate anthracyline toxicity in immune cells

    Directory of Open Access Journals (Sweden)

    Amber eFrick

    2015-04-01

    Full Text Available The role of the immune system in response to chemotherapeutic agents remains elusive. The interpatient variability observed in immune and chemotherapeutic cytotoxic responses is likely, at least in part, due to complex genetic differences. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at identifying genes underlying these chemotherapeutic cytotoxic effects on immune cells. Using genome-wide association studies (GWAS, we identified four genome-wide significant quantitative trait loci (QTL that contributed to the sensitivity of doxorubicin and idarubicin in immune cells. Of particular interest, a locus on chromosome 16 was significantly associated with cell viability following idarubicin administration (p = 5.01x10-8. Within this QTL lies App, which encodes amyloid beta precursor protein. Comparison of dose-response curves verified that T-cells in App knockout mice were more sensitive to idarubicin than those of C57BL/6J control mice (p < 0.05.In conclusion, the cellular screening approach coupled with GWAS led to the identification and subsequent validation of a gene involved in T-cell viability after idarubicin treatment. Previous studies have suggested a role for App in in vitro and in vivo cytotoxicity to anticancer agents; the overexpression of App enhances resistance, while the knockdown of this gene is deleterious to cell viability. Thus, further investigations should include performing mechanistic studies, validating additional genes from the GWAS, including Ppfia1 and Ppfibp1, and ultimately translating the findings to in vivo and human studies.

  3. When noisy neighbors are a blessing: analysis of gene expression noise identifies coregulated genes

    NARCIS (Netherlands)

    Junker, J.P.; van Oudenaarden, A.

    2012-01-01

    In this issue of Molecular Cell, Stewart-Ornstein et al. (2012) use systematic pair-wise correlation analysis of expression noise in a large number of yeast genes to identify clusters of functionally related genes and signaling pathways responsible for elevated noise.

  4. Identifying redundant and missing relations in the gene ontology.

    Science.gov (United States)

    Mougin, Fleur

    2015-01-01

    Significant efforts have been undertaken for providing the Gene Ontology (GO) in a computable format as well as for enriching it with logical definitions. Automated approaches can thus be applied to GO for assisting its maintenance and for checking its internal coherence. However, inconsistencies may still remain within GO. In this frame, the objective of this work was to audit GO relationships. First, reasoning over relationships was exploited for detecting redundant relations existing between GO concepts. Missing necessary and sufficient conditions were then identified based on the compositional structure of the preferred names of GO concepts. More than one thousand redundant relations and 500 missing necessary and sufficient conditions were found. The proposed approach was thus successful for detecting inconsistencies within GO relations. The application of lexical approaches as well as the exploitation of synonyms and textual definitions could be useful for identifying additional necessary and sufficient conditions. Multiple necessary and sufficient conditions for a given GO concept may be indicative of inconsistencies.

  5. Identifying the genes of unconventional high temperature superconductors.

    Science.gov (United States)

    Hu, Jiangping

    We elucidate a recently emergent framework in unifying the two families of high temperature (high [Formula: see text]) superconductors, cuprates and iron-based superconductors. The unification suggests that the latter is simply the counterpart of the former to realize robust extended s-wave pairing symmetries in a square lattice. The unification identifies that the key ingredients (gene) of high [Formula: see text] superconductors is a quasi two dimensional electronic environment in which the d-orbitals of cations that participate in strong in-plane couplings to the p-orbitals of anions are isolated near Fermi energy. With this gene, the superexchange magnetic interactions mediated by anions could maximize their contributions to superconductivity. Creating the gene requires special arrangements between local electronic structures and crystal lattice structures. The speciality explains why high [Formula: see text] superconductors are so rare. An explicit prediction is made to realize high [Formula: see text] superconductivity in Co/Ni-based materials with a quasi two dimensional hexagonal lattice structure formed by trigonal bipyramidal complexes.

  6. Utilizing Gene Tree Variation to Identify Candidate Effector Genes in Zymoseptoria tritici

    Directory of Open Access Journals (Sweden)

    Megan C. McDonald

    2016-04-01

    Full Text Available Zymoseptoria tritici is a host-specific, necrotrophic pathogen of wheat. Infection by Z. tritici is characterized by its extended latent period, which typically lasts 2 wks, and is followed by extensive host cell death, and rapid proliferation of fungal biomass. This work characterizes the level of genomic variation in 13 isolates, for which we have measured virulence on 11 wheat cultivars with differential resistance genes. Between the reference isolate, IPO323, and the 13 Australian isolates we identified over 800,000 single nucleotide polymorphisms, of which ∼10% had an effect on the coding regions of the genome. Furthermore, we identified over 1700 probable presence/absence polymorphisms in genes across the Australian isolates using de novo assembly. Finally, we developed a gene tree sorting method that quickly identifies groups of isolates within a single gene alignment whose sequence haplotypes correspond with virulence scores on a single wheat cultivar. Using this method, we have identified < 100 candidate effector genes whose gene sequence correlates with virulence toward a wheat cultivar carrying a major resistance gene.

  7. Identifying sexual differentiation genes that affect Drosophila life span

    Directory of Open Access Journals (Sweden)

    Tower John

    2009-12-01

    Full Text Available Abstract Background Sexual differentiation often has significant effects on life span and aging phenotypes. For example, males and females of several species have different life spans, and genetic and environmental manipulations that affect life span often have different magnitude of effect in males versus females. Moreover, the presence of a differentiated germ-line has been shown to affect life span in several species, including Drosophila and C. elegans. Methods Experiments were conducted to determine how alterations in sexual differentiation gene activity might affect the life span of Drosophila melanogaster. Drosophila females heterozygous for the tudor[1] mutation produce normal offspring, while their homozygous sisters produce offspring that lack a germ line. To identify additional sexual differentiation genes that might affect life span, the conditional transgenic system Geneswitch was employed, whereby feeding adult flies or developing larvae the drug RU486 causes the over-expression of selected UAS-transgenes. Results In this study germ-line ablation caused by the maternal tudor[1] mutation was examined in a long-lived genetic background, and was found to increase life span in males but not in females, consistent with previous reports. Fitting the data to a Gompertz-Makeham model indicated that the maternal tudor[1] mutation increases the life span of male progeny by decreasing age-independent mortality. The Geneswitch system was used to screen through several UAS-type and EP-type P element mutations in genes that regulate sexual differentiation, to determine if additional sex-specific effects on life span would be obtained. Conditional over-expression of transformer female isoform (traF during development produced male adults with inhibited sexual differentiation, however this caused no significant change in life span. Over-expression of doublesex female isoform (dsxF during development was lethal to males, and produced a limited

  8. Blood pressure loci identified with a gene-centric array.

    Science.gov (United States)

    Johnson, Toby; Gaunt, Tom R; Newhouse, Stephen J; Padmanabhan, Sandosh; Tomaszewski, Maciej; Kumari, Meena; Morris, Richard W; Tzoulaki, Ioanna; O'Brien, Eoin T; Poulter, Neil R; Sever, Peter; Shields, Denis C; Thom, Simon; Wannamethee, Sasiwarang G; Whincup, Peter H; Brown, Morris J; Connell, John M; Dobson, Richard J; Howard, Philip J; Mein, Charles A; Onipinla, Abiodun; Shaw-Hawkins, Sue; Zhang, Yun; Davey Smith, George; Day, Ian N M; Lawlor, Debbie A; Goodall, Alison H; Fowkes, F Gerald; Abecasis, Gonçalo R; Elliott, Paul; Gateva, Vesela; Braund, Peter S; Burton, Paul R; Nelson, Christopher P; Tobin, Martin D; van der Harst, Pim; Glorioso, Nicola; Neuvrith, Hani; Salvi, Erika; Staessen, Jan A; Stucchi, Andrea; Devos, Nabila; Jeunemaitre, Xavier; Plouin, Pierre-François; Tichet, Jean; Juhanson, Peeter; Org, Elin; Putku, Margus; Sõber, Siim; Veldre, Gudrun; Viigimaa, Margus; Levinsson, Anna; Rosengren, Annika; Thelle, Dag S; Hastie, Claire E; Hedner, Thomas; Lee, Wai K; Melander, Olle; Wahlstrand, Björn; Hardy, Rebecca; Wong, Andrew; Cooper, Jackie A; Palmen, Jutta; Chen, Li; Stewart, Alexandre F R; Wells, George A; Westra, Harm-Jan; Wolfs, Marcel G M; Clarke, Robert; Franzosi, Maria Grazia; Goel, Anuj; Hamsten, Anders; Lathrop, Mark; Peden, John F; Seedorf, Udo; Watkins, Hugh; Ouwehand, Willem H; Sambrook, Jennifer; Stephens, Jonathan; Casas, Juan-Pablo; Drenos, Fotios; Holmes, Michael V; Kivimaki, Mika; Shah, Sonia; Shah, Tina; Talmud, Philippa J; Whittaker, John; Wallace, Chris; Delles, Christian; Laan, Maris; Kuh, Diana; Humphries, Steve E; Nyberg, Fredrik; Cusi, Daniele; Roberts, Robert; Newton-Cheh, Christopher; Franke, Lude; Stanton, Alice V; Dominiczak, Anna F; Farrall, Martin; Hingorani, Aroon D; Samani, Nilesh J; Caulfield, Mark J; Munroe, Patricia B

    2011-12-09

    Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 × 10(-7) study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r(2) = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery and follow-up data identified SNPs significantly associated with BP at p < 8.56 × 10(-7) at four further loci (NPR3, HFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies.

  9. Reconstructability analysis as a tool for identifying gene-gene interactions in studies of human diseases.

    Science.gov (United States)

    Shervais, Stephen; Kramer, Patricia L; Westaway, Shawn K; Cox, Nancy J; Zwick, Martin

    2010-01-01

    There are a number of common human diseases for which the genetic component may include an epistatic interaction of multiple genes. Detecting these interactions with standard statistical tools is difficult because there may be an interaction effect, but minimal or no main effect. Reconstructability analysis (RA) uses Shannon's information theory to detect relationships between variables in categorical datasets. We applied RA to simulated data for five different models of gene-gene interaction, and find that even with heritability levels as low as 0.008, and with the inclusion of 50 non-associated genes in the dataset, we can identify the interacting gene pairs with an accuracy of > or =80%. We applied RA to a real dataset of type 2 non-insulin-dependent diabetes (NIDDM) cases and controls, and closely approximated the results of more conventional single SNP disease association studies. In addition, we replicated prior evidence for epistatic interactions between SNPs on chromosomes 2 and 15.

  10. Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

    Science.gov (United States)

    Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

    2015-06-01

    To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer

    Science.gov (United States)

    Wu, Jiaqi; Hu, Shuofeng; Chen, Yaowen; Li, Zongcheng; Zhang, Jian; Yuan, Hanyu; Shi, Qiang; Shao, Ningsheng; Ying, Xiaomin

    2017-01-01

    Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very young women. To facilitate the identification of potential regulatory or driver genes, we present the Breast Cancer Integrative Platform (BCIP, http://omics.bmi.ac.cn/bcancer/). BCIP maintains multi-omics data selected with strict quality control and processed with uniform normalization methods, including gene expression profiles from 9,005 tumor and 376 normal tissue samples, copy number variation information from 3,035 tumor samples, microRNA-target interactions, co-expressed genes, KEGG pathways, and mammary tissue-specific gene functional networks. This platform provides a user-friendly interface integrating comprehensive and flexible analysis tools on differential gene expression, copy number variation, and survival analysis. The prominent characteristic of BCIP is that users can perform analysis by customizing subgroups with single or combined clinical features, including subtypes, histological grades, pathologic stages, metastasis status, lymph node status, ER/PR/HER2 status, TP53 mutation status, menopause status, age, tumor size, therapy responses, and prognosis. BCIP will help to identify regulatory or driver genes and candidate biomarkers for further research in breast cancer. PMID:28327601

  12. Prevalent Exon-Intron Structural Changes in the APETALA1/FRUITFULL, SEPALLATA, AGAMOUS-LIKE6, and FLOWERING LOCUS C MADS-Box Gene Subfamilies Provide New Insights into Their Evolution.

    Science.gov (United States)

    Yu, Xianxian; Duan, Xiaoshan; Zhang, Rui; Fu, Xuehao; Ye, Lingling; Kong, Hongzhi; Xu, Guixia; Shan, Hongyan

    2016-01-01

    AP1/FUL, SEP, AGL6, and FLC subfamily genes play important roles in flower development. The phylogenetic relationships among them, however, have been controversial, which impedes our understanding of the origin and functional divergence of these genes. One possible reason for the controversy may be the problems caused by changes in the exon-intron structure of genes, which, according to recent studies, may generate non-homologous sites and hamper the homology-based sequence alignment. In this study, we first performed exon-by-exon alignments of these and three outgroup subfamilies (SOC1, AG, and STK). Phylogenetic trees reconstructed based on these matrices show improved resolution and better congruence with species phylogeny. In the context of these phylogenies, we traced evolutionary changes of exon-intron structures in each subfamily. We found that structural changes have occurred frequently following gene duplication and speciation events. Notably, exons 7 and 8 (if present) suffered more structural changes than others. With the knowledge of exon-intron structural changes, we generated more reasonable alignments containing all the focal subfamilies. The resulting trees showed that the SEP subfamily is sister to the monophyletic group formed by AP1/FUL and FLC subfamily genes and that the AGL6 subfamily forms a sister group to the three abovementioned subfamilies. Based on this topology, we inferred the evolutionary history of exon-intron structural changes among different subfamilies. Particularly, we found that the eighth exon originated before the divergence of AP1/FUL, FLC, SEP, and AGL6 subfamilies and degenerated in the ancestral FLC-like gene. These results provide new insights into the origin and evolution of the AP1/FUL, FLC, SEP, and AGL6 subfamilies.

  13. Identifying concerted evolution and gene conversion in mammalian gene pairs lasting over 100 million years

    Directory of Open Access Journals (Sweden)

    Scherer Stephen W

    2009-07-01

    Full Text Available Abstract Background Concerted evolution occurs in multigene families and is characterized by stretches of homogeneity and higher sequence similarity between paralogues than between orthologues. Here we identify human gene pairs that have undergone concerted evolution, caused by ongoing gene conversion, since at least the human-mouse divergence. Our strategy involved the identification of duplicated genes with greater similarity within a species than between species. These genes were required to be present in multiple mammalian genomes, suggesting duplication early in mammalian divergence. To eliminate genes that have been conserved due to strong purifying selection, our analysis also required at least one intron to have retained high sequence similarity between paralogues. Results We identified three human gene pairs undergoing concerted evolution (BMP8A/B, DDX19A/B, and TUBG1/2. Phylogenetic investigations reveal that in each case the duplication appears to have occurred prior to eutherian mammalian radiation, with exactly two paralogues present in all examined species. This indicates that all three gene duplication events were established over 100 million years ago. Conclusion The extended duration of concerted evolution in multiple distant lineages suggests that there has been prolonged homogenization of specific segments within these gene pairs. Although we speculate that selection for homogenization could have been utilized in order to maintain crucial homo- or hetero- binding domains, it remains unclear why gene conversion has persisted for such extended periods of time. Through these analyses, our results demonstrate additional examples of a process that plays a definite, although unspecified, role in molecular evolution.

  14. Phylogenetic analysis of AGAMOUS sequences reveals the origin of the diploid and tetraploid forms of self-pollinating wild buckwheat, Fagopyrum homotropicum Ohnishi.

    Science.gov (United States)

    Tomiyoshi, Mitsuyuki; Yasui, Yasuo; Ohsako, Takanori; Li, Cheng-Yun; Ohnishi, Ohmi

    2012-09-01

    Fagopyrum homotropicum Ohnishi is a self-pollinating wild buckwheat species indigenous to eastern Tibet and the Yunnan and Sichuan Provinces of China. It is useful breeding material for shifting cultivated buckwheat (F. esculentum ssp. esculentum Moench) from out-crossing to self-pollinating. Despite its importance as a genetic resource in buckwheat breeding, the genetic variation of F. homotropicum is poorly understood. In this study, we investigated the genetic variation and phylogenetic relationships of the diploid and tetraploid forms of F. homotropicum based on the nucleotide sequences of a nuclear gene, AGAMOUS (AG). Neighbor-joining analysis revealed that representative individuals clustered into three large groups (Group I, II and III). Each group contained diploid and tetraploid forms of F. homotropicum. We identified tetraploid plants that had two diverged AG sequences; one belonging to Group I and the other belonging to Group II, or one belonging to Group II and the other belonging to Group III. These results suggest that the tetraploid form originated from at least two hybridization events between deeply differentiated diploids. The results also imply that the genetic diversity contributed by tetraploidization of differentiated diploids may have allowed the distribution range of F. homotropicum to expand to the northern areas of China.

  15. Identifying biological themes within lists of genes with EASE.

    Science.gov (United States)

    Hosack, Douglas A; Dennis, Glynn; Sherman, Brad T; Lane, H Clifford; Lempicki, Richard A

    2003-01-01

    EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.

  16. Gene-trap mutagenesis identifies mammalian genes contributing to intoxication by Clostridium perfringens ε-toxin.

    Directory of Open Access Journals (Sweden)

    Susan E Ivie

    Full Text Available The Clostridium perfringens ε-toxin is an extremely potent toxin associated with lethal toxemias in domesticated ruminants and may be toxic to humans. Intoxication results in fluid accumulation in various tissues, most notably in the brain and kidneys. Previous studies suggest that the toxin is a pore-forming toxin, leading to dysregulated ion homeostasis and ultimately cell death. However, mammalian host factors that likely contribute to ε-toxin-induced cytotoxicity are poorly understood. A library of insertional mutant Madin Darby canine kidney (MDCK cells, which are highly susceptible to the lethal affects of ε-toxin, was used to select clones of cells resistant to ε-toxin-induced cytotoxicity. The genes mutated in 9 surviving resistant cell clones were identified. We focused additional experiments on one of the identified genes as a means of validating the experimental approach. Gene expression microarray analysis revealed that one of the identified genes, hepatitis A virus cellular receptor 1 (HAVCR1, KIM-1, TIM1, is more abundantly expressed in human kidney cell lines than it is expressed in human cells known to be resistant to ε-toxin. One human kidney cell line, ACHN, was found to be sensitive to the toxin and expresses a larger isoform of the HAVCR1 protein than the HAVCR1 protein expressed by other, toxin-resistant human kidney cell lines. RNA interference studies in MDCK and in ACHN cells confirmed that HAVCR1 contributes to ε-toxin-induced cytotoxicity. Additionally, ε-toxin was shown to bind to HAVCR1 in vitro. The results of this study indicate that HAVCR1 and the other genes identified through the use of gene-trap mutagenesis and RNA interference strategies represent important targets for investigation of the process by which ε-toxin induces cell death and new targets for potential therapeutic intervention.

  17. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Energy Technology Data Exchange (ETDEWEB)

    Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

    2011-10-15

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  18. A gene-trap strategy identifies quiescence-induced genes in synchronized myoblasts

    Indian Academy of Sciences (India)

    Ramkumar Sambasivan; Grace K Pavlath; Jyotsna Dhawan

    2008-03-01

    Cellular quiescence is characterized not only by reduced mitotic and metabolic activity but also by altered gene expression. Growing evidence suggests that quiescence is not merely a basal state but is regulated by active mechanisms. To understand the molecular programme that governs reversible cell cycle exit, we focused on quiescence-related gene expression in a culture model of myogenic cell arrest and activation. Here we report the identification of quiescence-induced genes using a gene-trap strategy. Using a retroviral vector, we generated a library of gene traps in C2C12 myoblasts that were screened for arrest-induced insertions by live cell sorting (FACS-gal). Several independent genetrap lines revealed arrest-dependent induction of gal activity, confirming the efficacy of the FACS screen. The locus of integration was identified in 15 lines. In three lines, insertion occurred in genes previously implicated in the control of quiescence, i.e. EMSY – a BRCA2-interacting protein, p8/com1– a p300HAT-binding protein and MLL5 – a SET domain protein. Our results demonstrate that expression of chromatin modulatory genes is induced in G0, providing support to the notion that this reversibly arrested state is actively regulated.

  19. Gene network analysis in a pediatric cohort identifies novel lung function genes.

    Directory of Open Access Journals (Sweden)

    Bruce A Ong

    Full Text Available Lung function is a heritable trait and serves as an important clinical predictor of morbidity and mortality for pulmonary conditions in adults, however, despite its importance, no studies have focused on uncovering pediatric-specific loci influencing lung function. To identify novel genetic determinants of pediatric lung function, we conducted a genome-wide association study (GWAS of four pulmonary function traits, including FVC, FEV1, FEV1/FVC and FEF25-75% in 1556 children. Further, we carried out gene network analyses for each trait including all SNPs with a P-value of <1.0 × 10(-3 from the individual GWAS. The GWAS identified SNPs with notable trends towards association with the pulmonary function measures, including the previously described INTS12 locus association with FEV1 (pmeta=1.41 × 10(-7. The gene network analyses identified 34 networks of genes associated with pulmonary function variables in Caucasians. Of those, the glycoprotein gene network reached genome-wide significance for all four variables. P-value range pmeta=6.29 × 10(-4 - 2.80 × 10(-8 on meta-analysis. In this study, we report on specific pathways that are significantly associated with pediatric lung function at genome-wide significance. In addition, we report the first loci associated with lung function in both pediatric Caucasian and African American populations.

  20. Identifying novel genes in C. elegans using SAGE tags

    Directory of Open Access Journals (Sweden)

    Chen Nansheng

    2010-12-01

    Full Text Available Abstract Background Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their isoforms are hard to obtain due to their low level or rare expression. In order to obtain full-length sequences of all protein-coding genes, alternative approaches are required. Results In this project, we have developed a method of reconstructing full-length cDNA sequences based on short expressed sequence tags which is called sequence tag-based amplification of cDNA ends (STACE. Expressed tags are used as anchors for retrieving full-length transcripts in two rounds of PCR amplification. We have demonstrated the application of STACE in reconstructing full-length cDNA sequences using expressed tags mined in an array of serial analysis of gene expression (SAGE of C. elegans cDNA libraries. We have successfully applied STACE to recover sequence information for 12 genes, for two of which we found isoforms. STACE was used to successfully recover full-length cDNA sequences for seven of these genes. Conclusions The STACE method can be used to effectively reconstruct full-length cDNA sequences of genes that are under-represented in cDNA sequencing projects and have been missed by existing gene prediction methods, but their existence has been suggested by short sequence tags such as SAGE tags.

  1. Functional gene group analysis identifies synaptic gene groups as risk factor for schizophrenia.

    Science.gov (United States)

    Lips, E S; Cornelisse, L N; Toonen, R F; Min, J L; Hultman, C M; Holmans, P A; O'Donovan, M C; Purcell, S M; Smit, A B; Verhage, M; Sullivan, P F; Visscher, P M; Posthuma, D

    2012-10-01

    Schizophrenia is a highly heritable disorder with a polygenic pattern of inheritance and a population prevalence of ~1%. Previous studies have implicated synaptic dysfunction in schizophrenia. We tested the accumulated association of genetic variants in expert-curated synaptic gene groups with schizophrenia in 4673 cases and 4965 healthy controls, using functional gene group analysis. Identifying groups of genes with similar cellular function rather than genes in isolation may have clinical implications for finding additional drug targets. We found that a group of 1026 synaptic genes was significantly associated with the risk of schizophrenia (P=7.6 × 10(-11)) and more strongly associated than 100 randomly drawn, matched control groups of genetic variants (P<0.01). Subsequent analysis of synaptic subgroups suggested that the strongest association signals are derived from three synaptic gene groups: intracellular signal transduction (P=2.0 × 10(-4)), excitability (P=9.0 × 10(-4)) and cell adhesion and trans-synaptic signaling (P=2.4 × 10(-3)). These results are consistent with a role of synaptic dysfunction in schizophrenia and imply that impaired intracellular signal transduction in synapses, synaptic excitability and cell adhesion and trans-synaptic signaling play a role in the pathology of schizophrenia.

  2. Analysis of the retinal gene expression profile after hypoxic preconditioning identifies candidate genes for neuroprotection

    Directory of Open Access Journals (Sweden)

    Wenzel Andreas

    2008-02-01

    Full Text Available Abstract Background Retinal degeneration is a main cause of blindness in humans. Neuroprotective therapies may be used to rescue retinal cells and preserve vision. Hypoxic preconditioning stabilizes the transcription factor HIF-1α in the retina and strongly protects photoreceptors in an animal model of light-induced retinal degeneration. To address the molecular mechanisms of the protection, we analyzed the transcriptome of the hypoxic retina using microarrays and real-time PCR. Results Hypoxic exposure induced a marked alteration in the retinal transcriptome with significantly different expression levels of 431 genes immediately after hypoxic exposure. The normal expression profile was restored within 16 hours of reoxygenation. Among the differentially regulated genes, several candidates for neuroprotection were identified like metallothionein-1 and -2, the HIF-1 target gene adrenomedullin and the gene encoding the antioxidative and cytoprotective enzyme paraoxonase 1 which was previously not known to be a hypoxia responsive gene in the retina. The strongly upregulated cyclin dependent kinase inhibitor p21 was excluded from being essential for neuroprotection. Conclusion Our data suggest that neuroprotection after hypoxic preconditioning is the result of the differential expression of a multitude of genes which may act in concert to protect visual cells against a toxic insult.

  3. Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs

    Directory of Open Access Journals (Sweden)

    Ye Zhi-Qiang

    2011-08-01

    Full Text Available Abstract Background Differential coexpression analysis (DCEA is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance. Results We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links. Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis. Conclusions This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.

  4. ANALISIS POTENSI EKONOMI DAERAH DALAM PENGEMBANGAN KOMODITI UNGGULAN KABUPATEN AGAM

    Directory of Open Access Journals (Sweden)

    Yolamalinda

    2014-10-01

    Full Text Available Globalization requires areas within the national territory to compete in the free trade competitively with products from countries all over the world. Regional economic development is expected to produce superior quality products that can compete in competition, both domestically and abroad. Agam as areas that have the potential of tourism and culture has the potential to perform on the world market with superior commodity sub-sectors of the manufacturing industry. This article analyzes the election of regional commodity Agam using LQ analysis, specialization index, Shift share and SWOT analysis. The analysis finds that subsekctor processing industry has a competitive advantage and thus likely to be developed to increase the region's economy. Commodity embroidery as a creative industry is a commodity that is mapped able to compete on the sub-sectors of the processing industry because the rich local cultural values and Islamic values. A variety of programs and government policies are needed to support these commodities to appear on the international market.

  5. Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes.

    Science.gov (United States)

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR-essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR-essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR-essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR-essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR-induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led

  6. Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes.

    Directory of Open Access Journals (Sweden)

    Daniel Wuttke

    Full Text Available Dietary restriction (DR, limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR-essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/. To dissect the interactions of DR-essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR-essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR-essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2 had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR-induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of

  7. Dissecting the Gene Network of Dietary Restriction to Identify Evolutionarily Conserved Pathways and New Functional Genes

    Science.gov (United States)

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR–essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR–essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR–essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR–essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR–induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple

  8. Identifying the Interaction between Genes and Gene Products Based on Frequently Seen Verbs in Medline Abstracts.

    Science.gov (United States)

    Sekimizu; Park; Tsujii

    1998-01-01

    We have selected the most frequently seen verbs from raw texts made up of 1-million-words of Medline abstracts, and we were able to identify (or bracket) noun phrases contained in the corpus, with a precision rate of 90%. Then, based on the noun-phrase-bracketted corpus, we tried to find the subject and object terms for some frequently seen verbs in the domain. The precision rate of finding the right subject and object for each verb was about 73%. This task was only made possible because we were able to linguistically analyze (or parse) a large quantity of a raw corpus. Our approach will be useful for classifying genes and gene products and for identifying the interaction between them. It is the first step of our effort in building a genome-related thesaurus and hierarchies in a fully automatic way.

  9. Epidermal growth factor gene is a newly identified candidate gene for gout

    Science.gov (United States)

    Han, Lin; Cao, Chunwei; Jia, Zhaotong; Liu, Shiguo; Liu, Zhen; Xin, Ruosai; Wang, Can; Li, Xinde; Ren, Wei; Wang, Xuefeng; Li, Changgui

    2016-01-01

    Chromosome 4q25 has been identified as a genomic region associated with gout. However, the associations of gout with the genes in this region have not yet been confirmed. Here, we performed two-stage analysis to determine whether variations in candidate genes in the 4q25 region are associated with gout in a male Chinese Han population. We first evaluated 96 tag single nucleotide polymorphisms (SNPs) in eight inflammatory/immune pathway- or glucose/lipid metabolism-related genes in the 4q25 region in 480 male gout patients and 480 controls. The SNP rs12504538, located in the elongation of very-long-chain-fatty-acid-like family member 6 gene (Elovl6), was found to be associated with gout susceptibility (Padjusted = 0.00595). In the second stage of analysis, we performed fine mapping analysis of 93 tag SNPs in Elovl6 and in the epidermal growth factor gene (EGF) and its flanking regions in 1017 male patients gout and 1897 healthy male controls. We observed a significant association between the T allele of EGF rs2298999 and gout (odds ratio = 0.77, 95% confidence interval = 0.67–0.88, Padjusted = 6.42 × 10−3). These results provide the first evidence for an association between the EGF rs2298999 C/T polymorphism and gout. Our findings should be validated in additional populations. PMID:27506295

  10. Identifying promoters for gene expression in Clostridium thermocellum

    Directory of Open Access Journals (Sweden)

    Daniel G. Olson

    2015-12-01

    Full Text Available A key tool for metabolic engineering is the ability to express heterologous genes. One obstacle to gene expression in non-model organisms, and especially in relatively uncharacterized bacteria, is the lack of well-characterized promoters. Here we test 17 promoter regions for their ability to drive expression of the reporter genes β-galactosidase (lacZ and NADPH-alcohol dehydrogenase (adhB in Clostridium thermocellum, an important bacterium for the production of cellulosic biofuels. Only three promoters have been commonly used for gene expression in C. thermocellum, gapDH, cbp and eno. Of the new promoters tested, 2638, 2926, 966 and 815 showed reliable expression. The 2638 promoter showed relatively higher activity when driving adhB (compared to lacZ, and the 815 promoter showed relatively higher activity when driving lacZ (compared to adhB.

  11. SUPERMAN, a regulator of floral homeotic genes in Arabidopsis.

    Science.gov (United States)

    Bowman, J L; Sakai, H; Jack, T; Weigel, D; Mayer, U; Meyerowitz, E M

    1992-03-01

    We describe a locus, SUPERMAN, mutations in which result in extra stamens developing at the expense of the central carpels in the Arabidopsis thaliana flower. The development of superman flowers, from initial primordium to mature flower, is described by scanning electron microscopy. The development of doubly and triply mutant strains, constructed with superman alleles and previously identified homeotic mutations that cause alterations in floral organ identity, is also described. Essentially additive phenotypes are observed in superman agamous and superman apetala2 double mutants. The epistatic relationships observed between either apetala3 or pistillata and superman alleles suggest that the SUPERMAN gene product could be a regulator of these floral homeotic genes. To test this, the expression patterns of AGAMOUS and APETALA3 were examined in superman flowers. In wild-type flowers, APETALA3 expression is restricted to the second and third whorls where it is required for the specification of petals and stamens. In contrast, in superman flowers, APETALA3 expression expands to include most of the cells that would normally constitute the fourth whorl. This ectopic APETALA3 expression is proposed to be one of the causes of the development of the extra stamens in superman flowers. The spatial pattern of AGAMOUS expression remains unaltered in superman flowers as compared to wild-type flowers. Taken together these data indicate that one of the functions of the wild-type SUPERMAN gene product is to negatively regulate APETALA3 in the fourth whorl of the flower. In addition, superman mutants exhibit a loss of determinacy of the floral meristem, an effect that appears to be mediated by the APETALA3 and PISTILLATA gene products.

  12. Exploiting natural variation to identify insect-resistance genes.

    Science.gov (United States)

    Broekgaarden, Colette; Snoeren, Tjeerd A L; Dicke, Marcel; Vosman, Ben

    2011-10-01

    Herbivorous insects are widespread and often serious constraints to crop production. The use of insect-resistant crops is a very effective way to control insect pests in agriculture, and the development of such crops can be greatly enhanced by knowledge on plant resistance mechanisms and the genes involved. Plants have evolved diverse ways to cope with insect attack that has resulted in natural variation for resistance towards herbivorous insects. Studying the molecular genetics and transcriptional background of this variation has facilitated the identification of resistance genes and processes that lead to resistance against insects. With the development of new technologies, molecular studies are not restricted to model plants anymore. This review addresses the need to exploit natural variation in resistance towards insects to increase our knowledge on resistance mechanisms and the genes involved. We will discuss how this knowledge can be exploited in breeding programmes to provide sustainable crop protection against insect pests. Additionally, we discuss the current status of genetic research on insect-resistance genes. We conclude that insect-resistance mechanisms are still unclear at the molecular level and that exploiting natural variation with novel technologies will contribute greatly to the development of insect-resistant crop varieties.

  13. Using new genetic tools to identify potato resistance genes

    Science.gov (United States)

    Plant diseases present a burden to agriculture through yield losses due to plant stress, costs associated with disease control, and efforts to detect infections and limit disease epidemics. Plant breeders are interested in the identification and incorporation of simply inherited genes that confer ro...

  14. Transcriptome Sequencing Identified Genes and Gene Ontologies Associated with Early Freezing Tolerance in Maize

    Science.gov (United States)

    Li, Zhao; Hu, Guanghui; Liu, Xiangfeng; Zhou, Yao; Li, Yu; Zhang, Xu; Yuan, Xiaohui; Zhang, Qian; Yang, Deguang; Wang, Tianyu; Zhang, Zhiwu

    2016-01-01

    Originating in a tropical climate, maize has faced great challenges as cultivation has expanded to the majority of the world's temperate zones. In these zones, frost and cold temperatures are major factors that prevent maize from reaching its full yield potential. Among 30 elite maize inbred lines adapted to northern China, we identified two lines of extreme, but opposite, freezing tolerance levels—highly tolerant and highly sensitive. During the seedling stage of these two lines, we used RNA-seq to measure changes in maize whole genome transcriptome before and after freezing treatment. In total, 19,794 genes were expressed, of which 4550 exhibited differential expression due to either treatment (before or after freezing) or line type (tolerant or sensitive). Of the 4550 differently expressed genes, 948 exhibited differential expression due to treatment within line or lines under freezing condition. Analysis of gene ontology found that these 948 genes were significantly enriched for binding functions (DNA binding, ATP binding, and metal ion binding), protein kinase activity, and peptidase activity. Based on their enrichment, literature support, and significant levels of differential expression, 30 of these 948 genes were selected for quantitative real-time PCR (qRT-PCR) validation. The validation confirmed our RNA-Seq-based findings, with squared correlation coefficients of 80% and 50% in the tolerance and sensitive lines, respectively. This study provided valuable resources for further studies to enhance understanding of the molecular mechanisms underlying maize early freezing response and enable targeted breeding strategies for developing varieties with superior frost resistance to achieve yield potential. PMID:27774095

  15. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

    Directory of Open Access Journals (Sweden)

    Zwinderman Aeilko H

    2009-09-01

    Full Text Available Abstract Background We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes. Results We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes. Conclusion We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

  16. Gene : CBRC-AGAM-02-0153 [SEVENS

    Lifescience Database Archive (English)

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  17. Gene : CBRC-AGAM-04-0011 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CPQVFDPRKLVLVEHRSCTKYNLCVLGLMLTVSCPNDLRFNNERCECDFKEKVHCEGEDPATTTVDYASSTDATTVYDSTTEDSTTEESTTELATSESTTEDSTTEESTTEVTVSESTTEDST...TEESTTEGTVSESTTEDSTTEESTTEVATSESTTEESTTEESTTEVTVSESTTEDSTTKESTTEVTVSESTTEDST...TEESTTEVATSESTTEDSTTEESTTEVTVSESTTEDSTTEESTTEAATSESTTEDSTTEEATTEVTVSESTTEDSTTEESTTEVVTSESTTEDSTTEESTTEVVTSESTTEDST...TEESTTEVATSESTTEDSTTEESTTEVTVSESTTEDSTTEESTTEAATSESTTEDSTTEEATTEVTVSESTTEDSTTEESTTEVAVSESTTEDST...TEESTTEVATSESTTEESTTEESTTEVTVSEPTTEDSTTEESTTEVTTSESTTEESTTEESTTEVTVSESTTEDSTTEESTTEVTTSESTTEDST

  18. Gene : CBRC-AGAM-03-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available female salivary glands 0-2 days after emergence Anopheles gambiae cDNA, mRNA seq...WFDRAIWIVYRSLPILVNISYFYKAYRLILFPEDNTSAASVIASVWGFTEGTLRICLIELRYGTLASIMSFLNERSYRQQDSLVRQQRATLFGENNRIQLILVATMLM

  19. Gene : CBRC-AGAM-04-0082 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available cds=p(1,3606) /gb=AJ535205 /gi=27227575 /ug=Aga.35084 /len=3606 9e-51 24% MSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMS...SASTSEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTSGTTRTT...PTRPTPTDTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMS...SASTPEPSTKPGTTRTTPTRPTTTESTDTTMSSASTTEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTPGTTRTTPTRPTSTEST...DTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDTTMSSASTPEPSTTPGTTRTTPTRPTSTESTDT

  20. Gene : CBRC-AGAM-04-0078 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available 439 /ug=Aga.1901 /len=2137 2e-40 28% MMTPTSPTIPTSPTSSSSPTIRTSPSVPTSPSTPQTSSSTTESTTASSTTSTSMTTSISPTIPSSPTSLSSPTTRTSPSAPTSPSTPQTTISTTES...TTAVTMNSDSSSSSTESTTPSSTTSTSMTTPTSPTIPTSPTSPSSPTIRTSPSATTSPSTPKTSSSTTESTTASSTTSISPTI...PSSPTSLSSPTTRTSPSAPTSPSTPETTISTTESTTAVTMTSDSTSSSTESTTVSSTTPETTSSITESTTAVTMTSDSSSSSTESTTASSTTPATTSSTTEST...TVSSTTSETTSSRTESTTAITMTSDSTSSSTESTTASSTTSTSMTTSISPTIPSSPTSLSSPTTRTSPSAPTSPSTPETISSTTEST...TASSTISTSMTTSISPTIPSSPTSPSSSTTRTTPSAPTSPSTPETTSSTTESTASSTTSTSMTTPTSPTIPSSPTSPSSPTTRTSPSAPTSPTTPETISSTTESTTA

  1. Gene : CBRC-AGAM-02-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available 1 4e-18 22% MALAVRLVLACHLAWPQPSRLRSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTTVSITLASTFSTTVSITFASTSSVSSVSSFSTTSDTG...SDGSGCSSCSGLSSGLASTFSTTVSITLASTFSTTVSITFASTSSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTTVSITLASTSSVSSVSSFSTTSDTGSD...GSGCSSCSGLSSGLASTFSTTVSITLASTFSTTVSITFASTSSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTTVSITFAST...SSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTTVSITFASTSSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTMVSITLASTFSTTVSITFAST...SSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTTISITLASTFSTTVSITFASTSSVSSVSSFSTTSDTGSDGSGCSSCSGLSSGLASTFSTTVSITFAST

  2. Gene : CBRC-AGAM-07-0049 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available gi=18896463 /ug=Aga.18967 /len=462 3e-12 73% MIKLYCFQLCACISAFCAFAMCKRVEMKCSFSLSLSLSLCLSLSLSLSLSVSLSVSLSVSLSVSLSVSLSVSLSLSLSLSLSLSLS...MCLSLSLSLSLSLSLSPCLSLFLSLYLSLSLSISLYLSLFLSLCLSLTVSLSLSLSLSISLSISLYLSLSISISISISISISLSFSLSLSLSLSLSLSLSLSVSLSVSLS...LSLSLSLSLSLSLSLSLYLSLYLSLYLSLSLSLSLSLSLSLSLSLSLTLSLSLSLSLSLSLS

  3. Gene : CBRC-AGAM-02-0165 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available ef|XP_309589.1| putative glyco-protein hormone fsh-like receptor (AGAP004035-PA) [Anopheles gambiae str. PES...T] gb|EAA05376.2| putative glyco-protein hormone fsh-like receptor (AGAP004035-PA) [Anopheles gambiae str. P

  4. Identifying Stress Transcription Factors Using Gene Expression and TF-Gene Association Data.

    Science.gov (United States)

    Wu, Wei-Sheng; Chen, Bor-Sen

    2009-11-24

    Unicellular organisms such as yeasts have evolved to survive environmental stresses by rapidly reorganizing the genomic expression program to meet the challenges of harsh environments. The complex adaptation mechanisms to stress remain to be elucidated. In this study, we developed Stress Transcription Factor Identification Algorithm (STFIA), which integrates gene expression and TF-gene association data to identify the stress transcription factors (TFs) of six kinds of stresses. We identified some general stress TFs that are in response to various stresses, and some specific stress TFs that are in response to one specific stress. The biological significance of our findings is validated by the literature. We found that a small number of TFs may be sufficient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the adaptation mechanisms to different stresses may have a bow-tie structure. Second, there may exist extensive regulatory cross-talk among different stress responses. In conclusion, this study proposes a network of the regulators of stress responses and their mechanism of action.

  5. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

    NARCIS (Netherlands)

    Waaijenborg, S.; Zwinderman, A.H.

    2009-01-01

    ABSTRACT: BACKGROUND: We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the can

  6. Identifying Genes Responsible for Tamoxifen Resistance in Breast Cancer

    NARCIS (Netherlands)

    D. Meijer (Daniëlle)

    2008-01-01

    textabstractBreast cancer is one of the leading causes of death of women in western countries. It affects one out of eight females in the USA (1) and one out of nine females in The Netherlands (www.kankerregistratie.nl) during their lifetime. Many risk factors for breast cancer have been identified

  7. Blood Pressure Loci Identified with a Gene-Centric Array

    NARCIS (Netherlands)

    Johnson, Toby; Gaunt, Tom R.; Newhouse, Stephen J.; Padmanabhan, Sandosh; Tomaszewski, Maciej; Kumari, Meena; Morris, Richard W.; Tzoulaki, Ioanna; O'Brien, Eoin T.; Poulter, Neil R.; Sever, Peter; Shields, Denis C.; Thom, Simon; Wannamethee, Sasiwarang G.; Whincup, Peter H.; Brown, Morris J.; Connell, John M.; Dobson, Richard J.; Howard, Philip J.; Mein, Charles A.; Onipinla, Abiodun; Shaw-Hawkins, Sue; Zhang, Yun; Smith, George Davey; Day, Ian N. M.; Lawlor, Debbie A.; Goodall, Alison H.; Fowkes, F. Gerald; Abecasis, Goncalo R.; Elliott, Paul; Gateva, Vesela; Braund, Peter S.; Burton, Paul R.; Nelson, Christopher P.; Tobin, Martin D.; van der Harst, Pim; Glorioso, Nicola; Neuvrith, Hani; Salvi, Erika; Staessen, Jan A.; Stucchi, Andrea; Devos, Nabila; Jeunemaitre, Xavier; Plouin, Pierre-Francois; Tichet, Jean; Juhanson, Peeter; Org, Elin; Putku, Margus; Sober, Siim; Veldre, Gudrun; Viigimaa, Margus; Levinsson, Anna; Rosengren, Annika; Thelle, Dag S.; Hastie, Claire E.; Hedner, Thomas; Lee, Wai K.; Melander, Olle; Wahlstrand, Bjoern; Hardy, Rebecca; Wong, Andrew; Cooper, Jackie A.; Palmen, Jutta; Chen, Li; Stewart, Alexandre F. R.; Wells, George A.; Westra, Harm-Jan; Wolfs, Marcel G. M.; Clarke, Robert; Franzosi, Maria Grazia; Goel, Anuj; Hamsten, Anders; Lathrop, Mark; Peden, John F.; Seedorf, Udo; Watkins, Hugh; Ouwehand, Willem H.; Sambrook, Jennifer; Stephens, Jonathan; Casas, Juan-Pablo; Drenos, Fotios; Holmes, Michael V.; Kivimaki, Mika; Shah, Sonia; Shah, Tina; Talmud, Philippa J.; Whittaker, John; Wallace, Chris; Delles, Christian; Laan, Mans; Kuh, Diana; Humphries, Steve E.; Nyberg, Fredrik; Cusi, Daniele; Roberts, Robert; Newton-Cheh, Christopher; Franke, Lude; Stanton, Alice V.; Dominiczak, Anna F.; Farrall, Martin; Hingorani, Aroon D.; Samani, Nilesh J.; Caulfield, Mark J.; Munroe, Patricia B.

    2011-01-01

    Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a

  8. Blood Pressure Loci Identified with a Gene-Centric Array

    NARCIS (Netherlands)

    Johnson, Toby; Gaunt, Tom R.; Newhouse, Stephen J.; Padmanabhan, Sandosh; Tomaszewski, Maciej; Kumari, Meena; Morris, Richard W.; Tzoulaki, Ioanna; O'Brien, Eoin T.; Poulter, Neil R.; Sever, Peter; Shields, Denis C.; Thom, Simon; Wannamethee, Sasiwarang G.; Whincup, Peter H.; Brown, Morris J.; Connell, John M.; Dobson, Richard J.; Howard, Philip J.; Mein, Charles A.; Onipinla, Abiodun; Shaw-Hawkins, Sue; Zhang, Yun; Smith, George Davey; Day, Ian N. M.; Lawlor, Debbie A.; Goodall, Alison H.; Fowkes, F. Gerald; Abecasis, Goncalo R.; Elliott, Paul; Gateva, Vesela; Braund, Peter S.; Burton, Paul R.; Nelson, Christopher P.; Tobin, Martin D.; van der Harst, Pim; Glorioso, Nicola; Neuvrith, Hani; Salvi, Erika; Staessen, Jan A.; Stucchi, Andrea; Devos, Nabila; Jeunemaitre, Xavier; Plouin, Pierre-Francois; Tichet, Jean; Juhanson, Peeter; Org, Elin; Putku, Margus; Sober, Siim; Veldre, Gudrun; Viigimaa, Margus; Levinsson, Anna; Rosengren, Annika; Thelle, Dag S.; Hastie, Claire E.; Hedner, Thomas; Lee, Wai K.; Melander, Olle; Wahlstrand, Bjoern; Hardy, Rebecca; Wong, Andrew; Cooper, Jackie A.; Palmen, Jutta; Chen, Li; Stewart, Alexandre F. R.; Wells, George A.; Westra, Harm-Jan; Wolfs, Marcel G. M.; Clarke, Robert; Franzosi, Maria Grazia; Goel, Anuj; Hamsten, Anders; Lathrop, Mark; Peden, John F.; Seedorf, Udo; Watkins, Hugh; Ouwehand, Willem H.; Sambrook, Jennifer; Stephens, Jonathan; Casas, Juan-Pablo; Drenos, Fotios; Holmes, Michael V.; Kivimaki, Mika; Shah, Sonia; Shah, Tina; Talmud, Philippa J.; Whittaker, John; Wallace, Chris; Delles, Christian; Laan, Mans; Kuh, Diana; Humphries, Steve E.; Nyberg, Fredrik; Cusi, Daniele; Roberts, Robert; Newton-Cheh, Christopher; Franke, Lude; Stanton, Alice V.; Dominiczak, Anna F.; Farrall, Martin; Hingorani, Aroon D.; Samani, Nilesh J.; Caulfield, Mark J.; Munroe, Patricia B.

    2011-01-01

    Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a besp

  9. Network analysis identifies common genes associated with obesity in six obesity-related diseases*

    OpenAIRE

    Su, Li-ning; Wang, Yan-bing; Wnag, Chun-guang; Wei, Hui-ping

    2017-01-01

    Obesity has been reported to be associated with many diseases. However, common obesity-induced biological processes have not been evaluated across these diseases. We identified genes associated with obesity and obesity-related diseases, and used them to construct protein‒protein interaction networks. We also analyzed gene ontology (GO) in those genes overlapping between obesity and disease. Our work identifies gene modules common to obesity and obesity-related diseases, which can provide a ba...

  10. A transcription map of the 6p22.3 reading disability locus identifying candidate genes

    Directory of Open Access Journals (Sweden)

    Gruen Jeffrey R

    2003-06-01

    Full Text Available Abstract Background Reading disability (RD is a common syndrome with a large genetic component. Chromosome 6 has been identified in several linkage studies as playing a significant role. A more recent study identified a peak of transmission disequilibrium to marker JA04 (G72384 on chromosome 6p22.3, suggesting that a gene is located near this marker. Results In silico cloning was used to identify possible candidate genes located near the JA04 marker. The 2 million base pairs of sequence surrounding JA04 was downloaded and searched against the dbEST database to identify ESTs. In total, 623 ESTs from 80 different tissues were identified and assembled into 153 putative coding regions from 19 genes and 2 pseudogenes encoded near JA04. The identified genes were tested for their tissue specific expression by RT-PCR. Conclusions In total, five possible candidate genes for RD and other diseases mapping to this region were identified.

  11. Network properties of complex human disease genes identified through genome-wide association studies.

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    Fredrik Barrenas

    Full Text Available BACKGROUND: Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs, thereby eliminating discovery bias. PRINCIPAL FINDINGS: We derived a network of complex diseases (n = 54 and complex disease genes (n = 349 to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process. CONCLUSIONS: This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

  12. Network properties of complex human disease genes identified through genome-wide association studies.

    Science.gov (United States)

    Barrenas, Fredrik; Chavali, Sreenivas; Holme, Petter; Mobini, Reza; Benson, Mikael

    2009-11-30

    Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias. We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process. This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

  13. Sleeping Beauty transposon mutagenesis identifies genes that cooperate with mutant Smad4 in gastric cancer development.

    Science.gov (United States)

    Takeda, Haruna; Rust, Alistair G; Ward, Jerrold M; Yew, Christopher Chin Kuan; Jenkins, Nancy A; Copeland, Neal G

    2016-04-05

    Mutations in SMAD4 predispose to the development of gastrointestinal cancer, which is the third leading cause of cancer-related deaths. To identify genes driving gastric cancer (GC) development, we performed a Sleeping Beauty (SB) transposon mutagenesis screen in the stomach of Smad4(+/-) mutant mice. This screen identified 59 candidate GC trunk drivers and a much larger number of candidate GC progression genes. Strikingly, 22 SB-identified trunk drivers are known or candidate cancer genes, whereas four SB-identified trunk drivers, including PTEN, SMAD4, RNF43, and NF1, are known human GC trunk drivers. Similar to human GC, pathway analyses identified WNT, TGF-β, and PI3K-PTEN signaling, ubiquitin-mediated proteolysis, adherens junctions, and RNA degradation in addition to genes involved in chromatin modification and organization as highly deregulated pathways in GC. Comparative oncogenomic filtering of the complete list of SB-identified genes showed that they are highly enriched for genes mutated in human GC and identified many candidate human GC genes. Finally, by comparing our complete list of SB-identified genes against the list of mutated genes identified in five large-scale human GC sequencing studies, we identified LDL receptor-related protein 1B (LRP1B) as a previously unidentified human candidate GC tumor suppressor gene. In LRP1B, 129 mutations were found in 462 human GC samples sequenced, and LRP1B is one of the top 10 most deleted genes identified in a panel of 3,312 human cancers. SB mutagenesis has, thus, helped to catalog the cooperative molecular mechanisms driving SMAD4-induced GC growth and discover genes with potential clinical importance in human GC.

  14. Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks.

    Science.gov (United States)

    Trinh, Hung-Cuong; Kwon, Yung-Keun

    2015-11-01

    Efficiently identifying functionally important genes in order to understand the minimal requirements of normal cellular development is challenging. To this end, a variety of structural measures have been proposed and their effectiveness has been investigated in recent literature; however, few studies have shown the effectiveness of dynamics-based measures. This led us to investigate a dynamic measure to identify functionally important genes, and the effectiveness of which was verified through application on two large-scale human signaling networks. We specifically consider Boolean sensitivity-based dynamics against an update-rule perturbation (BSU) as a dynamic measure. Through investigations on two large-scale human signaling networks, we found that genes with relatively high BSU values show slower evolutionary rate and higher proportions of essential genes and drug targets than other genes. Gene-ontology analysis showed clear differences between the former and latter groups of genes. Furthermore, we compare the identification accuracies of essential genes and drug targets via BSU and five well-known structural measures. Although BSU did not always show the best performance, it effectively identified the putative set of genes, which is significantly different from the results obtained via the structural measures. Most interestingly, BSU showed the highest synergy effect in identifying the functionally important genes in conjunction with other measures. Our results imply that Boolean-sensitive dynamics can be used as a measure to effectively identify functionally important genes in signaling networks.

  15. Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

    Institute of Scientific and Technical Information of China (English)

    Kai; Shi; Zhi-Tong; Bing; Gui-Qun; Cao; Ling; Guo; Ya-Na; Cao; Hai-Ou; Jiang; Mei-Xia; Zhang

    2015-01-01

    AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

  16. Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

    Directory of Open Access Journals (Sweden)

    Kai Shi

    2015-04-01

    Full Text Available AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis (WGCNA is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study. METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus (GEO database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes. The function of the genes were annotated by gene ontology (GO. RESULTS: In this study, we identified four co-expression modules significantly correlated with clinic traits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location (sclera and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter (LTD. Additionally, we identified the hug gene (top connectivity with other genes in each module. The hub gene RPS15A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma. CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

  17. LGscore: A method to identify disease-related genes using biological literature and Google data.

    Science.gov (United States)

    Kim, Jeongwoo; Kim, Hyunjin; Yoon, Youngmi; Park, Sanghyun

    2015-04-01

    Since the genome project in 1990s, a number of studies associated with genes have been conducted and researchers have confirmed that genes are involved in disease. For this reason, the identification of the relationships between diseases and genes is important in biology. We propose a method called LGscore, which identifies disease-related genes using Google data and literature data. To implement this method, first, we construct a disease-related gene network using text-mining results. We then extract gene-gene interactions based on co-occurrences in abstract data obtained from PubMed, and calculate the weights of edges in the gene network by means of Z-scoring. The weights contain two values: the frequency and the Google search results. The frequency value is extracted from literature data, and the Google search result is obtained using Google. We assign a score to each gene through a network analysis. We assume that genes with a large number of links and numerous Google search results and frequency values are more likely to be involved in disease. For validation, we investigated the top 20 inferred genes for five different diseases using answer sets. The answer sets comprised six databases that contain information on disease-gene relationships. We identified a significant number of disease-related genes as well as candidate genes for Alzheimer's disease, diabetes, colon cancer, lung cancer, and prostate cancer. Our method was up to 40% more accurate than existing methods. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Digital gene expression profiling of flax (Linum usitatissimum L.) stem peel identifies genes enriched in fiber-bearing phloem tissue.

    Science.gov (United States)

    Guo, Yuan; Qiu, Caisheng; Long, Songhua; Chen, Ping; Hao, Dongmei; Preisner, Marta; Wang, Hui; Wang, Yufu

    2017-08-30

    To better understand the molecular mechanisms and gene expression characteristics associated with development of bast fiber cell within flax stem phloem, the gene expression profiling of flax stem peels and leaves were screened, using Illumina's Digital Gene Expression (DGE) analysis. Four DGE libraries (2 for stem peel and 2 for leaf), ranging from 6.7 to 9.2 million clean reads were obtained, which produced 7.0 million and 6.8 million mapped reads for flax stem peel and leave, respectively. By differential gene expression analysis, a total of 975 genes, of which 708 (73%) genes have protein-coding annotation, were identified as phloem enriched genes putatively involved in the processes of polysaccharide and cell wall metabolism. Differential expression genes (DEGs) was validated using quantitative RT-PCR, the expression pattern of all nine genes determined by qRT-PCR fitted in well with that obtained by sequencing analysis. Cluster and Gene Ontology (GO) analysis revealed that a large number of genes related to metabolic process, catalytic activity and binding category were expressed predominantly in the stem peels. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the phloem enriched genes suggested approximately 111 biological pathways. The large number of genes and pathways produced from DGE sequencing will expand our understanding of the complex molecular and cellular events in flax bast fiber development and provide a foundation for future studies on fiber development in other bast fiber crops. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. The MADS domain protein DIANA acts together with AGAMOUS-LIKE80 to specify the central cell in Arabidopsis ovules.

    Science.gov (United States)

    Bemer, Marian; Wolters-Arts, Mieke; Grossniklaus, Ueli; Angenent, Gerco C

    2008-08-01

    MADS box genes in plants consist of MIKC-type and type I genes. While MIKC-type genes have been studied extensively, the functions of type I genes are still poorly understood. Evidence suggests that type I MADS box genes are involved in embryo sac and seed development. We investigated two independent T-DNA insertion alleles of the Arabidopsis thaliana type I MADS box gene AGAMOUS-LIKE61 (AGL61) and showed that in agl61 mutant ovules, the polar nuclei do not fuse and central cell morphology is aberrant. Furthermore, the central cell begins to degenerate before fertilization takes place. Although pollen tubes are attracted and perceived by the mutant ovules, neither endosperm development nor zygote formation occurs. AGL61 is expressed in the central cell during the final stages of embryo sac development. An AGL61:green fluorescent protein-beta-glucoronidase fusion protein localizes exclusively to the polar nuclei and the secondary nucleus of the central cell. Yeast two-hybrid analysis showed that AGL61 can form a heterodimer with AGL80 and that the nuclear localization of AGL61 is lost in the agl80 mutant. Thus, AGL61 and AGL80 appear to function together to differentiate the central cell in Arabidopsis. We renamed AGL61 DIANA, after the virginal Roman goddess of the hunt.

  20. Identifying the optimal gene and gene set in hepatocellular carcinoma based on differential expression and differential co-expression algorithm.

    Science.gov (United States)

    Dong, Li-Yang; Zhou, Wei-Zhong; Ni, Jun-Wei; Xiang, Wei; Hu, Wen-Hao; Yu, Chang; Li, Hai-Yan

    2017-02-01

    The objective of this study was to identify the optimal gene and gene set for hepatocellular carcinoma (HCC) utilizing differential expression and differential co-expression (DEDC) algorithm. The DEDC algorithm consisted of four parts: calculating differential expression (DE) by absolute t-value in t-statistics; computing differential co-expression (DC) based on Z-test; determining optimal thresholds on the basis of Chi-squared (χ2) maximization and the corresponding gene was the optimal gene; and evaluating functional relevance of genes categorized into different partitions to determine the optimal gene set with highest mean minimum functional information (FI) gain (Δ*G). The optimal thresholds divided genes into four partitions, high DE and high DC (HDE-HDC), high DE and low DC (HDE-LDC), low DE and high DC (LDE‑HDC), and low DE and low DC (LDE-LDC). In addition, the optimal gene was validated by conducting reverse transcription-polymerase chain reaction (RT-PCR) assay. The optimal threshold for DC and DE were 1.032 and 1.911, respectively. Using the optimal gene, the genes were divided into four partitions including: HDE-HDC (2,053 genes), HED-LDC (2,822 genes), LDE-HDC (2,622 genes), and LDE-LDC (6,169 genes). The optimal gene was microtubule‑associated protein RP/EB family member 1 (MAPRE1), and RT-PCR assay validated the significant difference between the HCC and normal state. The optimal gene set was nucleoside metabolic process (GO\\GO:0009116) with Δ*G = 18.681 and 24 HDE-HDC partitions in total. In conclusion, we successfully investigated the optimal gene, MAPRE1, and gene set, nucleoside metabolic process, which may be potential biomarkers for targeted therapy and provide significant insight for revealing the pathological mechanism underlying HCC.

  1. Identification and validation of superior reference gene for gene expression normalization via RT-qPCR in staminate and pistillate flowers of Jatropha curcas – A biodiesel plant

    Science.gov (United States)

    Karuppaiya, Palaniyandi; Yan, Xiao-Xue; Liao, Wang; Chen, Fang; Tang, Lin

    2017-01-01

    Physic nut (Jatropha curcas L) seed oil is a natural resource for the alternative production of fossil fuel. Seed oil production is mainly depended on seed yield, which was restricted by the low ratio of staminate flowers to pistillate flowers. Further, the mechanism of physic nut flower sex differentiation has not been fully understood yet. Quantitative Real Time—Polymerase Chain Reaction is a reliable and widely used technique to quantify the gene expression pattern in biological samples. However, for accuracy of qRT-PCR, appropriate reference gene is highly desirable to quantify the target gene level. Hence, the present study was aimed to identify the stable reference genes in staminate and pistillate flowers of J. curcas. In this study, 10 candidate reference genes were selected and evaluated for their expression stability in staminate and pistillate flowers, and their stability was validated by five different algorithms (ΔCt, BestKeeper, NormFinder, GeNorm and RefFinder). Resulting, TUB and EF found to be the two most stably expressed reference for staminate flower; while GAPDH1 and EF found to be the most stably expressed reference gene for pistillate flowers. Finally, RT-qPCR assays of target gene AGAMOUS using the identified most stable reference genes confirmed the reliability of selected reference genes in different stages of flower development. AGAMOUS gene expression levels at different stages were further proved by gene copy number analysis. Therefore, the present study provides guidance for selecting appropriate reference genes for analyzing the expression pattern of floral developmental genes in staminate and pistillate flowers of J. curcas. PMID:28234941

  2. Gene expression signature-based screening identifies new broadly effective influenza a antivirals.

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    Laurence Josset

    Full Text Available Classical antiviral therapies target viral proteins and are consequently subject to resistance. To counteract this limitation, alternative strategies have been developed that target cellular factors. We hypothesized that such an approach could also be useful to identify broad-spectrum antivirals. The influenza A virus was used as a model for its viral diversity and because of the need to develop therapies against unpredictable viruses as recently underlined by the H1N1 pandemic. We proposed to identify a gene-expression signature associated with infection by different influenza A virus subtypes which would allow the identification of potential antiviral drugs with a broad anti-influenza spectrum of activity. We analyzed the cellular gene expression response to infection with five different human and avian influenza A virus strains and identified 300 genes as differentially expressed between infected and non-infected samples. The most 20 dysregulated genes were used to screen the connectivity map, a database of drug-associated gene expression profiles. Candidate antivirals were then identified by their inverse correlation to the query signature. We hypothesized that such molecules would induce an unfavorable cellular environment for influenza virus replication. Eight potential antivirals including ribavirin were identified and their effects were tested in vitro on five influenza A strains. Six of the molecules inhibited influenza viral growth. The new pandemic H1N1 virus, which was not used to define the gene expression signature of infection, was inhibited by five out of the eight identified molecules, demonstrating that this strategy could contribute to identifying new broad anti-influenza agents acting on cellular gene expression. The identified infection signature genes, the expression of which are modified upon infection, could encode cellular proteins involved in the viral life cycle. This is the first study showing that gene expression

  3. GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed

    Directory of Open Access Journals (Sweden)

    Venkatasubrahmanyam Shivkumar

    2008-12-01

    Full Text Available Abstract Background The amount of gene expression data in the public repositories, such as NCBI Gene Expression Omnibus (GEO has grown exponentially, and provides a gold mine for bioinformaticians, but has not been easily accessible by biologists and clinicians. Results We developed an automated approach to annotate and analyze all GEO data sets, including 1,515 GEO data sets from 231 microarray types across 42 species, and performed 12,658 group versus group comparisons of 24 GEO-specified types. We then built GeneChaser, a web server that enables biologists and clinicians without bioinformatics skills to easily identify biological and clinical conditions in which a gene or set of genes was differentially expressed. GeneChaser displays these conditions in graphs, gives statistical comparisons, allows sort/filter functions and provides access to the original studies. We performed a single gene search for Nanog and a multiple gene search for Nanog, Oct4, Sox2 and LIN28, confirmed their roles in embryonic stem cell development, identified several drugs that regulate their expression, and suggested their potential roles in sex determination, abnormal sperm morphology, malaria infection, and cancer. Conclusion We demonstrated that GeneChaser is a powerful tool to elucidate information on function, transcriptional regulation, drug-response and clinical implications for genes of interest.

  4. Common Marker Genes Identified from Various Sample Types for Systemic Lupus Erythematosus.

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    Peng-Fei Bing

    Full Text Available Systemic lupus erythematosus (SLE is a complex auto-immune disease. Gene expression studies have been conducted to identify SLE-related genes in various types of samples. It is unknown whether there are common marker genes significant for SLE but independent of sample types, which may have potentials for follow-up translational research. The aim of this study is to identify common marker genes across various sample types for SLE.Based on four public microarray gene expression datasets for SLE covering three representative types of blood-born samples (monocyte; peripheral blood mononuclear cell, PBMC; whole blood, we utilized three statistics (fold-change, FC; t-test p value; false discovery rate adjusted p value to scrutinize genes simultaneously regulated with SLE across various sample types. For common marker genes, we conducted the Gene Ontology enrichment analysis and Protein-Protein Interaction analysis to gain insights into their functions.We identified 10 common marker genes associated with SLE (IFI6, IFI27, IFI44L, OAS1, OAS2, EIF2AK2, PLSCR1, STAT1, RNASE2, and GSTO1. Significant up-regulation of IFI6, IFI27, and IFI44L with SLE was observed in all the studied sample types, though the FC was most striking in monocyte, compared with PBMC and whole blood (8.82-251.66 vs. 3.73-74.05 vs. 1.19-1.87. Eight of the above 10 genes, except RNASE2 and GSTO1, interact with each other and with known SLE susceptibility genes, participate in immune response, RNA and protein catabolism, and cell death.Our data suggest that there exist common marker genes across various sample types for SLE. The 10 common marker genes, identified herein, deserve follow-up studies to dissert their potentials as diagnostic or therapeutic markers to predict SLE or treatment response.

  5. Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

    Science.gov (United States)

    Chen, Liming; Jenjaroenpun, Piroon; Pillai, Andrea Mun Ching; Ivshina, Anna V.; Ow, Ghim Siong; Efthimios, Motakis; Zhiqun, Tang; Lee, Song-Choon; Rogers, Keith; Ward, Jerrold M.; Mori, Seiichi; Adams, David J.; Jenkins, Nancy A.; Copeland, Neal G.; Ban, Kenneth Hon-Kim; Kuznetsov, Vladimir A.; Thiery, Jean Paul

    2017-01-01

    Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers. PMID:28251929

  6. Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

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    Wei Guo

    2017-01-01

    Full Text Available As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients’ personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.

  7. Using phylogenomic patterns and gene ontology to identify proteins of importance in plant evolution.

    Science.gov (United States)

    Cibrián-Jaramillo, Angélica; De la Torre-Bárcena, Jose E; Lee, Ernest K; Katari, Manpreet S; Little, Damon P; Stevenson, Dennis W; Martienssen, Rob; Coruzzi, Gloria M; DeSalle, Rob

    2010-07-12

    We use measures of congruence on a combined expressed sequenced tag genome phylogeny to identify proteins that have potential significance in the evolution of seed plants. Relevant proteins are identified based on the direction of partitioned branch and hidden support on the hypothesis obtained on a 16-species tree, constructed from 2,557 concatenated orthologous genes. We provide a general method for detecting genes or groups of genes that may be under selection in directions that are in agreement with the phylogenetic pattern. Gene partitioning methods and estimates of the degree and direction of support of individual gene partitions to the overall data set are used. Using this approach, we correlate positive branch support of specific genes for key branches in the seed plant phylogeny. In addition to basic metabolic functions, such as photosynthesis or hormones, genes involved in posttranscriptional regulation by small RNAs were significantly overrepresented in key nodes of the phylogeny of seed plants. Two genes in our matrix are of critical importance as they are involved in RNA-dependent regulation, essential during embryo and leaf development. These are Argonaute and the RNA-dependent RNA polymerase 6 found to be overrepresented in the angiosperm clade. We use these genes as examples of our phylogenomics approach and show that identifying partitions or genes in this way provides a platform to explain some of the more interesting organismal differences among species, and in particular, in the evolution of plants.

  8. Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

    Science.gov (United States)

    Guo, Wei; Shang, Dong-Mei; Cao, Jing-Hui; Feng, Kaiyan; Wang, ShaoPeng

    2017-01-01

    As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.

  9. Identifying suitable reference genes for gene expression analysis in developing skeletal muscle in pigs

    Directory of Open Access Journals (Sweden)

    Guanglin Niu

    2016-12-01

    Full Text Available The selection of suitable reference genes is crucial to accurately evaluate and normalize the relative expression level of target genes for gene function analysis. However, commonly used reference genes have variable expression levels in developing skeletal muscle. There are few reports that systematically evaluate the expression stability of reference genes across prenatal and postnatal developing skeletal muscle in mammals. Here, we used quantitative PCR to examine the expression levels of 15 candidate reference genes (ACTB, GAPDH, RNF7, RHOA, RPS18, RPL32, PPIA, H3F3, API5, B2M, AP1S1, DRAP1, TBP, WSB, and VAPB in porcine skeletal muscle at 26 different developmental stages (15 prenatal and 11 postnatal periods. We evaluated gene expression stability using the computer algorithms geNorm, NormFinder, and BestKeeper. Our results indicated that GAPDH and ACTB had the greatest variability among the candidate genes across prenatal and postnatal stages of skeletal muscle development. RPS18, API5, and VAPB had stable expression levels in prenatal stages, whereas API5, RPS18, RPL32, and H3F3 had stable expression levels in postnatal stages. API5 and H3F3 expression levels had the greatest stability in all tested prenatal and postnatal stages, and were the most appropriate reference genes for gene expression normalization in developing skeletal muscle. Our data provide valuable information for gene expression analysis during different stages of skeletal muscle development in mammals. This information can provide a valuable guide for the analysis of human diseases.

  10. Identifying suitable reference genes for gene expression analysis in developing skeletal muscle in pigs.

    Science.gov (United States)

    Niu, Guanglin; Yang, Yalan; Zhang, YuanYuan; Hua, Chaoju; Wang, Zishuai; Tang, Zhonglin; Li, Kui

    2016-01-01

    The selection of suitable reference genes is crucial to accurately evaluate and normalize the relative expression level of target genes for gene function analysis. However, commonly used reference genes have variable expression levels in developing skeletal muscle. There are few reports that systematically evaluate the expression stability of reference genes across prenatal and postnatal developing skeletal muscle in mammals. Here, we used quantitative PCR to examine the expression levels of 15 candidate reference genes (ACTB, GAPDH, RNF7, RHOA, RPS18, RPL32, PPIA, H3F3, API5, B2M, AP1S1, DRAP1, TBP, WSB, and VAPB) in porcine skeletal muscle at 26 different developmental stages (15 prenatal and 11 postnatal periods). We evaluated gene expression stability using the computer algorithms geNorm, NormFinder, and BestKeeper. Our results indicated that GAPDH and ACTB had the greatest variability among the candidate genes across prenatal and postnatal stages of skeletal muscle development. RPS18, API5, and VAPB had stable expression levels in prenatal stages, whereas API5, RPS18, RPL32, and H3F3 had stable expression levels in postnatal stages. API5 and H3F3 expression levels had the greatest stability in all tested prenatal and postnatal stages, and were the most appropriate reference genes for gene expression normalization in developing skeletal muscle. Our data provide valuable information for gene expression analysis during different stages of skeletal muscle development in mammals. This information can provide a valuable guide for the analysis of human diseases.

  11. Systematic enrichment analysis of gene expression profiling studies identifies consensus pathways implicated in colorectal cancer development

    Directory of Open Access Journals (Sweden)

    Jesús Lascorz

    2011-01-01

    Full Text Available Background: A large number of gene expression profiling (GEP studies on colorectal carcinogenesis have been performed but no reliable gene signature has been identified so far due to the lack of reproducibility in the reported genes. There is growing evidence that functionally related genes, rather than individual genes, contribute to the etiology of complex traits. We used, as a novel approach, pathway enrichment tools to define functionally related genes that are consistently up- or down-regulated in colorectal carcinogenesis. Materials and Methods: We started the analysis with 242 unique annotated genes that had been reported by any of three recent meta-analyses covering GEP studies on genes differentially expressed in carcinoma vs normal mucosa. Most of these genes (218, 91.9% had been reported in at least three GEP studies. These 242 genes were submitted to bioinformatic analysis using a total of nine tools to detect enrichment of Gene Ontology (GO categories or Kyoto Encyclopedia of Genes and Genomes (KEGG pathways. As a final consistency criterion the pathway categories had to be enriched by several tools to be taken into consideration. Results: Our pathway-based enrichment analysis identified the categories of ribosomal protein constituents, extracellular matrix receptor interaction, carbonic anhydrase isozymes, and a general category related to inflammation and cellular response as significantly and consistently overrepresented entities. Conclusions: We triaged the genes covered by the published GEP literature on colorectal carcinogenesis and subjected them to multiple enrichment tools in order to identify the consistently enriched gene categories. These turned out to have known functional relationships to cancer development and thus deserve further investigation.

  12. Analysis of pan-genome to identify the core genes and essential genes of Brucella spp.

    Science.gov (United States)

    Yang, Xiaowen; Li, Yajie; Zang, Juan; Li, Yexia; Bie, Pengfei; Lu, Yanli; Wu, Qingmin

    2016-04-01

    Brucella spp. are facultative intracellular pathogens, that cause a contagious zoonotic disease, that can result in such outcomes as abortion or sterility in susceptible animal hosts and grave, debilitating illness in humans. For deciphering the survival mechanism of Brucella spp. in vivo, 42 Brucella complete genomes from NCBI were analyzed for the pan-genome and core genome by identification of their composition and function of Brucella genomes. The results showed that the total 132,143 protein-coding genes in these genomes were divided into 5369 clusters. Among these, 1710 clusters were associated with the core genome, 1182 clusters with strain-specific genes and 2477 clusters with dispensable genomes. COG analysis indicated that 44 % of the core genes were devoted to metabolism, which were mainly responsible for energy production and conversion (COG category C), and amino acid transport and metabolism (COG category E). Meanwhile, approximately 35 % of the core genes were in positive selection. In addition, 1252 potential essential genes were predicted in the core genome by comparison with a prokaryote database of essential genes. The results suggested that the core genes in Brucella genomes are relatively conservation, and the energy and amino acid metabolism play a more important role in the process of growth and reproduction in Brucella spp. This study might help us to better understand the mechanisms of Brucella persistent infection and provide some clues for further exploring the gene modules of the intracellular survival in Brucella spp.

  13. A novel reverse-genetic approach (SIMF) identifies Mutator insertions in new Myb genes.

    Science.gov (United States)

    Rabinowicz, P D; Grotewold, E

    2000-11-01

    We have developed a new strategy designated SIMF (Systematic Insertional Mutagenesis of Families), to identify DNA insertions in many members of a gene family simultaneously. This method requires only a short amino acid sequence conserved in all members of the family to make a degenerate oligonucleotide, and a sequence from the end of the DNA insertion. The SIMF strategy was successfully applied to the large maize R2R3 Myb family of regulatory genes, and Mutator insertions in several novel Myb genes were identified. Application of this technique to identify insertions in other large gene families could significantly decrease the effort involved in screening at the same time for insertions in all members of groups of genes that share a limited sequence identity.

  14. Gene expression meta-analysis identifies chromosomal regions involved in ovarian cancer survival

    DEFF Research Database (Denmark)

    Thomassen, Mads; Jochumsen, Kirsten M; Mogensen, Ole;

    2009-01-01

    the relation of gene expression and chromosomal position to identify chromosomal regions of importance for early recurrence of ovarian cancer. By use of *Gene Set Enrichment Analysis*, we have ranked chromosomal regions according to their association to survival. Over-representation analysis including 1......Ovarian cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth, whereas others are causal for metastasis and recurrence. By using publicly available data sets, we have investigated......-4 consecutive cytogenetic bands identified regions with increased expression for chromosome 5q12-14, and a very large region of chromosome 7 with the strongest signal at 7p15-13 among tumors from short-living patients. Reduced gene expression was identified at 4q26-32, 6p12-q15, 9p21-q32, and 11p14-11. We...

  15. Description and interpretation of various SNPs identified by BRCA2 gene sequencing

    Directory of Open Access Journals (Sweden)

    Anca Negura

    2011-12-01

    Full Text Available Molecular diagnosis for hereditary breast and ovarian cancer (HBOC involves systematic DNA sequencing of predisposition genes like BRCA1 or BRCA2. Deleterious mutations within such genes are responsible for developing the disease, but other sequence variants can also be identified. Common Single Nucleotide Polymorphisms (SNPs are usually present in human genome, defining alleles whose frequencies widely vary in different populations. Either intragenic or intronic, silent or generating aminoacid substitutions, SNPs cannot be afforded themselves a predisposition status. However, prevalent SNPs can be used to define gene haplotypes, with also various frequencies. Since some mutation can easily be assigned to haplotypes (such is the case for BRCA1 gene, SNPs can therefore provide usual information in interpreting gene mutations effects on hereditary predisposition to cancer. Here we describe 10 BRCA2 SNPs identified by complete gene sequencing

  16. A systems genetics approach identifies genes and pathways for type 2 diabetes in human islets

    DEFF Research Database (Denmark)

    Taneera, Jalal; Lang, Stefan; Sharma, Amitabh;

    2012-01-01

    Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors. Using 48 genes located near T2D risk variants, we identified...... gene coexpression and protein-protein interaction networks that were strongly associated with islet insulin secretion and HbA(1c). We integrated our data to form a rank list of putative T2D genes, of which CHL1, LRFN2, RASGRP1, and PPM1K were validated in INS-1 cells to influence insulin secretion...... of genes potentially involved in T2D....

  17. Cartilage-selective genes identified in genome-scale analysis of non-cartilage and cartilage gene expression

    Directory of Open Access Journals (Sweden)

    Cohn Zachary A

    2007-06-01

    Full Text Available Abstract Background Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius. Results 161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis. Conclusion Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.

  18. GeneBrowser 2: an application to explore and identify common biological traits in a set of genes

    Directory of Open Access Journals (Sweden)

    Oliveira José

    2010-07-01

    Full Text Available Abstract Background The development of high-throughput laboratory techniques created a demand for computer-assisted result analysis tools. Many of these techniques return lists of genes whose interpretation requires finding relevant biological roles for the problem at hand. The required information is typically available in public databases, and usually, this information must be manually retrieved to complement the analysis. This process is a very time-consuming task that should be automated as much as possible. Results GeneBrowser is a web-based tool that, for a given list of genes, combines data from several public databases with visualisation and analysis methods to help identify the most relevant and common biological characteristics. The functionalities provided include the following: a central point with the most relevant biological information for each inserted gene; a list of the most related papers in PubMed and gene expression studies in ArrayExpress; and an extended approach to functional analysis applied to Gene Ontology, homologies, gene chromosomal localisation and pathways. Conclusions GeneBrowser provides a unique entry point to several visualisation and analysis methods, providing fast and easy analysis of a set of genes. GeneBrowser fills the gap between Web portals that analyse one gene at a time and functional analysis tools that are limited in scope and usually desktop-based.

  19. The compact Selaginella genome identifies changes in gene content associated with the evolution of vascular plants

    Energy Technology Data Exchange (ETDEWEB)

    Grigoriev, Igor V.; Banks, Jo Ann; Nishiyama, Tomoaki; Hasebe, Mitsuyasu; Bowman, John L.; Gribskov, Michael; dePamphilis, Claude; Albert, Victor A.; Aono, Naoki; Aoyama, Tsuyoshi; Ambrose, Barbara A.; Ashton, Neil W.; Axtell, Michael J.; Barker, Elizabeth; Barker, Michael S.; Bennetzen, Jeffrey L.; Bonawitz, Nicholas D.; Chapple, Clint; Cheng, Chaoyang; Correa, Luiz Gustavo Guedes; Dacre, Michael; DeBarry, Jeremy; Dreyer, Ingo; Elias, Marek; Engstrom, Eric M.; Estelle, Mark; Feng, Liang; Finet, Cedric; Floyd, Sandra K.; Frommer, Wolf B.; Fujita, Tomomichi; Gramzow, Lydia; Gutensohn, Michael; Harholt, Jesper; Hattori, Mitsuru; Heyl, Alexander; Hirai, Tadayoshi; Hiwatashi, Yuji; Ishikawa, Masaki; Iwata, Mineko; Karol, Kenneth G.; Koehler, Barbara; Kolukisaoglu, Uener; Kubo, Minoru; Kurata, Tetsuya; Lalonde, Sylvie; Li, Kejie; Li, Ying; Litt, Amy; Lyons, Eric; Manning, Gerard; Maruyama, Takeshi; Michael, Todd P.; Mikami, Koji; Miyazaki, Saori; Morinaga, Shin-ichi; Murata, Takashi; Mueller-Roeber, Bernd; Nelson, David R.; Obara, Mari; Oguri, Yasuko; Olmstead, Richard G.; Onodera, Naoko; Petersen, Bent Larsen; Pils, Birgit; Prigge, Michael; Rensing, Stefan A.; Riano-Pachon, Diego Mauricio; Roberts, Alison W.; Sato, Yoshikatsu; Scheller, Henrik Vibe; Schulz, Burkhard; Schulz, Christian; Shakirov, Eugene V.; Shibagaki, Nakako; Shinohara, Naoki; Shippen, Dorothy E.; Sorensen, Iben; Sotooka, Ryo; Sugimoto, Nagisa; Sugita, Mamoru; Sumikawa, Naomi; Tanurdzic, Milos; Theilsen, Gunter; Ulvskov, Peter; Wakazuki, Sachiko; Weng, Jing-Ke; Willats, William W.G.T.; Wipf, Daniel; Wolf, Paul G.; Yang, Lixing; Zimmer, Andreas D.; Zhu, Qihui; Mitros, Therese; Hellsten, Uffe; Loque, Dominique; Otillar, Robert; Salamov, Asaf; Schmutz, Jeremy; Shapiro, Harris; Lindquist, Erika; Lucas, Susan; Rokhsar, Daniel

    2011-04-28

    We report the genome sequence of the nonseed vascular plant, Selaginella moellendorffii, and by comparative genomics identify genes that likely played important roles in the early evolution of vascular plants and their subsequent evolution

  20. Tsukamurella pulmonis Bloodstream Infection Identified by secA1 Gene Sequencing

    OpenAIRE

    Pérez del Molino Bernal, IC; Cano García, María Eliecer; García de la Fuente, C; Martínez Martínez, Luis; López, Monica; Fernández Mazarrasa, Carlos; Agüero Balbín, Jesús

    2014-01-01

    Recurrent bloodstream infections caused by a Gram-positive bacterium affected an immunocompromised child. Tsukamurella pulmonis was the microorganism identified by secA1 gene sequencing. Antibiotic treatment in combination with removal of the subcutaneous port healed the patient.

  1. Epigenetic characterization of the growth hormone gene identifies SmcHD1 as a regulator of autosomal gene clusters.

    Directory of Open Access Journals (Sweden)

    Shabnam Massah

    Full Text Available Regulatory elements for the mouse growth hormone (GH gene are located distally in a putative locus control region (LCR in addition to key elements in the promoter proximal region. The role of promoter DNA methylation for GH gene regulation is not well understood. Pit-1 is a POU transcription factor required for normal pituitary development and obligatory for GH gene expression. In mammals, Pit-1 mutations eliminate GH production resulting in a dwarf phenotype. In this study, dwarf mice illustrated that Pit-1 function was obligatory for GH promoter hypomethylation. By monitoring promoter methylation levels during developmental GH expression we found that the GH promoter became hypomethylated coincident with gene expression. We identified a promoter differentially methylated region (DMR that was used to characterize a methylation-dependent DNA binding activity. Upon DNA affinity purification using the DMR and nuclear extracts, we identified structural maintenance of chromosomes hinge domain containing -1 (SmcHD1. To better understand the role of SmcHD1 in genome-wide gene expression, we performed microarray analysis and compared changes in gene expression upon reduced levels of SmcHD1 in human cells. Knock-down of SmcHD1 in human embryonic kidney (HEK293 cells revealed a disproportionate number of up-regulated genes were located on the X-chromosome, but also suggested regulation of genes on non-sex chromosomes. Among those, we identified several genes located in the protocadherin β cluster. In addition, we found that imprinted genes in the H19/Igf2 cluster associated with Beckwith-Wiedemann and Silver-Russell syndromes (BWS & SRS were dysregulated. For the first time using human cells, we showed that SmcHD1 is an important regulator of imprinted and clustered genes.

  2. X-exome sequencing of 405 unresolved families identifies seven novel intellectual disability genes

    DEFF Research Database (Denmark)

    Hu, H; Haas, S A; Chelly, J;

    2016-01-01

    X-linked intellectual disability (XLID) is a clinically and genetically heterogeneous disorder. During the past two decades in excess of 100 X-chromosome ID genes have been identified. Yet, a large number of families mapping to the X-chromosome remained unresolved suggesting that more XLID genes ...

  3. Exploiting natural variation in Saccharomyces cerevisiae to identify genes for increased ethanol resistance.

    Science.gov (United States)

    Lewis, Jeffrey A; Elkon, Isaac M; McGee, Mick A; Higbee, Alan J; Gasch, Audrey P

    2010-12-01

    Ethanol production from lignocellulosic biomass holds promise as an alternative fuel. However, industrial stresses, including ethanol stress, limit microbial fermentation and thus prevent cost competitiveness with fossil fuels. To identify novel engineering targets for increased ethanol tolerance, we took advantage of natural diversity in wild Saccharomyces cerevisiae strains. We previously showed that an S288c-derived lab strain cannot acquire higher ethanol tolerance after a mild ethanol pretreatment, which is distinct from other stresses. Here, we measured acquired ethanol tolerance in a large panel of wild strains and show that most strains can acquire higher tolerance after pretreatment. We exploited this major phenotypic difference to address the mechanism of acquired ethanol tolerance, by comparing the global gene expression response to 5% ethanol in S288c and two wild strains. Hundreds of genes showed variation in ethanol-dependent gene expression across strains. Computational analysis identified several transcription factor modules and known coregulated genes as differentially expressed, implicating genetic variation in the ethanol signaling pathway. We used this information to identify genes required for acquisition of ethanol tolerance in wild strains, including new genes and processes not previously linked to ethanol tolerance, and four genes that increase ethanol tolerance when overexpressed. Our approach shows that comparative genomics across natural isolates can quickly identify genes for industrial engineering while expanding our understanding of natural diversity.

  4. CTDGFinder: A Novel Homology-Based Algorithm for Identifying Closely Spaced Clusters of Tandemly Duplicated Genes.

    Science.gov (United States)

    Ortiz, Juan F; Rokas, Antonis

    2017-01-01

    Closely spaced clusters of tandemly duplicated genes (CTDGs) contribute to the diversity of many phenotypes, including chemosensation, snake venom, and animal body plans. CTDGs have traditionally been identified subjectively as genomic neighborhoods containing several gene duplicates in close proximity; however, CTDGs are often highly variable with respect to gene number, intergenic distance, and synteny. This lack of formal definition hampers the study of CTDG evolutionary dynamics and the discovery of novel CTDGs in the exponentially growing body of genomic data. To address this gap, we developed a novel homology-based algorithm, CTDGFinder, which formalizes and automates the identification of CTDGs by examining the physical distribution of individual members of families of duplicated genes across chromosomes. Application of CTDGFinder accurately identified CTDGs for many well-known gene clusters (e.g., Hox and beta-globin gene clusters) in the human, mouse and 20 other mammalian genomes. Differences between previously annotated gene clusters and our inferred CTDGs were due to the exclusion of nonhomologs that have historically been considered parts of specific gene clusters, the inclusion or absence of genes between the CTDGs and their corresponding gene clusters, and the splitting of certain gene clusters into distinct CTDGs. Examination of human genes showing tissue-specific enhancement of their expression by CTDGFinder identified members of several well-known gene clusters (e.g., cytochrome P450s and olfactory receptors) and revealed that they were unequally distributed across tissues. By formalizing and automating CTDG identification, CTDGFinder will facilitate understanding of CTDG evolutionary dynamics, their functional implications, and how they are associated with phenotypic diversity. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e

  5. Use of suppression subtractive hybridization to identify downy mildew genes expressed during infection of Arabidopsis thaliana.

    Science.gov (United States)

    Bittner-Eddy, Peter D; Allen, Rebecca L; Rehmany, Anne P; Birch, Paul; Beynon, Jim L

    2003-11-01

    SUMMARY Peronospora parasitica is an obligate biotrophic oomycete that causes downy mildew in Arabidopsis thaliana and Brassica species. Our goal is to identify P. parasitica (At) genes that are involved in pathogenicity. We used suppression subtractive hybridization (SSH) to generate cDNA libraries enriched for in planta-expressed parasite genes and up-regulated host genes. A total of 1345 clones were sequenced representing cDNA fragments from 25 putative P. parasitica (At) genes (Ppat 1-25) and 618 Arabidopsis genes. Analyses of expression patterns showed that 15 Ppats were expressed only in planta. Eleven Ppats encoded peptides with homology (BlastP values planta-expressed genes from P. parasitica (At) that complements other gene discovery approaches such as EST sequencing.

  6. Gene dosage, expression, and ontology analysis identifies driver genes in the carcinogenesis and chemoradioresistance of cervical cancer.

    Science.gov (United States)

    Lando, Malin; Holden, Marit; Bergersen, Linn C; Svendsrud, Debbie H; Stokke, Trond; Sundfør, Kolbein; Glad, Ingrid K; Kristensen, Gunnar B; Lyng, Heidi

    2009-11-01

    Integrative analysis of gene dosage, expression, and ontology (GO) data was performed to discover driver genes in the carcinogenesis and chemoradioresistance of cervical cancers. Gene dosage and expression profiles of 102 locally advanced cervical cancers were generated by microarray techniques. Fifty-two of these patients were also analyzed with the Illumina expression method to confirm the gene expression results. An independent cohort of 41 patients was used for validation of gene expressions associated with clinical outcome. Statistical analysis identified 29 recurrent gains and losses and 3 losses (on 3p, 13q, 21q) associated with poor outcome after chemoradiotherapy. The intratumor heterogeneity, assessed from the gene dosage profiles, was low for these alterations, showing that they had emerged prior to many other alterations and probably were early events in carcinogenesis. Integration of the alterations with gene expression and GO data identified genes that were regulated by the alterations and revealed five biological processes that were significantly overrepresented among the affected genes: apoptosis, metabolism, macromolecule localization, translation, and transcription. Four genes on 3p (RYBP, GBE1) and 13q (FAM48A, MED4) correlated with outcome at both the gene dosage and expression level and were satisfactorily validated in the independent cohort. These integrated analyses yielded 57 candidate drivers of 24 genetic events, including novel loci responsible for chemoradioresistance. Further mapping of the connections among genetic events, drivers, and biological processes suggested that each individual event stimulates specific processes in carcinogenesis through the coordinated control of multiple genes. The present results may provide novel therapeutic opportunities of both early and advanced stage cervical cancers.

  7. NCBI nr-aa BLAST: CBRC-AGAM-03-0043 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0043 ref|XP_804109.1| calpain cysteine peptidase [Trypanosoma cruzi st...rain CL Brener] gb|EAN82258.1| calpain cysteine peptidase, putative [Trypanosoma cruzi] XP_804109.1 0.99 29% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-05-0006 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0006 ref|XP_001647749.1| neuropeptide y receptor (npy-r) (pr4 receptor...) [Aedes aegypti] gb|EAT32417.1| neuropeptide y receptor (npy-r) (pr4 receptor) [Aedes aegypti] XP_001647749.1 1e-87 55% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-05-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0017 ref|XP_001653188.1| neuropeptide y receptor (npy-r) (pr4 receptor...) [Aedes aegypti] gb|EAT39936.1| neuropeptide y receptor (npy-r) (pr4 receptor) [Aedes aegypti] XP_001653188.1 2e-73 55% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-05-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0017 ref|XP_001647749.1| neuropeptide y receptor (npy-r) (pr4 receptor...) [Aedes aegypti] gb|EAT32417.1| neuropeptide y receptor (npy-r) (pr4 receptor) [Aedes aegypti] XP_001647749.1 1e-64 59% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-05-0006 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0006 ref|XP_001653188.1| neuropeptide y receptor (npy-r) (pr4 receptor...) [Aedes aegypti] gb|EAT39936.1| neuropeptide y receptor (npy-r) (pr4 receptor) [Aedes aegypti] XP_001653188.1 7e-99 54% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-07-0022 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0022 ref|YP_858652.1| O-antigen polymerase [Aeromonas hydrophila subsp. hydrop...hila ATCC 7966] gb|ABK39666.1| O-antigen polymerase [Aeromonas hydrophila subsp. hydrophila ATCC 7966] YP_858652.1 1e-162 84% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-01-0097 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0097 ref|YP_001433581.1| protein of unknown function DUF296 [Roseiflexus castenholz...ii DSM 13941] gb|ABU59563.1| protein of unknown function DUF296 [Roseiflexus castenholzii DSM 13941] YP_001433581.1 0.11 35% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-04-0109 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0109 ref|YP_001430593.1| Adenylosuccinate synthase [Roseiflexus castenholz...ii DSM 13941] gb|ABU56575.1| Adenylosuccinate synthase [Roseiflexus castenholzii DSM 13941] YP_001430593.1 4.6 27% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0035 ref|ZP_00951521.1| Ribonuclease BN [Croceibacter atlanticus HTCC2...559] gb|EAP86044.1| Ribonuclease BN [Croceibacter atlanticus HTCC2559] ZP_00951521.1 1e-33 29% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|ZP_00950995.1| heavy-metal transporting P-type ATPase [Croceibacter atlantic...us HTCC2559] gb|EAP86703.1| heavy-metal transporting P-type ATPase [Croceibacter atlanticus HTCC2559] ZP_00950995.1 1e-132 63% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-02-0009 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0009 ref|ZP_01871667.1| transporter, putative [Caminibacter mediatlantic...us TB-2] gb|EDM23795.1| transporter, putative [Caminibacter mediatlanticus TB-2] ZP_01871667.1 0.19 20% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|ZP_00950924.1| heavy-metal transporting P-type ATPase [Croceibacter atlantic...us HTCC2559] gb|EAP86632.1| heavy-metal transporting P-type ATPase [Croceibacter atlanticus HTCC2559] ZP_00950924.1 1e-128 62% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0041 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0041 ref|ZP_00949869.1| hypothetical protein CA2559_04595 [Croceibacter atlantic...us HTCC2559] gb|EAP88008.1| hypothetical protein CA2559_04595 [Croceibacter atlanticus HTCC2559] ZP_00949869.1 3e-31 32% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-07-0034 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0034 ref|YP_661398.1| major facilitator superfamily MFS_1 [Pseudoalteromonas atlantic...a T6c] gb|ABG40344.1| major facilitator superfamily MFS_1 [Pseudoalteromonas atlantica T6c] YP_661398.1 5e-57 45% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|ZP_00949226.1| hypothetical protein CA2559_01380 [Croceibacter atlantic...us HTCC2559] gb|EAP87365.1| hypothetical protein CA2559_01380 [Croceibacter atlanticus HTCC2559] ZP_00949226.1 6e-68 45% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-07-0042 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0042 ref|ZP_00950694.1| potassium efflux system protein [Croceibacter atlantic...us HTCC2559] gb|EAP86402.1| potassium efflux system protein [Croceibacter atlanticus HTCC2559] ZP_00950694.1 1e-79 47% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-03-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0017 ref|ZP_01562088.1| hypothetical protein Bcenmc03DRAFT_2302 [Burkh...olderia cenocepacia MC0-3] gb|EAV60091.1| hypothetical protein Bcenmc03DRAFT_2302 [Burkholderia cenocepacia MC0-3] ZP_01562088.1 4e-04 26% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-01-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0052 ref|ZP_01564777.1| hypothetical protein Bcenmc03DRAFT_4887 [Burkh...olderia cenocepacia MC0-3] gb|EAV57364.1| hypothetical protein Bcenmc03DRAFT_4887 [Burkholderia cenocepacia MC0-3] ZP_01564777.1 5e-05 28% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-02-0081 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0081 ref|YP_001129521.1| ATP synthase F0 subunit 6 [Hynobius arisanens...is] gb|ABO20776.1| ATP synthase F0 subunit 6 [Hynobius arisanensis] YP_001129521.1 0.43 24% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-07-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0040 ref|ZP_01882627.1| multidrug resistance protein [Pedobacter sp. B...AL39] gb|EDM38378.1| multidrug resistance protein [Pedobacter sp. BAL39] ZP_01882627.1 3e-50 42% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-07-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0044 ref|ZP_01884120.1| Mn2+/Fe2+ transporter, NRAMP family protein [Pedo...bacter sp. BAL39] gb|EDM36559.1| Mn2+/Fe2+ transporter, NRAMP family protein [Pedobacter sp. BAL39] ZP_01884120.1 1e-119 55% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-07-0073 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0073 ref|ZP_01886744.1| hypothetical protein PBAL39_17149 [Pedobacter ...sp. BAL39] gb|EDM34019.1| hypothetical protein PBAL39_17149 [Pedobacter sp. BAL39] ZP_01886744.1 1e-114 75% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-07-0041 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0041 ref|YP_001193736.1| UbiA prenyltransferase [Flavobacterium johnson...iae UW101] gb|ABQ04417.1| UbiA prenyltransferase [Flavobacterium johnsoniae UW101] YP_001193736.1 4e-31 33% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0042 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0042 ref|ZP_00591264.1| Potassium efflux system protein [Prosthecochlo...ris aestuarii DSM 271] gb|EAN23634.1| Potassium efflux system protein [Prosthecochloris aestuarii DSM 271] ZP_00591264.1 1e-75 46% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-01-0076 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0076 ref|ZP_01979394.1| hypothetical protein A5A_0268 [Vibrio cholerae... MZO-2] gb|EDM53680.1| hypothetical protein A5A_0268 [Vibrio cholerae MZO-2] ZP_01979394.1 0.49 25% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-03-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0017 ref|ZP_01512956.1| conserved hypothetical protein [Burkholderia phytofirm...ans PsJN] gb|EAV02438.1| conserved hypothetical protein [Burkholderia phytofirmans PsJN] ZP_01512956.1 0.070 25% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0060 ref|ZP_01507056.1| diguanylate cyclase [Burkholderia phytofirmans... PsJN] gb|EAV08201.1| diguanylate cyclase [Burkholderia phytofirmans PsJN] ZP_01507056.1 6e-21 28% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-03-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0016 ref|ZP_01508657.1| CDP-alcohol phosphatidyltransferase [Burkholderia phytofirm...ans PsJN] gb|EAV06352.1| CDP-alcohol phosphatidyltransferase [Burkholderia phytofirmans PsJN] ZP_01508657.1 0.024 29% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0060 ref|ZP_01512692.1| diguanylate cyclase [Burkholderia phytofirmans... PsJN] gb|EAV02625.1| diguanylate cyclase [Burkholderia phytofirmans PsJN] ZP_01512692.1 2e-23 29% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-05-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0028 ref|ZP_01444683.1| tail fiber protein, putative [Roseovarius sp. ...HTCC2601] gb|EAU45064.1| tail fiber protein, putative [Roseovarius sp. HTCC2601] ZP_01444683.1 1e-17 36% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-02-0103 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0103 ref|ZP_01443014.1| hypothetical protein R2601_13804 [Roseovarius ...sp. HTCC2601] gb|EAU46901.1| hypothetical protein R2601_13804 [Roseovarius sp. HTCC2601] ZP_01443014.1 5.5 35% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0016 ref|NP_935992.1| putative multidrug resistance protein [Vibrio vulnificus... YJ016] dbj|BAC95963.1| putative multidrug resistance protein [Vibrio vulnificus YJ016] NP_935992.1 4e-51 56% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0007 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0007 ref|YP_001099812.1| putative Urea transporter [Herminiimonas arse...nicoxydans] emb|CAL61685.1| putative Urea transporter [Herminiimonas arsenicoxydans] YP_001099812.1 4e-36 33% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-02-0069 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0069 ref|XP_001652791.1| GDP-fucose transporter, putative [Aedes aegyp...ti] gb|EAT40804.1| GDP-fucose transporter, putative [Aedes aegypti] XP_001652791.1 1e-129 83% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-05-0031 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0031 ref|NP_068292.1| PxORF73 peptide [Plutella xylostella granuloviru...s] gb|AAG27371.1|AF270937_73 PxORF73 peptide [Plutella xylostella granulovirus] NP_068292.1 2e-15 42% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-07-0048 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0048 ref|YP_069528.1| MscS family mechanosensitive channel [Yersinia p...seudotuberculosis IP 32953] emb|CAH20227.1| MscS family mechanosensitive channel [Yersinia pseudotuberculosis IP 32953] YP_069528.1 1e-106 53% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-04-0109 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0109 ref|ZP_02018486.1| acriflavin resistance protein [Methylobacterium extorque...ns PA1] gb|EDN54609.1| acriflavin resistance protein [Methylobacterium extorquens PA1] ZP_02018486.1 2.7 30% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-01-0094 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0094 ref|XP_001650650.1| rab6 gtpase activating protein, gapcena (rabgap...1 protein) [Aedes aegypti] gb|EAT48196.1| rab6 gtpase activating protein, gapcena (rabgap1 protein) [Aedes aegypti] XP_001650650.1 1e-172 69% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-01-0094 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0094 ref|XP_001650651.1| rab6 gtpase activating protein, gapcena (rabgap...1 protein) [Aedes aegypti] gb|EAT48197.1| rab6 gtpase activating protein, gapcena (rabgap1 protein) [Aedes aegypti] XP_001650651.1 1e-172 69% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|YP_001195410.1| heavy metal translocating P-type ATPase [Flavobacterium john...soniae UW101] gb|ABQ06091.1| heavy metal translocating P-type ATPase [Flavobacterium johnsoniae UW101] YP_001195410.1 1e-129 64% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|YP_001192547.1| heavy metal translocating P-type ATPase [Flavobacterium john...soniae UW101] gb|ABQ03228.1| heavy metal translocating P-type ATPase [Flavobacterium johnsoniae UW101] YP_001192547.1 1e-129 62% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-07-0073 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0073 ref|YP_001195652.1| protein of unknown function DUF81 [Flavobacterium john...soniae UW101] gb|ABQ06333.1| protein of unknown function DUF81 [Flavobacterium johnsoniae UW101] YP_001195652.1 4e-97 65% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-02-0138 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0138 ref|ZP_02029487.1| hypothetical protein BIFADO_01945 [Bifidobacterium adolescent...is L2-32] gb|EDN81820.1| hypothetical protein BIFADO_01945 [Bifidobacterium adolescentis L2-32] ZP_02029487.1 3e-07 24% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0029 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0029 ref|YP_001126159.1| Lantibiotic mersacidin modifying enzyme [Geob...acillus thermodenitrificans NG80-2] gb|ABO67414.1| Lantibiotic mersacidin modifying enzyme [Geobacillus thermodenitrificans NG80-2] YP_001126159.1 1e-17 24% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0029 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0029 ref|NP_241318.1| lantibiotic mersacidin modifying enzyme [Bacillu...s halodurans C-125] dbj|BAB04171.1| lantibiotic mersacidin modifying enzyme [Bacillus halodurans C-125] NP_241318.1 3e-26 26% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-07-0067 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0067 ref|NP_709711.1| similar to protein of glp regulon [Shigella flex...neri 2a str. 301] gb|AAN45418.1| similar to protein of glp regulon [Shigella flexneri 2a str. 301] NP_709711.1 2e-79 62% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0020 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0020 ref|YP_311760.1| putative transport protein [Shigella sonnei Ss04...6] gb|AAZ89525.1| putative transport protein [Shigella sonnei Ss046] YP_311760.1 1e-120 62% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-01-0097 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0097 ref|YP_001263414.1| major facilitator superfamily MFS_1 [Sphingomonas... wittichii RW1] gb|ABQ69276.1| major facilitator superfamily MFS_1 [Sphingomonas wittichii RW1] YP_001263414.1 4.6 27% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-02-0168 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0168 ref|YP_001263414.1| major facilitator superfamily MFS_1 [Sphingomonas... wittichii RW1] gb|ABQ69276.1| major facilitator superfamily MFS_1 [Sphingomonas wittichii RW1] YP_001263414.1 0.77 31% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-01-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0052 ref|YP_333983.1| hypothetical protein BURPS1710b_2591 [Burkholder...ia pseudomallei 1710b] gb|ABA48937.1| hypothetical protein BURPS1710b_2591 [Burkholderia pseudomallei 1710b] YP_333983.1 5e-11 27% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-03-0021 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0021 ref|YP_331887.1| Planctomycete extracellular domain protein [Burkholder...ia pseudomallei 1710b] gb|ABA51048.1| Planctomycete extracellular domain protein [Burkholderia pseudomallei 1710b] YP_331887.1 0.005 31% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-04-0021 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0021 ref|ZP_01571862.1| 200 kDa antigen p200, putative [Burkholderia m...ultivorans ATCC 17616] gb|EAV64386.1| 200 kDa antigen p200, putative [Burkholderia multivorans ATCC 17616] ZP_01571862.1 0.82 25% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-02-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0064 ref|YP_335617.1| haemagluttinin family protein [Burkholderia pseu...domallei 1710b] gb|ABA51851.1| haemagluttinin family protein [Burkholderia pseudomallei 1710b] YP_335617.1 0.0 31% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-02-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0044 ref|ZP_01559561.1| conserved hypothetical protein [Burkholderia a...mbifaria MC40-6] gb|EAV47895.1| conserved hypothetical protein [Burkholderia ambifaria MC40-6] ZP_01559561.1 0.83 27% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0060 ref|YP_553299.1| Putative diguanylate cyclase (GGDEF domain) [Burkholder...ia xenovorans LB400] gb|ABE33949.1| Putative diguanylate cyclase (GGDEF domain) [Burkholderia xenovorans LB400] YP_553299.1 3e-20 29% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0107 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0107 ref|YP_001475028.1| diguanylate cyclase [Shewanella sediminis HAW...-EB3] gb|ABV37900.1| diguanylate cyclase [Shewanella sediminis HAW-EB3] YP_001475028.1 2.3 32% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-07-0010 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0010 ref|ZP_01842482.1| amino acid permease-associated region [Shewanella... baltica OS223] gb|EDK49834.1| amino acid permease-associated region [Shewanella baltica OS223] ZP_01842482.1 1e-127 91% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-07-0032 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0032 ref|YP_001094015.1| glucose/galactose transporter [Shewanella loi...hica PV-4] gb|ABO23756.1| glucose/galactose transporter [Shewanella loihica PV-4] YP_001094015.1 4e-28 35% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-04-0116 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0116 ref|YP_591829.1| hypothetical protein Acid345_2754 [Acidobacteria... bacterium Ellin345] gb|ABF41755.1| hypothetical protein Acid345_2754 [Acidobacteria bacterium Ellin345] YP_591829.1 0.033 28% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-05-0030 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0030 ref|YP_591012.1| Polynucleotide adenylyltransferase [Acidobacteri...a bacterium Ellin345] gb|ABF40938.1| Polynucleotide adenylyltransferase [Acidobacteria bacterium Ellin345] YP_591012.1 4e-06 35% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-07-0022 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0022 ref|YP_087435.1| RfaL protein [Mannheimia succiniciproducens MBEL...55E] gb|AAU36850.1| RfaL protein [Mannheimia succiniciproducens MBEL55E] YP_087435.1 5e-17 26% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-07-0022 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0022 ref|ZP_01789747.1| hypothetical protein CGSHiAA_08330 [Haemophilus influenza...e PittAA] gb|EDK08473.1| hypothetical protein CGSHiAA_08330 [Haemophilus influenzae PittAA] ZP_01789747.1 2e-14 23% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-07-0002 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0002 ref|YP_001142445.1| membrane protein [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO90697.1| membrane protein [Aeromonas salmonicida subsp. salmonicida A449] YP_001142445.1 1e-130 93% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0060 ref|YP_001142002.1| GGDEF domain protein [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO90254.1| GGDEF domain protein [Aeromonas salmonicida subsp. salmonicida A449] YP_001142002.1 6e-21 30% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0010 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0010 ref|YP_001141362.1| amino acid permease [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO89614.1| amino acid permease [Aeromonas salmonicida subsp. salmonicida A449] YP_001141362.1 1e-132 96% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-07-0022 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0022 ref|YP_001140043.1| O-antigen ligase [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO88295.1| O-antigen ligase [Aeromonas salmonicida subsp. salmonicida A449] YP_001140043.1 1e-145 76% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0021 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0021 ref|YP_001140588.1| Na+/H antiporter NhaA [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO88840.1| Na+/H antiporter NhaA [Aeromonas salmonicida subsp. salmonicida A449] YP_001140588.1 1e-123 94% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-01-0054 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0054 ref|YP_001142086.1| transporter, NadC family [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO90338.1| transporter, NadC family [Aeromonas salmonicida subsp. salmonicida A449] YP_001142086.1 0.76 25% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0064 ref|YP_001143407.1| sulfatase [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO91659.1| sulfatase [Aeromonas salmonicida subsp. salmonicida A449] YP_001143407.1 0.0 87% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-07-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0060 ref|YP_001141367.1| GGDEF domain protein [Aeromonas salmonicida subsp. salmon...icida A449] gb|ABO89619.1| GGDEF domain protein [Aeromonas salmonicida subsp. salmonicida A449] YP_001141367.1 1e-165 88% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|ZP_01051647.1| Heavy metal translocating P-type ATPase [Tenac...ibaculum sp. MED152] gb|EAQ41075.1| Heavy metal translocating P-type ATPase [Polaribacter dokdonensis MED152] ZP_01051647.1 1e-132 63% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0038 ref|ZP_01688972.1| ABC transporter, permease protein, putative [Microscilla marina... ATCC 23134] gb|EAY29695.1| ABC transporter, permease protein, putative [Microscilla marina ATCC 23134] ZP_01688972.1 4e-61 37% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0034 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0034 ref|YP_752172.1| major facilitator superfamily MFS_1 [Shewanella frigidimarina... NCIMB 400] gb|ABI73333.1| major facilitator superfamily MFS_1 [Shewanella frigidimarina NCIMB 400] YP_752172.1 2e-55 45% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-07-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0040 ref|ZP_01687956.1| mate efflux family protein [Microscilla marina... ATCC 23134] gb|EAY31163.1| mate efflux family protein [Microscilla marina ATCC 23134] ZP_01687956.1 2e-50 42% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-04-0034 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0034 ref|NP_001040306.1| presenilin-like signal peptide peptidase [Bom...byx mori] gb|ABD36167.1| presenilin-like signal peptide peptidase [Bombyx mori] NP_001040306.1 1e-135 65% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0035 ref|NP_937902.1| NADH dehydrogenase subunit 5 [Strongyloides stercoral...is] emb|CAD90562.1| NADH dehydrogenase subunit 5 [Strongyloides stercoralis] NP_937902.1 0.009 23% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-02-0015 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0015 gb|AAP56247.1| deltamethrin resistance-associated NYD-OP1 [Culex pipi...ens pallens] gb|AAP56248.1| deltamethrin resistance-associated NYD-OP2 [Culex pipiens pallens] AAP56247.1 0.0 86% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-02-0007 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0007 gb|AAP56249.1| deltamethrin resistance-associated NYD-OP3 [Culex pipi...ens pallens] gb|AAP56250.1| deltamethrin resistance-associated NYD-OP4 [Culex pipiens pallens] AAP56249.1 0.0 86% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-02-0008 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0008 gb|AAP56249.1| deltamethrin resistance-associated NYD-OP3 [Culex pipi...ens pallens] gb|AAP56250.1| deltamethrin resistance-associated NYD-OP4 [Culex pipiens pallens] AAP56249.1 0.0 86% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-02-0015 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0015 gb|AAP56249.1| deltamethrin resistance-associated NYD-OP3 [Culex pipi...ens pallens] gb|AAP56250.1| deltamethrin resistance-associated NYD-OP4 [Culex pipiens pallens] AAP56249.1 0.0 86% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-02-0008 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0008 gb|AAP56247.1| deltamethrin resistance-associated NYD-OP1 [Culex pipi...ens pallens] gb|AAP56248.1| deltamethrin resistance-associated NYD-OP2 [Culex pipiens pallens] AAP56247.1 0.0 86% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-02-0005 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0005 gb|AAP56249.1| deltamethrin resistance-associated NYD-OP3 [Culex pipi...ens pallens] gb|AAP56250.1| deltamethrin resistance-associated NYD-OP4 [Culex pipiens pallens] AAP56249.1 1e-178 81% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-02-0007 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0007 gb|AAP56247.1| deltamethrin resistance-associated NYD-OP1 [Culex pipi...ens pallens] gb|AAP56248.1| deltamethrin resistance-associated NYD-OP2 [Culex pipiens pallens] AAP56247.1 0.0 86% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-01-0080 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0080 ref|ZP_01467057.1| conserved hypothetical protein [Stigmatella au...rantiaca DW4/3-1] gb|EAU62171.1| conserved hypothetical protein [Stigmatella aurantiaca DW4/3-1] ZP_01467057.1 4e-06 30% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-04-0115 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available as] gb|ABD45587.1| thymidylate synthase, flavin-dependent [Ehrlichia chaffeensis str. Arkansas] YP_507138.1 2.8 29% ... ...CBRC-AGAM-04-0115 ref|YP_507138.1| thymidylate synthase, flavin-dependent [Ehrlichia chaffeensis str. Arkans

  12. NCBI nr-aa BLAST: CBRC-AGAM-07-0041 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0041 ref|ZP_01120431.1| hypothetical protein RB2501_08655 [Robiginital...ea biformata HTCC2501] gb|EAR16959.1| hypothetical protein RB2501_08655 [Robiginitalea biformata HTCC2501] ZP_01120431.1 4e-31 32% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0038 ref|ZP_01121627.1| hypothetical protein RB2501_11202 [Robiginital...ea biformata HTCC2501] gb|EAR14889.1| hypothetical protein RB2501_11202 [Robiginitalea biformata HTCC2501] ZP_01121627.1 1e-62 41% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-03-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0028 ref|XP_001652261.1| transient receptor potential cation channel p...rotein painless [Aedes aegypti] gb|EAT41530.1| transient receptor potential cation channel protein painless [Aedes aegypti] XP_001652261.1 1e-102 38% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-03-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0028 ref|XP_001652262.1| transient receptor potential cation channel p...rotein painless [Aedes aegypti] gb|EAT41531.1| transient receptor potential cation channel protein painless [Aedes aegypti] XP_001652262.1 1e-102 38% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-04-0024 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0024 ref|NP_001089174.1| putative transient receptor potential channel... [Xenopus laevis] emb|CAE09056.1| putative transient receptor potential channel [Xenopus laevis] NP_001089174.1 1e-158 42% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-05-0012 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0012 ref|YP_781556.1| filamentous haemagglutinin family outer membrane... protein [Rhodopseudomonas palustris BisA53] gb|ABJ06576.1| filamentous haemagglutinin family outer membrane protein [Rhodopseudomonas palustris BisA53] YP_781556.1 0.001 26% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-02-0179 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0179 ref|NP_542440.1| bride of sevenless CG8285-PA [Drosophila melanog...aster] sp|P22815|BOSS_DROME Protein bride of sevenless precursor emb|CAA39373.1| bride of sevenless protein

  19. NCBI nr-aa BLAST: CBRC-AGAM-02-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0071 ref|NP_001035057.1| pteropsin [Apis mellifera] tpg|DAA05735.1| TP...A_exp: pteropsin [Apis mellifera] tpg|DAA05736.1| TPA_exp: pteropsin [Apis mellifera] NP_001035057.1 3e-73 46% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-07-0041 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0041 ref|YP_863633.1| prenyltransferase family protein [Gramella forse...tii KT0803] emb|CAL68566.1| prenyltransferase family protein [Gramella forsetii KT0803] YP_863633.1 8e-30 31% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|YP_862949.1| copper-translocating P-type ATPase [Gramella for...setii KT0803] emb|CAL67882.1| copper-translocating P-type ATPase [Gramella forsetii KT0803] YP_862949.1 1e-135 66% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-07-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0040 ref|YP_860564.1| NorM-like multidrug efflux protein [Gramella for...setii KT0803] emb|CAL65497.1| NorM-like multidrug efflux protein [Gramella forsetii KT0803] YP_860564.1 3e-53 43% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|YP_956869.1| conserved hypothetical protein, membrane [Gramel...la forsetii KT0803] emb|CAL65134.1| conserved hypothetical protein, membrane [Gramella forsetii KT0803] YP_956869.1 3e-65 41% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|YP_861225.1| copper-translocating P-type ATPase [Gramella for...setii KT0803] emb|CAL66158.1| copper-translocating P-type ATPase [Gramella forsetii KT0803] YP_861225.1 1e-132 64% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|YP_861383.1| copper-translocating P-type ATPase [Gramella for...setii KT0803] emb|CAL66316.1| copper-translocating P-type ATPase [Gramella forsetii KT0803] YP_861383.1 1e-129 62% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-01-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0038 ref|NP_938793.1| Putative cytochrome C biogenesis protein [Corynebacterium diphtheria...e NCTC 13129] emb|CAE48916.1| Putative cytochrome C biogenesis protein [Corynebacterium diphtheriae] NP_938793.1 1.6 24% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-02-0026 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0026 ref|NP_001076809.1| adipokinetic hormone receptor [Tribolium cast...aneum] gb|ABE02225.1| adipokinetic hormone receptor [Tribolium castaneum] gb|ABN79650.1| adipokinetic hormone receptor [Tribolium castaneum] NP_001076809.1 5e-71 44% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-01-0097 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0097 ref|ZP_01647447.1| oxidoreductase-like [Salinispora arenicola CNS...205] gb|ABV97571.1| oxidoreductase domain protein [Salinispora arenicola CNS-205] ZP_01647447.1 0.71 29% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-07-0025 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0025 ref|YP_687144.1| hypothetical protein RRC32 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37818.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687144.1 2e-31 49% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0025 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0025 ref|YP_687145.1| hypothetical protein RRC34 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37819.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687145.1 7e-31 50% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0037 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0037 ref|YP_687144.1| hypothetical protein RRC32 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37818.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687144.1 2e-10 30% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-02-0057 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0057 ref|YP_687145.1| hypothetical protein RRC34 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37819.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687145.1 2e-10 33% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-02-0102 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0102 ref|YP_687144.1| hypothetical protein RRC32 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37818.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687144.1 7e-12 27% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-02-0057 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0057 ref|YP_687144.1| hypothetical protein RRC32 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37818.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687144.1 9e-11 35% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0037 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0037 ref|YP_687145.1| hypothetical protein RRC34 [uncultured methanogen...ic archaeon RC-I] emb|CAJ37819.1| hypothetical protein [uncultured methanogenic archaeon RC-I] YP_687145.1 5e-11 28% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-04-0125 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0125 ref|NP_525103.2| bunched CG5461-PA, isoform A [Drosophila melanog...aster] sp|Q24523|BUN2_DROME Protein bunched, class 2 isoform (Protein shortsighted) gb|AAF53201.2| CG5461-PA, isoform A [Drosophila melanogaster] NP_525103.2 6e-62 37% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-01-0058 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0058 ref|NP_525103.2| bunched CG5461-PA, isoform A [Drosophila melanog...aster] sp|Q24523|BUN2_DROME Protein bunched, class 2 isoform (Protein shortsighted) gb|AAF53201.2| CG5461-PA, isoform A [Drosophila melanogaster] NP_525103.2 2e-14 32% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0016 ref|ZP_01236824.1| putative multidrug resistance protein [Vibrio angus...tum S14] gb|EAS62911.1| putative multidrug resistance protein [Vibrio angustum S14] ZP_01236824.1 2e-53 56% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0003 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0003 ref|ZP_01235768.1| Putative cytochrome c-type biogenesis protein CcmF [Vibrio angus...tum S14] gb|EAS64028.1| Putative cytochrome c-type biogenesis protein CcmF [Vibrio angustum S14] ZP_01235768.1 9e-71 68% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-01-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0071 ref|NP_476722.1| shotgun CG3722-PA [Drosophila melanogaster] sp|Q...24298|CADE_DROME DE-cadherin precursor (Protein shotgun) gb|AAF46659.1| CG3722-PA [Drosophila melanogaster] NP_476722.1 0.0 40% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-01-0070 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0070 ref|NP_476722.1| shotgun CG3722-PA [Drosophila melanogaster] sp|Q...24298|CADE_DROME DE-cadherin precursor (Protein shotgun) gb|AAF46659.1| CG3722-PA [Drosophila melanogaster] NP_476722.1 0.0 42% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-07-0002 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0002 ref|YP_050752.1| hypothetical protein ECA2661 [Erwinia carotovora subsp. atroseptic...a SCRI1043] emb|CAG75561.1| putative membrane protein [Erwinia carotovora subsp. atroseptica SCRI1043] YP_050752.1 2e-66 53% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-04-0107 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0107 ref|YP_001112131.1| protein of unknown function UPF0118 [Desulfoto...maculum reducens MI-1] gb|ABO49306.1| protein of unknown function UPF0118 [Desulfotomaculum reducens MI-1] YP_001112131.1 1.8 23% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-07-0068 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0068 ref|YP_574435.1| flavin-containing monooxygenase FMO [Chromohalobacter salex...igens DSM 3043] gb|ABE59736.1| flavin-containing monooxygenase FMO [Chromohalobacter salexigens DSM 3043] YP_574435.1 7.1 30% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-04-0117 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0117 ref|YP_001314462.1| Monosaccharide-transporting ATPase [Sinorhizobium medica...e WSM419] gb|ABR64529.1| Monosaccharide-transporting ATPase [Sinorhizobium medicae WSM419] YP_001314462.1 2.4 30% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-02-0057 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0057 ref|XP_001526785.1| conserved hypothetical protein [Lodderomyces ...elongisporus NRRL YB-4239] gb|EDK43435.1| conserved hypothetical protein [Lodderomyces elongisporus NRRL YB-4239] XP_001526785.1 5e-07 29% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-02-0138 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0138 ref|XP_001645325.1| hypothetical protein Kpol_1058p4 [Vanderwalto...zyma polyspora DSM 70294] gb|EDO17467.1| hypothetical protein Kpol_1058p4 [Vanderwaltozyma polyspora DSM 70294] XP_001645325.1 9e-06 24% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-07-0008 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0008 ref|XP_001645325.1| hypothetical protein Kpol_1058p4 [Vanderwalto...zyma polyspora DSM 70294] gb|EDO17467.1| hypothetical protein Kpol_1058p4 [Vanderwaltozyma polyspora DSM 70294] XP_001645325.1 0.0 46% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-03-0043 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0043 ref|XP_001642520.1| hypothetical protein Kpol_325p1 [Vanderwaltoz...yma polyspora DSM 70294] gb|EDO14662.1| hypothetical protein Kpol_325p1 [Vanderwaltozyma polyspora DSM 70294] XP_001642520.1 1.3 25% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-03-0087 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0087 ref|XP_001643844.1| hypothetical protein Kpol_499p14 [Vanderwalto...zyma polyspora DSM 70294] gb|EDO15986.1| hypothetical protein Kpol_499p14 [Vanderwaltozyma polyspora DSM 70294] XP_001643844.1 4e-20 23% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-03-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0044 ref|XP_317840.2| candidate odorant receptor (AGAP011467-PA) [Anop...heles gambiae str. PEST] gb|EAA12959.2| candidate odorant receptor (AGAP011467-PA) [Anopheles gambiae str. PEST] XP_317840.2 0.0 91% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-04-0057 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0057 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 2e-33 26% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-04-0085 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0085 ref|XP_318760.1| candidate odorant receptor (AGAP009704-PA) [Anop...heles gambiae str. PEST] gb|EAA43487.1| candidate odorant receptor (AGAP009704-PA) [Anopheles gambiae str. PEST] XP_318760.1 2e-49 26% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-03-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0044 ref|XP_310087.2| candidate odorant receptor (AGAP009409-PA) [Anop...heles gambiae str. PEST] gb|EAA05741.2| candidate odorant receptor (AGAP009409-PA) [Anopheles gambiae str. PEST] XP_310087.2 2e-62 34% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-04-0063 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0063 ref|XP_310073.1| candidate odorant receptor (AGAP009398-PA) [Anop...heles gambiae str. PEST] gb|EAA05815.2| candidate odorant receptor (AGAP009398-PA) [Anopheles gambiae str. PEST] XP_310073.1 1e-81 40% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-04-0059 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0059 ref|XP_310060.1| candidate odorant receptor (AGAP009390-PA) [Anop...heles gambiae str. PEST] gb|EAA45274.1| candidate odorant receptor (AGAP009390-PA) [Anopheles gambiae str. PEST] XP_310060.1 2e-40 28% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-01-0066 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0066 ref|XP_316698.1| candidate odorant receptor (AGAP006667-PA) [Anop...heles gambiae str. PEST] gb|EAA11341.2| candidate odorant receptor (AGAP006667-PA) [Anopheles gambiae str. PEST] XP_316698.1 0.0 96% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-03-0068 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0068 ref|XP_320552.1| candidate odorant receptor (AGAP011979-PA) [Anop...heles gambiae str. PEST] gb|EAA43311.1| candidate odorant receptor (AGAP011979-PA) [Anopheles gambiae str. PEST] XP_320552.1 0.0 100% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-02-0108 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0108 ref|XP_319538.1| candidate odorant receptor (AGAP003310-PA) [Anop...heles gambiae str. PEST] gb|EAA14660.2| candidate odorant receptor (AGAP003310-PA) [Anopheles gambiae str. PEST] XP_319538.1 2e-14 23% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-02-0180 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0180 ref|XP_310907.1| candidate odorant receptor (AGAP000226-PA) [Anop...heles gambiae str. PEST] gb|EAA45138.1| candidate odorant receptor (AGAP000226-PA) [Anopheles gambiae str. PEST] XP_310907.1 2e-54 32% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-04-0088 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0088 ref|XP_318786.1| candidate odorant receptor (AGAP009718-PA) [Anop...heles gambiae str. PEST] gb|EAA43497.1| candidate odorant receptor (AGAP009718-PA) [Anopheles gambiae str. PEST] XP_318786.1 0.0 97% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0086 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0086 ref|XP_318795.1| candidate odorant receptor (AGAP009720-PA) [Anop...heles gambiae str. PEST] gb|EAA43501.1| candidate odorant receptor (AGAP009720-PA) [Anopheles gambiae str. PEST] XP_318795.1 1e-153 66% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-02-0053 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0053 ref|XP_320552.1| candidate odorant receptor (AGAP011979-PA) [Anop...heles gambiae str. PEST] gb|EAA43311.1| candidate odorant receptor (AGAP011979-PA) [Anopheles gambiae str. PEST] XP_320552.1 8e-09 20% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-05-0015 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0015 ref|XP_313200.1| candidate odorant receptor (AGAP004278-PA) [Anop...heles gambiae str. PEST] gb|EAA44759.1| candidate odorant receptor (AGAP004278-PA) [Anopheles gambiae str. PEST] XP_313200.1 2e-54 32% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-04-0061 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0061 ref|XP_310070.2| candidate odorant receptor (AGAP009396-PA) [Anop...heles gambiae str. PEST] gb|EAA05818.3| candidate odorant receptor (AGAP009396-PA) [Anopheles gambiae str. PEST] XP_310070.2 0.0 79% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-04-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0064 ref|XP_317837.3| candidate odorant receptor (AGAP011469-PA) [Anop...heles gambiae str. PEST] gb|EAA13074.3| candidate odorant receptor (AGAP011469-PA) [Anopheles gambiae str. PEST] XP_317837.3 8e-64 34% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-04-0058 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0058 ref|XP_310066.3| candidate odorant receptor (AGAP009394-PA) [Anop...heles gambiae str. PEST] gb|EAA05765.4| candidate odorant receptor (AGAP009394-PA) [Anopheles gambiae str. PEST] XP_310066.3 5e-45 27% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-04-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0064 ref|XP_310070.2| candidate odorant receptor (AGAP009396-PA) [Anop...heles gambiae str. PEST] gb|EAA05818.3| candidate odorant receptor (AGAP009396-PA) [Anopheles gambiae str. PEST] XP_310070.2 1e-180 80% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-01-0010 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0010 ref|XP_315068.4| candidate odorant receptor (AGAP004971-PA) [Anop...heles gambiae str. PEST] gb|EAA10393.5| candidate odorant receptor (AGAP004971-PA) [Anopheles gambiae str. PEST] XP_315068.4 1e-81 41% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-04-0008 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0008 ref|XP_311815.3| candidate odorant receptor (AGAP003054-PA) [Anop...heles gambiae str. PEST] gb|EAA44825.3| candidate odorant receptor (AGAP003054-PA) [Anopheles gambiae str. PEST] XP_311815.3 5e-15 21% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-04-0092 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0092 ref|XP_318789.1| candidate odorant receptor (AGAP009719-PA) [Anop...heles gambiae str. PEST] gb|EAA43498.1| candidate odorant receptor (AGAP009719-PA) [Anopheles gambiae str. PEST] XP_318789.1 0.0 83% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-04-0070 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0070 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 1e-08 35% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-02-0183 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0183 ref|XP_313640.1| candidate odorant receptor (AGAP004357-PA) [Anop...heles gambiae str. PEST] gb|EAA09108.2| candidate odorant receptor (AGAP004357-PA) [Anopheles gambiae str. PEST] XP_313640.1 0.0 94% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-02-0051 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0051 ref|XP_321007.1| candidate odorant receptor (AGAP002044-PA) [Anop...heles gambiae str. PEST] gb|EAA43054.1| candidate odorant receptor (AGAP002044-PA) [Anopheles gambiae str. PEST] XP_321007.1 0.0 100% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-03-0070 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0070 ref|XP_310903.1| candidate odorant receptor (AGAP000230-PA) [Anop...heles gambiae str. PEST] gb|EAA45137.1| candidate odorant receptor (AGAP000230-PA) [Anopheles gambiae str. PEST] XP_310903.1 1e-16 20% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-02-0105 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0105 ref|XP_320543.1| candidate odorant receptor (AGAP011989-PA) [Anop...heles gambiae str. PEST] gb|EAA43309.1| candidate odorant receptor (AGAP011989-PA) [Anopheles gambiae str. PEST] XP_320543.1 1e-23 28% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-04-0075 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0075 ref|XP_310172.2| candidate odorant receptor (AGAP009520-PA) [Anop...heles gambiae str. PEST] gb|EAA05925.2| candidate odorant receptor (AGAP009520-PA) [Anopheles gambiae str. PEST] XP_310172.2 1e-116 55% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-04-0084 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0084 ref|XP_318760.1| candidate odorant receptor (AGAP009704-PA) [Anop...heles gambiae str. PEST] gb|EAA43487.1| candidate odorant receptor (AGAP009704-PA) [Anopheles gambiae str. PEST] XP_318760.1 0.0 100% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-03-0067 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0067 ref|XP_320541.1| candidate odorant receptor (AGAP011991-PA) [Anop...heles gambiae str. PEST] gb|EAA43307.1| candidate odorant receptor (AGAP011991-PA) [Anopheles gambiae str. PEST] XP_320541.1 6e-87 41% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-04-0061 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0061 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 1e-72 38% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-04-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0071 ref|XP_001230709.1| candidate odorant receptor (AGAP009412-PA) [A...nopheles gambiae str. PEST] gb|EAU77441.1| candidate odorant receptor (AGAP009412-PA) [Anopheles gambiae str. PEST] XP_001230709.1 1e-154 66% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0065 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0065 ref|XP_310070.2| candidate odorant receptor (AGAP009396-PA) [Anop...heles gambiae str. PEST] gb|EAA05818.3| candidate odorant receptor (AGAP009396-PA) [Anopheles gambiae str. PEST] XP_310070.2 2e-57 32% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-04-0068 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0068 ref|XP_001230709.1| candidate odorant receptor (AGAP009412-PA) [A...nopheles gambiae str. PEST] gb|EAU77441.1| candidate odorant receptor (AGAP009412-PA) [Anopheles gambiae str. PEST] XP_001230709.1 1e-154 67% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-02-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0052 ref|XP_309205.1| candidate odorant receptor (AGAP001012-PA) [Anop...heles gambiae str. PEST] gb|EAA45379.1| candidate odorant receptor (AGAP001012-PA) [Anopheles gambiae str. PEST] XP_309205.1 5e-08 20% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-07-0069 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0069 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 2e-65 34% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-04-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0071 ref|XP_310066.3| candidate odorant receptor (AGAP009394-PA) [Anop...heles gambiae str. PEST] gb|EAA05765.4| candidate odorant receptor (AGAP009394-PA) [Anopheles gambiae str. PEST] XP_310066.3 5e-43 31% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-02-0133 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0133 ref|XP_318763.1| candidate odorant receptor (AGAP009706-PA) [Anop...heles gambiae str. PEST] gb|EAA43490.1| candidate odorant receptor (AGAP009706-PA) [Anopheles gambiae str. PEST] XP_318763.1 2e-34 23% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-04-0084 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0084 ref|XP_318789.1| candidate odorant receptor (AGAP009719-PA) [Anop...heles gambiae str. PEST] gb|EAA43498.1| candidate odorant receptor (AGAP009719-PA) [Anopheles gambiae str. PEST] XP_318789.1 4e-50 29% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-01-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0044 ref|XP_316227.4| candidate olfactory receptor (AGAP006167-PA) [An...opheles gambiae str. PEST] gb|EAA44173.4| candidate olfactory receptor (AGAP006167-PA) [Anopheles gambiae str. PEST] XP_316227.4 0.0 98% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-04-0062 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0062 ref|XP_310072.3| candidate odorant receptor (AGAP009397-PA) [Anop...heles gambiae str. PEST] gb|EAA45281.3| candidate odorant receptor (AGAP009397-PA) [Anopheles gambiae str. PEST] XP_310072.3 4e-82 41% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-04-0087 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0087 ref|XP_318761.1| candidate odorant receptor (AGAP009705-PA) [Anop...heles gambiae str. PEST] gb|EAA43488.1| candidate odorant receptor (AGAP009705-PA) [Anopheles gambiae str. PEST] XP_318761.1 1e-134 59% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-04-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0038 ref|XP_319640.1| candidate odorant receptor (AGAP008894-PA) [Anop...heles gambiae str. PEST] gb|EAA15093.2| candidate odorant receptor (AGAP008894-PA) [Anopheles gambiae str. PEST] XP_319640.1 0.0 97% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-03-0062 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0062 ref|XP_317710.1| candidate odorant receptor (AGAP007797-PA) [Anop...heles gambiae str. PEST] gb|EAA43966.1| candidate odorant receptor (AGAP007797-PA) [Anopheles gambiae str. PEST] XP_317710.1 9e-30 29% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-03-0069 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0069 ref|XP_320543.1| candidate odorant receptor (AGAP011989-PA) [Anop...heles gambiae str. PEST] gb|EAA43309.1| candidate odorant receptor (AGAP011989-PA) [Anopheles gambiae str. PEST] XP_320543.1 0.0 100% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-04-0092 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0092 ref|XP_318761.1| candidate odorant receptor (AGAP009705-PA) [Anop...heles gambiae str. PEST] gb|EAA43488.1| candidate odorant receptor (AGAP009705-PA) [Anopheles gambiae str. PEST] XP_318761.1 1e-130 59% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-01-0033 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0033 ref|XP_001688726.1| candidate odorant receptor (AGAP005760-PA) [A...nopheles gambiae str. PEST] gb|EDO63732.1| candidate odorant receptor (AGAP005760-PA) [Anopheles gambiae str. PEST] XP_001688726.1 0.0 100% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-02-0082 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0082 ref|XP_312289.1| candidate odorant receptor (AGAP002639-PA) [Anop...heles gambiae str. PEST] gb|EAA07551.2| candidate odorant receptor (AGAP002639-PA) [Anopheles gambiae str. PEST] XP_312289.1 0.0 100% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-05-0015 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0015 ref|XP_314478.1| candidate odorant receptor (AGAP010505-PA) [Anop...heles gambiae str. PEST] gb|EAA44436.1| candidate odorant receptor (AGAP010505-PA) [Anopheles gambiae str. PEST] XP_314478.1 8e-22 29% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-05-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0016 ref|XP_320542.1| candidate odorant receptor (AGAP011990-PA) [Anop...heles gambiae str. PEST] gb|EAA43308.1| candidate odorant receptor (AGAP011990-PA) [Anopheles gambiae str. PEST] XP_320542.1 1e-16 20% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-04-0057 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0057 ref|XP_310062.1| candidate odorant receptor (AGAP009392-PA) [Anop...heles gambiae str. PEST] gb|EAA45278.1| candidate odorant receptor (AGAP009392-PA) [Anopheles gambiae str. PEST] XP_310062.1 2e-35 28% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-04-0056 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0056 ref|XP_310060.1| candidate odorant receptor (AGAP009390-PA) [Anop...heles gambiae str. PEST] gb|EAA45274.1| candidate odorant receptor (AGAP009390-PA) [Anopheles gambiae str. PEST] XP_310060.1 0.0 97% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0064 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 1e-74 41% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-04-0069 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0069 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 3e-53 31% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-07-0011 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0011 ref|XP_001238412.1| candidate odorant receptor (AGAP002046-PB) [A...nopheles gambiae str. PEST] gb|EAU75581.1| candidate odorant receptor (AGAP002046-PB) [Anopheles gambiae str. PEST] XP_001238412.1 0.0 96% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-04-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0060 ref|XP_310068.1| candidate odorant receptor (AGAP009395-PA) [Anop...heles gambiae str. PEST] gb|EAA05735.2| candidate odorant receptor (AGAP009395-PA) [Anopheles gambiae str. PEST] XP_310068.1 1e-106 49% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-04-0060 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0060 ref|XP_310073.1| candidate odorant receptor (AGAP009398-PA) [Anop...heles gambiae str. PEST] gb|EAA05815.2| candidate odorant receptor (AGAP009398-PA) [Anopheles gambiae str. PEST] XP_310073.1 1e-109 49% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-03-0046 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0046 ref|XP_310073.1| candidate odorant receptor (AGAP009398-PA) [Anop...heles gambiae str. PEST] gb|EAA05815.2| candidate odorant receptor (AGAP009398-PA) [Anopheles gambiae str. PEST] XP_310073.1 2e-65 34% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-05-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0016 ref|XP_309205.1| candidate odorant receptor (AGAP001012-PA) [Anop...heles gambiae str. PEST] gb|EAA45379.1| candidate odorant receptor (AGAP001012-PA) [Anopheles gambiae str. PEST] XP_309205.1 7e-32 27% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-02-0053 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0053 ref|XP_001238412.1| candidate odorant receptor (AGAP002046-PB) [A...nopheles gambiae str. PEST] gb|EAU75581.1| candidate odorant receptor (AGAP002046-PB) [Anopheles gambiae str. PEST] XP_001238412.1 0.0 97% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-03-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0071 ref|XP_310903.1| candidate odorant receptor (AGAP000230-PA) [Anop...heles gambiae str. PEST] gb|EAA45137.1| candidate odorant receptor (AGAP000230-PA) [Anopheles gambiae str. PEST] XP_310903.1 1e-20 22% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-02-0053 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0053 ref|XP_321005.1| candidate odorant receptor (AGAP002046-PA) [Anop...heles gambiae str. PEST] gb|EAA43053.1| candidate odorant receptor (AGAP002046-PA) [Anopheles gambiae str. PEST] XP_321005.1 0.0 100% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-03-0053 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0053 ref|XP_310172.2| candidate odorant receptor (AGAP009520-PA) [Anop...heles gambiae str. PEST] gb|EAA05925.2| candidate odorant receptor (AGAP009520-PA) [Anopheles gambiae str. PEST] XP_310172.2 7e-25 21% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0011 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0011 ref|XP_320553.1| candidate odorant receptor (AGAP011978-PA) [Anop...heles gambiae str. PEST] gb|EAA43312.1| candidate odorant receptor (AGAP011978-PA) [Anopheles gambiae str. PEST] XP_320553.1 2e-10 21% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-04-0065 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0065 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 2e-70 33% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-03-0068 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0068 ref|XP_320542.1| candidate odorant receptor (AGAP011990-PA) [Anop...heles gambiae str. PEST] gb|EAA43308.1| candidate odorant receptor (AGAP011990-PA) [Anopheles gambiae str. PEST] XP_320542.1 1e-27 24% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-02-0055 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0055 ref|XP_320910.3| candidate odorant receptor (AGAP002125-PA) [Anop...heles gambiae str. PEST] gb|EAA01535.4| candidate odorant receptor (AGAP002125-PA) [Anopheles gambiae str. PEST] XP_320910.3 0.0 100% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-01-0065 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0065 ref|XP_310064.1| candidate odorant receptor (AGAP009393-PA) [Anop...heles gambiae str. PEST] gb|EAA05812.2| candidate odorant receptor (AGAP009393-PA) [Anopheles gambiae str. PEST] XP_310064.1 3e-38 27% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-02-0133 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0133 ref|XP_318786.1| candidate odorant receptor (AGAP009718-PA) [Anop...heles gambiae str. PEST] gb|EAA43497.1| candidate odorant receptor (AGAP009718-PA) [Anopheles gambiae str. PEST] XP_318786.1 2e-38 24% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0011 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0011 ref|XP_311894.1| candidate odorant receptor (AGAP002995-PA) [Anop...heles gambiae str. PEST] gb|EAA44843.1| candidate odorant receptor (AGAP002995-PA) [Anopheles gambiae str. PEST] XP_311894.1 1e-06 24% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-04-0066 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0066 ref|XP_310085.1| candidate odorant receptor (AGAP009408-PA) [Anop...heles gambiae str. PEST] gb|EAA05771.2| candidate odorant receptor (AGAP009408-PA) [Anopheles gambiae str. PEST] XP_310085.1 1e-175 76% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-04-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0045 ref|XP_310060.1| candidate odorant receptor (AGAP009390-PA) [Anop...heles gambiae str. PEST] gb|EAA45274.1| candidate odorant receptor (AGAP009390-PA) [Anopheles gambiae str. PEST] XP_310060.1 1e-130 59% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0085 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0085 ref|XP_318786.1| candidate odorant receptor (AGAP009718-PA) [Anop...heles gambiae str. PEST] gb|EAA43497.1| candidate odorant receptor (AGAP009718-PA) [Anopheles gambiae str. PEST] XP_318786.1 1e-140 61% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-05-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0016 ref|XP_320541.1| candidate odorant receptor (AGAP011991-PA) [Anop...heles gambiae str. PEST] gb|EAA43307.1| candidate odorant receptor (AGAP011991-PA) [Anopheles gambiae str. PEST] XP_320541.1 1e-20 22% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-03-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0045 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 4e-47 29% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-04-0069 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0069 ref|XP_310090.1| candidate odorant receptor (AGAP009412-PB) [Anop...heles gambiae str. PEST] gb|EAA45285.1| candidate odorant receptor (AGAP009412-PB) [Anopheles gambiae str. PEST] XP_310090.1 0.0 100% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-04-0059 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0059 ref|XP_310062.1| candidate odorant receptor (AGAP009392-PA) [Anop...heles gambiae str. PEST] gb|EAA45278.1| candidate odorant receptor (AGAP009392-PA) [Anopheles gambiae str. PEST] XP_310062.1 0.0 90% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-01-0012 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0012 ref|XP_315068.4| candidate odorant receptor (AGAP004971-PA) [Anop...heles gambiae str. PEST] gb|EAA10393.5| candidate odorant receptor (AGAP004971-PA) [Anopheles gambiae str. PEST] XP_315068.4 1e-50 29% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-02-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0052 ref|XP_321005.1| candidate odorant receptor (AGAP002046-PA) [Anop...heles gambiae str. PEST] gb|EAA43053.1| candidate odorant receptor (AGAP002046-PA) [Anopheles gambiae str. PEST] XP_321005.1 0.0 96% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-02-0181 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0181 ref|XP_313640.1| candidate odorant receptor (AGAP004357-PA) [Anop...heles gambiae str. PEST] gb|EAA09108.2| candidate odorant receptor (AGAP004357-PA) [Anopheles gambiae str. PEST] XP_313640.1 1e-176 75% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-01-0066 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0066 ref|XP_317837.3| candidate odorant receptor (AGAP011469-PA) [Anop...heles gambiae str. PEST] gb|EAA13074.3| candidate odorant receptor (AGAP011469-PA) [Anopheles gambiae str. PEST] XP_317837.3 4e-45 29% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-02-0184 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0184 ref|XP_313638.1| candidate odorant receptor (AGAP004356-PA) [Anop...heles gambiae str. PEST] gb|EAA44571.1| candidate odorant receptor (AGAP004356-PA) [Anopheles gambiae str. PEST] XP_313638.1 0.0 94% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-03-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0044 ref|XP_310088.1| candidate odorant receptor (AGAP009410-PA) [Anop...heles gambiae str. PEST] gb|EAA05802.1| candidate odorant receptor (AGAP009410-PA) [Anopheles gambiae str. PEST] XP_310088.1 2e-77 38% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-04-0008 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0008 ref|XP_310907.1| candidate odorant receptor (AGAP000226-PA) [Anop...heles gambiae str. PEST] gb|EAA45138.1| candidate odorant receptor (AGAP000226-PA) [Anopheles gambiae str. PEST] XP_310907.1 3e-12 22% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-04-0068 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0068 ref|XP_317837.3| candidate odorant receptor (AGAP011469-PA) [Anop...heles gambiae str. PEST] gb|EAA13074.3| candidate odorant receptor (AGAP011469-PA) [Anopheles gambiae str. PEST] XP_317837.3 2e-54 31% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-03-0071 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0071 ref|XP_320541.1| candidate odorant receptor (AGAP011991-PA) [Anop...heles gambiae str. PEST] gb|EAA43307.1| candidate odorant receptor (AGAP011991-PA) [Anopheles gambiae str. PEST] XP_320541.1 0.0 100% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-04-0061 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0061 ref|XP_310087.2| candidate odorant receptor (AGAP009409-PA) [Anop...heles gambiae str. PEST] gb|EAA05741.2| candidate odorant receptor (AGAP009409-PA) [Anopheles gambiae str. PEST] XP_310087.2 1e-57 33% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-01-0011 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0011 ref|XP_315072.4| candidate odorant receptor (AGAP004974-PA) [Anop...heles gambiae str. PEST] gb|EAA10399.5| candidate odorant receptor (AGAP004974-PA) [Anopheles gambiae str. PEST] XP_315072.4 1e-50 29% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-04-0089 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0089 ref|XP_318795.1| candidate odorant receptor (AGAP009720-PA) [Anop...heles gambiae str. PEST] gb|EAA43501.1| candidate odorant receptor (AGAP009720-PA) [Anopheles gambiae str. PEST] XP_318795.1 0.0 83% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-02-0182 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0182 ref|XP_313640.1| candidate odorant receptor (AGAP004357-PA) [Anop...heles gambiae str. PEST] gb|EAA09108.2| candidate odorant receptor (AGAP004357-PA) [Anopheles gambiae str. PEST] XP_313640.1 1e-156 71% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-02-0088 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0088 ref|XP_312203.1| candidate odorant receptor (AGAP002722-PA) [Anop...heles gambiae str. PEST] gb|EAA44886.1| candidate odorant receptor (AGAP002722-PA) [Anopheles gambiae str. PEST] XP_312203.1 0.0 95% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-02-0108 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0108 ref|XP_311815.3| candidate odorant receptor (AGAP003054-PA) [Anop...heles gambiae str. PEST] gb|EAA44825.3| candidate odorant receptor (AGAP003054-PA) [Anopheles gambiae str. PEST] XP_311815.3 3e-45 30% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-02-0020 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0020 ref|NP_822673.1| integral membrane protein [Streptomyces avermitil...is MA-4680] dbj|BAC69208.1| putative integral membrane protein [Streptomyces avermitilis MA-4680] NP_822673.1 2e-04 24% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-01-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0040 ref|XP_001653866.1| ultraviolet-sensitive opsin [Aedes aegypti] gb|ABF18478.1| ultraviolet...-sensitive opsin [Aedes aegypti] gb|EAT38511.1| ultraviolet-sensitive opsin [Aedes aegypti] XP_001653866.1 1e-178 80% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-01-0100 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0100 ref|XP_001653866.1| ultraviolet-sensitive opsin [Aedes aegypti] gb|ABF18478.1| ultraviolet...-sensitive opsin [Aedes aegypti] gb|EAT38511.1| ultraviolet-sensitive opsin [Aedes aegypti] XP_001653866.1 2e-60 37% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-01-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0040 ref|NP_001011605.1| ultraviolet-sensitive opsin [Apis mellifera] ...sp|O61303|OPSUV_APIME Opsin, ultraviolet-sensitive (AMUVOP) (BUVOPS) gb|AAC13418.1| ultraviolet-sensitive op

  6. NCBI nr-aa BLAST: CBRC-AGAM-03-0026 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0026 ref|XP_001659082.1| substance P receptor (long form), putative [A...edes aegypti] gb|EAT39962.1| substance P receptor (long form), putative [Aedes aegypti] XP_001659082.1 6e-92 51% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-03-0077 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0077 ref|XP_001659082.1| substance P receptor (long form), putative [A...edes aegypti] gb|EAT39962.1| substance P receptor (long form), putative [Aedes aegypti] XP_001659082.1 1e-141 74% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-03-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0035 ref|YP_441277.1| hypothetical protein BTH_I0721 [Burkholderia thail...andensis E264] gb|ABC36322.1| membrane protein, putative [Burkholderia thailandensis E264] YP_441277.1 0.063 24% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-07-0061 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0061 ref|YP_443145.1| fosmidomycin resistance protein [Burkholderia thail...andensis E264] gb|ABC36646.1| fosmidomycin resistance protein [Burkholderia thailandensis E264] YP_443145.1 1e-117 64% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-02-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0044 ref|YP_001012824.1| Leucyl aminopeptidase, AmpS [Hyperthermus but...ylicus DSM 5456] gb|ABM80479.1| Leucyl aminopeptidase, AmpS [Hyperthermus butylicus DSM 5456] YP_001012824.1 0.37 29% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-02-0168 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0168 ref|XP_001523004.1| hypothetical protein MGCH7_ch7g1090 [Magnaporth...e grisea 70-15] gb|EAQ71683.1| hypothetical protein MGCH7_ch7g1090 [Magnaporthe grisea 70-15] XP_001523004.1 0.59 29% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-04-0104 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0104 ref|XP_001404421.1| hypothetical protein MGG_13192 [Magnaporthe g...risea 70-15] gb|EDJ96181.1| hypothetical protein MGG_13192 [Magnaporthe grisea 70-15] XP_001404421.1 0.083 31% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-02-0176 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0176 ref|ZP_01771797.1| Hypothetical protein COLAER_00786 [Collinsella... aerofaciens ATCC 25986] gb|EBA40261.1| Hypothetical protein COLAER_00786 [Collinsella aerofaciens ATCC 25986] ZP_01771797.1 0.14 26% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-01-0088 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0088 sp|P82149|NT53_DROME Lethal(2)neighbour of tid protein 2 (NOT53) ...emb|CAA64533.1| Not53 protein [Drosophila melanogaster] emb|CAA71168.1| lethal(2)neighbour of tid [Drosophila melanogaster] P82149 1e-123 61% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-02-0141 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0141 ref|ZP_01444683.1| tail fiber protein, putative [Roseovarius sp. ...HTCC2601] gb|EAU45064.1| tail fiber protein, putative [Roseovarius sp. HTCC2601] ZP_01444683.1 0.038 24% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-05-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0028 ref|NP_309677.1| putative tail fiber protein [Escherichia coli O1...57:H7 str. Sakai] dbj|BAB35073.1| putative tail fiber protein [Escherichia coli O157:H7 str. Sakai] NP_309677.1 3e-18 34% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|ZP_01117607.1| hypothetical protein PI23P_05432 [Polaribacter... irgensii 23-P] gb|EAR13914.1| hypothetical protein PI23P_05432 [Polaribacter irgensii 23-P] ZP_01117607.1 7e-71 44% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0017 ref|ZP_01119288.1| CAAX amino terminal protease family protein [Polar...ibacter irgensii 23-P] gb|EAR11475.1| CAAX amino terminal protease family protein [Polaribacter irgensii 23-P] ZP_01119288.1 3e-13 27% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-03-0074 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0074 ref|XP_567974.1| hypothetical protein [Cryptococcus neoformans var. neo...formans JEC21] gb|AAW46457.1| conserved hypothetical protein [Cryptococcus neoformans var. neoformans JEC21] XP_567974.1 5e-09 58% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-04-0003 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0003 ref|XP_001650244.1| sodium-dependent excitatory amino acid transp...orter [Aedes aegypti] gb|EAT48229.1| sodium-dependent excitatory amino acid transporter [Aedes aegypti] XP_001650244.1 1e-111 77% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0056 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0056 ref|YP_610695.1| putrescine ABC transporter, permease protein [Ps...eudomonas entomophila L48] emb|CAK17912.1| putrescine ABC transporter, permease protein [Pseudomonas] YP_610695.1 1e-130 84% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-07-0056 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0056 ref|YP_001170621.1| putrescine ABC transporter, permease protein ...[Pseudomonas stutzeri A1501] gb|ABP77779.1| putrescine ABC transporter, permease protein [Pseudomonas stutzeri A1501] YP_001170621.1 1e-128 86% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-02-0039 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0039 ref|XP_001589660.1| predicted protein [Sclerotinia sclerotiorum 1...980] gb|EDN93515.1| predicted protein [Sclerotinia sclerotiorum 1980] XP_001589660.1 2e-38 75% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-02-0112 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0112 ref|XP_001596058.1| predicted protein [Sclerotinia sclerotiorum 1...980] gb|EDN99420.1| predicted protein [Sclerotinia sclerotiorum 1980] XP_001596058.1 2e-21 82% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-01-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0035 ref|XP_001589660.1| predicted protein [Sclerotinia sclerotiorum 1...980] gb|EDN93515.1| predicted protein [Sclerotinia sclerotiorum 1980] XP_001589660.1 1e-42 90% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-05-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0052 ref|XP_001589660.1| predicted protein [Sclerotinia sclerotiorum 1...980] gb|EDN93515.1| predicted protein [Sclerotinia sclerotiorum 1980] XP_001589660.1 6e-51 91% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-03-0029 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0029 ref|XP_001598793.1| predicted protein [Sclerotinia sclerotiorum 1...980] gb|EDN91479.1| predicted protein [Sclerotinia sclerotiorum 1980] XP_001598793.1 2e-41 88% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-03-0029 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0029 ref|XP_001589660.1| predicted protein [Sclerotinia sclerotiorum 1...980] gb|EDN93515.1| predicted protein [Sclerotinia sclerotiorum 1980] XP_001589660.1 3e-41 97% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-03-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0035 ref|ZP_02007270.1| protein of unknown function DUF6, transmembrane [Ralstonia pick...ettii 12D] gb|EDN41481.1| protein of unknown function DUF6, transmembrane [Ralstonia pickettii 12D] ZP_02007270.1 0.063 23% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-01-0072 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0072 ref|YP_001034807.1| Platelet-binding glycoprotein [Streptococcus ...sanguinis SK36] gb|ABN44257.1| Platelet-binding glycoprotein [Streptococcus sanguinis SK36] YP_001034807.1 4e-59 30% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0027 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0027 ref|YP_001034807.1| Platelet-binding glycoprotein [Streptococcus ...sanguinis SK36] gb|ABN44257.1| Platelet-binding glycoprotein [Streptococcus sanguinis SK36] YP_001034807.1 0.001 26% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-02-0102 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0102 ref|YP_001034807.1| Platelet-binding glycoprotein [Streptococcus ...sanguinis SK36] gb|ABN44257.1| Platelet-binding glycoprotein [Streptococcus sanguinis SK36] YP_001034807.1 1e-11 24% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0063 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0063 ref|YP_001034807.1| Platelet-binding glycoprotein [Streptococcus ...sanguinis SK36] gb|ABN44257.1| Platelet-binding glycoprotein [Streptococcus sanguinis SK36] YP_001034807.1 2e-30 23% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-01-0005 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0005 ref|NP_724962.2| starry night CG11895-PA [Drosophila melanogaster...] sp|Q9V5N8|STAN_DROME Protocadherin-like wing polarity protein stan precursor (Protein starry night) (Prote

  15. NCBI nr-aa BLAST: CBRC-AGAM-02-0119 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0119 ref|ZP_00982898.1| COG0477: Permeases of the major facilitator su...perfamily [Burkholderia dolosa AUO158] gb|EAY72007.1| Major facilitator superfamily (MFS_1) transporter [Burkholderia dolosa AUO158] ZP_00982898.1 0.36 24% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-05-0012 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0012 ref|YP_001363161.1| DNA internalization-related competence protei...n ComEC/Rec2 [Kineococcus radiotolerans SRS30216] gb|ABS04897.1| DNA internalization-related competence prot

  17. NCBI nr-aa BLAST: CBRC-AGAM-01-0054 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0054 ref|YP_001165892.1| hypothetical protein Saro_3506 [Novosphingobium... aromaticivorans DSM 12444] gb|ABP64366.1| hypothetical protein Saro_3506 [Novosphingobium aromaticivorans DSM 12444] YP_001165892.1 0.99 27% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-04-0115 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0115 ref|NP_723435.1| rolling stone CG9552-PA [Drosophila melanogaster...] sp|O44252|ROST_DROME Protein rolling stone gb|AAF52733.1| CG9552-PA [Drosophila melanogaster] gb|AAV37015.1| GH19958p [Drosophila melanogaster] NP_723435.1 4.7 17% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-03-0072 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0072 ref|ZP_01560354.1| hypothetical protein Bcenmc03DRAFT_5869 [Burkholderia... cenocepacia MC0-3] gb|EAV62210.1| hypothetical protein Bcenmc03DRAFT_5869 [Burkholderia cenocepacia MC0-3] ZP_01560354.1 0.002 26% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-03-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0017 ref|ZP_01561792.1| conserved hypothetical protein [Burkholderia c...enocepacia MC0-3] gb|EAV60539.1| conserved hypothetical protein [Burkholderia cenocepacia MC0-3] ZP_01561792.1 0.004 27% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0023 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0023 ref|YP_776807.1| major facilitator superfamily MFS_1 [Burkholderia... cepacia AMMD] gb|ABI90473.1| major facilitator superfamily MFS_1 [Burkholderia ambifaria AMMD] YP_776807.1 3e-98 61% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-03-0072 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0072 ref|ZP_01567096.1| conserved hypothetical protein [Burkholderia c...enocepacia MC0-3] gb|EAV54827.1| conserved hypothetical protein [Burkholderia cenocepacia MC0-3] ZP_01567096.1 3e-05 26% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-03-0063 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0063 ref|YP_332515.1| putative lipoprotein [Burkholderia pseudomallei ...1710b] gb|ABA48419.1| putative lipoprotein [Burkholderia pseudomallei 1710b] YP_332515.1 7e-12 31% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-04-0048 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0048 ref|ZP_02030135.1| hypothetical protein PARMER_00103 [Parabacteroides... merdae ATCC 43184] gb|EDN88360.1| hypothetical protein PARMER_00103 [Parabacteroides merdae ATCC 43184] ZP_02030135.1 1.7 24% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-07-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0038 ref|ZP_02033844.1| hypothetical protein PARMER_03881 [Parabacteroides... merdae ATCC 43184] gb|EDN84434.1| hypothetical protein PARMER_03881 [Parabacteroides merdae ATCC 43184] ZP_02033844.1 2e-60 39% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-02-0012 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0012 ref|YP_001302790.1| hypothetical protein BDI_1410 [Parabacteroides... distasonis ATCC 8503] gb|ABR43168.1| conserved hypothetical protein [Parabacteroides distasonis ATCC 8503] YP_001302790.1 1.5 25% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-04-0117 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0117 ref|YP_001303292.1| putative permease [Parabacteroides distasonis... ATCC 8503] gb|ABR43670.1| putative permease [Parabacteroides distasonis ATCC 8503] YP_001303292.1 2.4 21% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-03-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0016 ref|YP_001301601.1| hypothetical protein BDI_0190 [Parabacteroides... distasonis ATCC 8503] gb|ABR41979.1| conserved hypothetical protein [Parabacteroides distasonis ATCC 8503] YP_001301601.1 7e-05 32% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-03-0016 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0016 ref|ZP_02030966.1| hypothetical protein PARMER_00942 [Parabacteroides... merdae ATCC 43184] gb|EDN87817.1| hypothetical protein PARMER_00942 [Parabacteroides merdae ATCC 43184] ZP_02030966.1 0.014 28% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0048 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0048 ref|YP_001164099.1| potassium efflux system [Yersinia pestis Pest...oides F] gb|ABP41126.1| potassium efflux system [Yersinia pestis Pestoides F] YP_001164099.1 1e-106 53% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0044 ref|YP_001193285.1| Mn2+/Fe2+ transporter, NRAMP family [Flavobac...terium johnsoniae UW101] gb|ABQ03966.1| Mn2+/Fe2+ transporter, NRAMP family [Flavobacterium johnsoniae UW101] YP_001193285.1 1e-123 62% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-07-0041 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0041 ref|YP_001295245.1| Prenyltransferase family protein [Flavobacter...ium psychrophilum JIP02/86] emb|CAL42427.1| Prenyltransferase family protein [Flavobacterium psychrophilum JIP02/86] YP_001295245.1 4e-31 34% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|YP_001295229.1| hypothetical protein FP0297 [Flavobacterium p...sychrophilum JIP02/86] emb|CAL42411.1| Protein of unknown function [Flavobacterium psychrophilum JIP02/86] YP_001295229.1 4e-70 43% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|ZP_01734073.1| hypothetical protein FBBAL38_06975 [Flavobacte...ria bacterium BAL38] gb|EAZ95423.1| hypothetical protein FBBAL38_06975 [Flavobacteria bacterium BAL38] ZP_01734073.1 5e-72 44% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0038 ref|ZP_01732766.1| ABC transporter, permease protein, putative [Flavob...acteria bacterium BAL38] gb|EAZ95835.1| ABC transporter, permease protein, putative [Flavobacteria bacterium BAL38] ZP_01732766.1 9e-60 41% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-07-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0035 ref|YP_001296724.1| Protein of unknown function YfkH [Flavobacter...ium psychrophilum JIP02/86] emb|CAL43921.1| Protein of unknown function YfkH [Flavobacterium psychrophilum JIP02/86] YP_001296724.1 3e-31 32% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|ZP_01105157.1| hypothetical protein FB2170_03125 [Flavobacter...iales bacterium HTCC2170] gb|EAR02242.1| hypothetical protein FB2170_03125 [Flavobacteriales bacterium HTCC2170] ZP_01105157.1 1e-68 43% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0035 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0035 ref|ZP_01732843.1| hypothetical protein FBBAL38_00795 [Flavobacte...ria bacterium BAL38] gb|EAZ95912.1| hypothetical protein FBBAL38_00795 [Flavobacteria bacterium BAL38] ZP_01732843.1 7e-31 30% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0034 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0034 ref|YP_001193612.1| major facilitator superfamily MFS_1 [Flavobac...terium johnsoniae UW101] gb|ABQ04293.1| major facilitator superfamily MFS_1 [Flavobacterium johnsoniae UW101] YP_001193612.1 3e-63 49% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-07-0007 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0007 ref|YP_001197147.1| Urea transporter [Flavobacterium johnsoniae U...W101] gb|ABQ07828.1| Urea transporter [Flavobacterium johnsoniae UW101] YP_001197147.1 4e-69 48% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0043 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0043 ref|YP_001297326.1| hypothetical protein FP2472 [Flavobacterium p...sychrophilum JIP02/86] emb|CAL44525.1| Hypothetical protein [Flavobacterium psychrophilum JIP02/86] YP_001297326.1 2e-06 24% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-07-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0040 ref|YP_001197343.1| MATE efflux family protein [Flavobacterium jo...hnsoniae UW101] gb|ABQ08024.1| MATE efflux family protein [Flavobacterium johnsoniae UW101] YP_001197343.1 6e-52 43% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-07-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0040 ref|ZP_01732967.1| multidrug resistance protein [Flavobacteria ba...cterium BAL38] gb|EAZ96036.1| multidrug resistance protein [Flavobacteria bacterium BAL38] ZP_01732967.1 9e-51 38% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-07-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0040 ref|ZP_01203383.1| multidrug efflux pump, matE family [Flavobacte...ria bacterium BBFL7] gb|EAS18538.1| multidrug efflux pump, matE family [Flavobacteria bacterium BBFL7] ZP_01203383.1 3e-50 43% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-07-0041 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0041 ref|ZP_01107412.1| hypothetical protein FB2170_08224 [Flavobacter...iales bacterium HTCC2170] gb|EAR00476.1| hypothetical protein FB2170_08224 [Flavobacteriales bacterium HTCC2170] ZP_01107412.1 2e-32 36% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-04-0130 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0130 ref|NP_001040188.1| KDEL endoplasmic reticulum protein retention ...receptor 2a [Bombyx mori] gb|ABD36213.1| KDEL endoplasmic reticulum protein retention receptor 2a [Bombyx mori] NP_001040188.1 3e-45 47% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-04-0113 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0113 ref|XP_001418476.1| predicted protein [Ostreococcus lucimarinus C...CE9901] gb|ABO96769.1| predicted protein [Ostreococcus lucimarinus CCE9901] XP_001418476.1 2.9 25% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-04-0122 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0122 ref|XP_001415961.1| predicted protein [Ostreococcus lucimarinus C...CE9901] gb|ABO94253.1| predicted protein [Ostreococcus lucimarinus CCE9901] XP_001415961.1 4e-25 32% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-07-0059 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0059 ref|YP_857839.1| pts system N-acetylglucosamine-specific eiicba component (eiicba-nag)(eii...N-acetylglucosamine-specific eiicba component (eiicba-nag)(eii-nag) [Aeromonas hydrophila subsp. hydrophila ATCC 7966] YP_857839.1 0.0 99% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-05-0031 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0031 ref|YP_294122.1| hypothetical protein EhV364 [Emiliania huxleyi v...irus 86] emb|CAI65791.1| putative membrane protein [Emiliania huxleyi virus 86] YP_294122.1 8e-16 44% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0028 ref|YP_001193977.1| hypothetical protein Fjoh_1626 [Flavobacterium john...soniae UW101] gb|ABQ04658.1| hypothetical protein Fjoh_1626 [Flavobacterium johnsoniae UW101] YP_001193977.1 5e-72 44% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-07-0049 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0049 ref|NP_965518.1| hypothetical protein LJ1711 [Lactobacillus johns...onii NCC 533] gb|AAS09484.1| hypothetical protein LJ_1711 [Lactobacillus johnsonii NCC 533] NP_965518.1 4e-39 50% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-05-0050 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0050 ref|NP_965518.1| hypothetical protein LJ1711 [Lactobacillus johns...onii NCC 533] gb|AAS09484.1| hypothetical protein LJ_1711 [Lactobacillus johnsonii NCC 533] NP_965518.1 3e-35 42% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-07-0023 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0023 ref|YP_234795.1| Major facilitator superfamily [Pseudomonas syringae pv. syringa...e B728a] gb|AAY36757.1| Major facilitator superfamily [Pseudomonas syringae pv. syringae B728a] YP_234795.1 1e-147 93% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0018 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0018 ref|YP_234515.1| hypothetical protein Psyr_1426 [Pseudomonas syringae pv. syringa...e B728a] gb|AAY36477.1| conserved hypothetical protein [Pseudomonas syringae pv. syringae B728a] YP_234515.1 4e-23 23% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-04-0109 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0109 ref|YP_236998.1| quinoprotein [Pseudomonas syringae pv. syringae ...B728a] gb|AAY38960.1| quinoprotein [Pseudomonas syringae pv. syringae B728a] YP_236998.1 1.2 29% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-07-0002 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0002 ref|YP_001453764.1| hypothetical protein CKO_02205 [Citrobacter k...oseri ATCC BAA-895] gb|ABV13328.1| hypothetical protein CKO_02205 [Citrobacter koseri ATCC BAA-895] YP_001453764.1 2e-65 50% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0067 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0067 ref|YP_001454621.1| hypothetical protein CKO_03094 [Citrobacter k...oseri ATCC BAA-895] gb|ABV14185.1| hypothetical protein CKO_03094 [Citrobacter koseri ATCC BAA-895] YP_001454621.1 1e-79 61% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0045 ref|ZP_01061096.1| copper/silver efflux P-type ATPase [Flavobacte...rium sp. MED217] gb|EAQ49147.1| copper/silver efflux P-type ATPase [Leeuwenhoekiella blandensis MED217] ZP_01061096.1 1e-128 60% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-07-0021 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0021 ref|YP_855218.1| Na+/H+ antiporter NhaA [Aeromonas hydrophila subsp. hydrop...hila ATCC 7966] gb|ABK38348.1| Na+/H+ antiporter NhaA [Aeromonas hydrophila subsp. hydrophila ATCC 7966] YP_855218.1 1e-126 99% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-07-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0064 ref|YP_855025.1| sulfatase [Aeromonas hydrophila subsp. hydrophil...a ATCC 7966] gb|ABK38012.1| sulfatase [Aeromonas hydrophila subsp. hydrophila ATCC 7966] YP_855025.1 0.0 98% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-04-0112 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0112 ref|ZP_01465266.1| hypothetical protein STIAU_3860 [Stigmatella aura...ntiaca DW4/3-1] gb|EAU63984.1| hypothetical protein STIAU_3860 [Stigmatella aurantiaca DW4/3-1] ZP_01465266.1 8.6 33% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-07-0029 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0029 ref|ZP_01423834.1| Lanthionine synthetase C-like [Herpetosiphon aura...ntiacus ATCC 23779] gb|EAU19386.1| Lanthionine synthetase C-like [Herpetosiphon aurantiacus ATCC 23779] ZP_01423834.1 2e-20 25% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-01-0081 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0081 ref|XP_308789.4| AGAP006968-PA [Anopheles gambiae str. PEST] gb|A...BU40241.1| anion exchanger [Anopheles gambiae] gb|EAA04339.5| AGAP006968-PA [Anopheles gambiae str. PEST] XP_308789.4 0.0 100% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-02-0058 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0058 ref|XP_308034.4| AGAP002156-PB [Anopheles gambiae str. PEST] gb|A...AQ63187.1| G-protein coupled receptor [Anopheles gambiae] gb|EAA03704.4| AGAP002156-PB [Anopheles gambiae str. PEST] XP_308034.4 0.0 100% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-05-0056 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0056 ref|XP_001237203.1| AGAP001022-PA [Anopheles gambiae str. PEST] g...b|AAR28375.1| putative sulfakinin GPCR [Anopheles gambiae] gb|EAU77575.1| AGAP001022-PA [Anopheles gambiae str. PEST] XP_001237203.1 0.0 100% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-02-0048 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0048 ref|XP_321046.3| AGAP002011-PA [Anopheles gambiae str. PEST] gb|A...AP47144.1| Rh-like glycoprotein [Anopheles gambiae] gb|EAA01247.4| AGAP002011-PA [Anopheles gambiae str. PEST] XP_321046.3 1e-138 97% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-05-0017 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0017 ref|XP_001237302.1| AGAP000351-PA [Anopheles gambiae str. PEST] g...b|AAR28374.1| putative NPY GPCR [Anopheles gambiae] gb|EAU77283.1| AGAP000351-PA [Anopheles gambiae str. PEST] XP_001237302.1 0.0 100% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-04-0013 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0013 gb|AAR01130.1| odorant receptor 1 [Anopheles gambiae] gb|AAR01131....1| odorant receptor 1 [Anopheles gambiae] gb|AAR01132.1| odorant receptor 1 [Anopheles gambiae] AAR01130.1 1e-16 31% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-04-0079 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0079 gb|AAR01130.1| odorant receptor 1 [Anopheles gambiae] gb|AAR01131....1| odorant receptor 1 [Anopheles gambiae] gb|AAR01132.1| odorant receptor 1 [Anopheles gambiae] AAR01130.1 1e-112 98% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-01-0050 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0050 ref|XP_001657779.1| D7 protein, putative [Aedes aegypti] gb|EAT41...994.1| D7 protein, putative [Aedes aegypti] gb|ABM68616.1| AAEL006424-PA [Aedes aegypti] XP_001657779.1 7e-19 27% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-04-0002 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0002 ref|XP_001658302.1| sodium/solute symporter [Aedes aegypti] gb|EA...T47697.1| sodium/solute symporter [Aedes aegypti] gb|ABM68585.1| AAEL001198-PA [Aedes aegypti] XP_001658302.1 1e-143 68% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-02-0048 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0048 ref|XP_001352669.1| GA20395-PA [Drosophila pseudoobscura] gb|EAL3...0167.1| GA20395-PA [Drosophila pseudoobscura] gb|AAV40852.1| Rh-like protein [Drosophila pseudoobscura] XP_001352669.1 3e-96 67% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-07-0027 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0027 ref|ZP_00232264.1| conserved hypothetical protein [Listeria monocyt...ogenes str. 4b H7858] gb|EAL07894.1| conserved hypothetical protein [Listeria monocytogenes str. 4b H7858] ZP_00232264.1 1e-07 38% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-02-0011 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0011 ref|ZP_01573989.1| sodium:neurotransmitter symporter [Clostridium... cellulolyticum H10] gb|EAV71958.1| sodium:neurotransmitter symporter [Clostridium cellulolyticum H10] ZP_01573989.1 0.93 23% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-03-0082 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0082 ref|XP_001568166.1| proteophosphoglycan ppg4 [Leishmania brazilie...nsis] emb|CAM43270.1| proteophosphoglycan ppg4 [Leishmania braziliensis] XP_001568166.1 6e-15 33% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-03-0032 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0032 ref|XP_001568167.1| proteophosphoglycan ppg4 [Leishmania brazilie...nsis] emb|CAM43271.1| proteophosphoglycan ppg4 [Leishmania braziliensis] XP_001568167.1 3e-40 29% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-04-0123 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0123 ref|XP_973273.1| PREDICTED: similar to Putative gustatory receptor 21a [Tribolium... castaneum] emb|CAL23143.2| gustatory receptor candidate 10 [Tribolium castaneum] emb|CAL231...72.3| gustatory receptor candidate 39 [Tribolium castaneum] XP_973273.1 1e-135 60% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-03-0064 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0064 ref|XP_974025.1| PREDICTED: similar to CG13788-PB, isoform B [Tribolium... castaneum] emb|CAL23157.2| gustatory receptor candidate 24 [Tribolium castaneum] XP_974025.1 0.002 20% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-01-0059 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0059 ref|XP_968580.1| PREDICTED: hypothetical protein [Tribolium casta...neum] emb|CAL23149.2| gustatory receptor candidate 16 [Tribolium castaneum] XP_968580.1 0.11 22% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-02-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0045 ref|NP_001076796.1| cardioactive peptide receptor 1 [Tribolium ca...staneum] gb|ABN79651.1| cardioactive peptide receptor 1 [Tribolium castaneum] NP_001076796.1 1e-111 66% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-02-0115 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0115 ref|XP_973273.1| PREDICTED: similar to Putative gustatory receptor 21a [Tribolium... castaneum] emb|CAL23143.2| gustatory receptor candidate 10 [Tribolium castaneum] emb|CAL231...72.3| gustatory receptor candidate 39 [Tribolium castaneum] XP_973273.1 9e-83 40% ...

  3. NCBI nr-aa BLAST: CBRC-AGAM-02-0045 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0045 ref|NP_001076795.1| cardioactive peptide receptor 2 [Tribolium ca...staneum] gb|ABN79652.1| cardioactive peptide receptor 2 [Tribolium castaneum] NP_001076795.1 1e-113 68% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-02-0098 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0098 ref|NP_001076793.1| ecdysis triggering hormone receptor isoform B [Tribolium... castaneum] gb|ABN79654.1| ecdysis triggering hormone receptor isoform B [Tribolium castaneum] NP_001076793.1 1e-114 62% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-02-0046 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0046 ref|NP_001076796.1| cardioactive peptide receptor 1 [Tribolium ca...staneum] gb|ABN79651.1| cardioactive peptide receptor 1 [Tribolium castaneum] NP_001076796.1 1e-121 68% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-02-0098 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0098 ref|NP_001076792.1| ecdysis triggering hormone receptor isoform A [Tribolium... castaneum] gb|ABN79653.1| ecdysis triggering hormone receptor isoform A [Tribolium castaneum] NP_001076792.1 1e-121 67% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-02-0128 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0128 ref|XP_972629.1| PREDICTED: similar to Probable gustatory receptor 64e [Tribolium... castaneum] emb|CAL23162.2| gustatory receptor candidate 29 [Tribolium castaneum] emb|CAL231...40.2| gustatory receptor candidate 7 [Tribolium castaneum] XP_972629.1 4e-42 29% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-02-0099 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0099 ref|NP_001076793.1| ecdysis triggering hormone receptor isoform B [Tribolium... castaneum] gb|ABN79654.1| ecdysis triggering hormone receptor isoform B [Tribolium castaneum] NP_001076793.1 4e-47 44% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-02-0065 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0065 ref|XP_975520.1| PREDICTED: similar to CG13788-PB, isoform B [Tribolium... castaneum] emb|CAL23188.2| gustatory receptor candidate 55 [Tribolium castaneum] XP_975520.1 9e-17 31% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0052 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0052 ref|XP_972629.1| PREDICTED: similar to Probable gustatory receptor 64e [Tribolium... castaneum] emb|CAL23162.2| gustatory receptor candidate 29 [Tribolium castaneum] emb|CAL231...40.2| gustatory receptor candidate 7 [Tribolium castaneum] XP_972629.1 4e-42 29% ...

  11. NCBI nr-aa BLAST: CBRC-AGAM-03-0072 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0072 ref|ZP_01909055.1| hypothetical protein PPSIR1_23714 [Plesiocysti...s pacifica SIR-1] gb|EDM78078.1| hypothetical protein PPSIR1_23714 [Plesiocystis pacifica SIR-1] ZP_01909055.1 5e-05 36% ...

  12. NCBI nr-aa BLAST: CBRC-AGAM-01-0038 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-01-0038 ref|ZP_01905410.1| hypothetical protein PPSIR1_21714 [Plesiocysti...s pacifica SIR-1] gb|EDM81578.1| hypothetical protein PPSIR1_21714 [Plesiocystis pacifica SIR-1] ZP_01905410.1 1.2 40% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-07-0049 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0049 ref|ZP_01274777.1| Surface protein from Gram-positive cocci, anch...or region [Lactobacillus reuteri 100-23] gb|EAS88254.1| Surface protein from Gram-positive cocci, anchor region [Lactobacillus reuteri 100-23] ZP_01274777.1 2e-39 52% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-02-0187 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0187 ref|XP_001648623.1| dopamine receptor, invertebrate [Aedes aegypt...i] gb|EAT33346.1| dopamine receptor, invertebrate [Aedes aegypti] XP_001648623.1 1e-112 63% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-07-0056 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0056 ref|ZP_01713395.1| binding-protein-dependent transport systems in...ner membrane component [Pseudomonas putida GB-1] gb|EAZ70051.1| binding-protein-dependent transport systems

  16. NCBI nr-aa BLAST: CBRC-AGAM-04-0104 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0104 ref|XP_001223090.1| hypothetical protein CHGG_03876 [Chaetomium globo...sum CBS 148.51] gb|EAQ87257.1| hypothetical protein CHGG_03876 [Chaetomium globosum CBS 148.51] XP_001223090.1 0.11 31% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-03-0057 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0057 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 0.0 37% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-02-0155 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0155 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 0.0 39% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-02-0094 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0094 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 0.0 39% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-02-0012 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0012 ref|YP_304864.1| hypothetical protein Mbar_A1321 [Methanosarcina barker...i str. Fusaro] gb|AAZ70284.1| hypothetical protein Mbar_A1321 [Methanosarcina barkeri str. Fusaro] YP_304864.1 0.39 34% ...

  1. NCBI nr-aa BLAST: CBRC-AGAM-04-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0028 ref|YP_305972.1| hypothetical protein Mbar_A2479 [Methanosarcina barker...i str. Fusaro] gb|AAZ71392.1| hypothetical protein Mbar_A2479 [Methanosarcina barkeri str. Fusaro] YP_305972.1 9e-19 35% ...

  2. NCBI nr-aa BLAST: CBRC-AGAM-02-0033 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0033 ref|NP_788686.1| tincar CG31247-PA, isoform A [Drosophila melanog...aster] ref|NP_788687.1| tincar CG31247-PD, isoform D [Drosophila melanogaster] gb|AAO41572.1| CG31247-PA, is

  3. NCBI nr-aa BLAST: CBRC-AGAM-05-0002 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0002 ref|YP_001477392.1| sugar transporter [Serratia proteamaculans 56...8] gb|ABV40264.1| sugar transporter [Serratia proteamaculans 568] YP_001477392.1 1e-08 25% ...

  4. NCBI nr-aa BLAST: CBRC-AGAM-07-0002 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0002 ref|YP_001477899.1| protein of unknown function DUF340 membrane [Serratia proteam...aculans 568] gb|ABV40771.1| protein of unknown function DUF340 membrane [Serratia proteamaculans 568] YP_001477899.1 4e-67 53% ...

  5. NCBI nr-aa BLAST: CBRC-AGAM-07-0020 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0020 ref|YP_001478696.1| major facilitator superfamily MFS_1 [Serratia proteam...aculans 568] gb|ABV41568.1| major facilitator superfamily MFS_1 [Serratia proteamaculans 568] YP_001478696.1 1e-164 82% ...

  6. NCBI nr-aa BLAST: CBRC-AGAM-07-0007 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0007 ref|YP_300356.1| hypothetical protein SSP0266 [Staphylococcus saprophytic...us subsp. saprophyticus ATCC 15305] dbj|BAE17411.1| conserved hypothetical protein [Staphylococcus saprophyticus subsp. saprophyticus ATCC 15305] YP_300356.1 2e-35 29% ...

  7. NCBI nr-aa BLAST: CBRC-AGAM-05-0040 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0040 ref|ZP_01520645.1| hypothetical protein CtesDRAFT_4667 [Comamonas... testosteroni KF-1] gb|EAV14383.1| hypothetical protein CtesDRAFT_4667 [Comamonas testosteroni KF-1] ZP_01520645.1 8e-05 27% ...

  8. NCBI nr-aa BLAST: CBRC-AGAM-02-0104 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0104 ref|ZP_01519290.1| hypothetical protein CtesDRAFT_1042 [Comamonas... testosteroni KF-1] gb|EAV15567.1| hypothetical protein CtesDRAFT_1042 [Comamonas testosteroni KF-1] ZP_01519290.1 8e-04 33% ...

  9. NCBI nr-aa BLAST: CBRC-AGAM-05-0028 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0028 ref|YP_719270.1| possible large adhesin [Haemophilus somnus 129PT...] gb|ABI25333.1| conserved hypothetical protein [Haemophilus somnus 129PT] YP_719270.1 1e-27 34% ...

  10. NCBI nr-aa BLAST: CBRC-AGAM-07-0030 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0030 ref|ZP_00769372.1| Binding-protein-dependent transport systems in...ner membrane component [Chloroflexus aurantiacus J-10-fl] gb|EAO57514.1| Binding-protein-dependent transport systems

  11. NCBI nr-aa BLAST: CBRC-AGAM-07-0056 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0056 ref|ZP_01637716.1| binding-protein-dependent transport systems in...ner membrane component [Pseudomonas putida W619] gb|EAX19985.1| binding-protein-dependent transport systems

  12. NCBI nr-aa BLAST: CBRC-AGAM-02-0058 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0058 ref|NP_001076809.1| adipokinetic hormone receptor [Tribolium cast...aneum] gb|ABE02225.1| adipokinetic hormone receptor [Tribolium castaneum] gb|ABN79650.1| adipokinetic hormone receptor [Tribolium castaneum] NP_001076809.1 1e-112 60% ...

  13. NCBI nr-aa BLAST: CBRC-AGAM-02-0165 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-02-0165 ref|XP_309589.1| putative glyco-protein hormone fsh-like receptor... (AGAP004035-PA) [Anopheles gambiae str. PEST] gb|EAA05376.2| putative glyco-protein hormone fsh-like receptor (AGAP004035-PA) [Anopheles gambiae str. PEST] XP_309589.1 0.0 93% ...

  14. NCBI nr-aa BLAST: CBRC-AGAM-04-0093 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0093 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 0.0 40% ...

  15. NCBI nr-aa BLAST: CBRC-AGAM-04-0091 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-04-0091 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 1e-163 38% ...

  16. NCBI nr-aa BLAST: CBRC-AGAM-05-0053 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-05-0053 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 0.0 39% ...

  17. NCBI nr-aa BLAST: CBRC-AGAM-03-0010 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-03-0010 ref|ZP_00373744.1| SD27140p [Wolbachia endosymbiont of Drosophila... ananassae] gb|EAL58741.1| SD27140p [Wolbachia endosymbiont of Drosophila ananassae] ZP_00373744.1 0.0 39% ...

  18. NCBI nr-aa BLAST: CBRC-AGAM-07-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0044 ref|ZP_01721553.1| manganese transport protein MntH [Algoriphagus... sp. PR1] gb|EAZ79142.1| manganese transport protein MntH [Algoriphagus sp. PR1] ZP_01721553.1 1e-104 62% ...

  19. NCBI nr-aa BLAST: CBRC-AGAM-07-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0044 ref|YP_001020152.1| manganese transport protein [Methylibium petr...oleiphilum PM1] gb|ABM93917.1| manganese transport protein [Methylibium petroleiphilum PM1] YP_001020152.1 2e-99 51% ...

  20. NCBI nr-aa BLAST: CBRC-AGAM-07-0044 [SEVENS

    Lifescience Database Archive (English)

    Full Text Available CBRC-AGAM-07-0044 ref|ZP_01302230.1| putative manganese transport protein MntH [Sph...ingomonas sp. SKA58] gb|EAT10059.1| putative manganese transport protein MntH [Sphingomonas sp. SKA58] ZP_01302230.1 1e-100 52% ...