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A lack of recombination limits breeding efforts in crops; therefore, increasing recombination rates can reduce linkage drag and generate new genetic combinations. We use computational analysis of 13 recombinant inbred mapping populations to assess crossover and gene conversion frequency in the hexaploid genome of wheat (Triticum aestivum). We observe that high-frequency crossover sites are shared between populations and that closely related parents lead to populations with more similar crossover patterns. We demonstrate that gene conversion is more prevalent and covers more of the genome in wheat than in other plants, making it a critical process in the generation of new haplotypes, particularly in centromeric regions where crossovers are rare. We identify quantitative trait loci for altered gene conversion and crossover frequency and confirm functionality for a novel RecQ helicase gene that belongs to an ancient clade that is missing in some plant lineages including Arabidopsis. This is the first gene to be demonstrated to be involved in gene conversion in wheat. Harnessing the RecQ helicase has the potential to break linkage drag utilizing widespread gene conversions.",{"EN":118},"Analysis of the recombination landscape of hexaploid bread wheat reveals genes controlling recombination and gene conversion frequency",{"VOID":120},"[\"7680209261760098487\"]",{"VOID":122},"Bernstein K, Gangloff S, Rothstein R. The RecQ DNA helicases in DNA repair. Annu Rev Genet. 2010;44:393–417.\nBorrill P, Ramirez-Gonzalez R, Uauy C. expVIP: a customisable RNA-seq data analysis and visualisation platform. Plant Physiol. 2016;170:2172–86.\nBrachet E, Beneut C, Serrentino M, Borde V. The CAF-1 and Hir histone chaperones associate with sites of meiotic double-strand breaks in budding yeast. PLOSone. 2015;10:5.\nBrenchley R, et al. Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature. 2012;491:705–10.\nBurridge A, Wilkinson P, Winfield M, Barker G, Allen A, Coghill J, Waterfall C, Edwards K. Conversion of array-based single nucleotide polymorphic markers for use in targeted genotyping by sequencing in hexaploid wheat (Tritium aestivum). Plant Biotechnol J. 2017;16(4):867–76.\nCesario J, McKim KS. RanGTP is required for meiotic spindle organization and the initiation of embryonic development in Drosophila. J Cell Sci. 2011;124(22):3797–810.\nChen J, Cooper D, Chuzhanova N, Ferec C, Patrinos GP. Gene conversion: mechanisms, evolution and human disease. Nat Rev Genet. 2007;8:762–75.\nClavijo BJ, et al. An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations. Genome Res. 2017;27(5):885–96.\nDarrier B, et al. High-resolution mapping of CO events in the hexaploid wheat genome suggests a universal recombination mechanism. Genetics. 2017;206(3):1373–88.\nDuroc Y, et al. Concerted action of the MutLβ heterodimer and Mer3 helicase regulates the global extent of meiotic gene conversion. eLife. 2017;6:e21900.\nEsch E, Szymaniak JM, Yates H, Pawlowski WP, Buckler ES. Using crossovers in recombinant inbred lines to identify quantitative trait loci controlling the global recombination frequency. Genetics. 2007;177(3):1851–8.\nFernandes JB, Séguéla-Arnaud M, Larcheveque C, Lloyd AH, Mercier R. Unleashing meiotic crossovers in hybrid plants. PNAS. 2018;115(10):2431–6.\nGardiner, L., Brabbs, T. and Hall, A. Recombination landscape of hexaploid bread wheat. Datasets. https:\u002F\u002Fwww.ebi.ac.uk\u002Fena\u002Fdata\u002Fview\u002FPRJEB28231 (2019).\nGirard C, Chelysheva L, Choinard S, Froger N, Macaisne N, et al. Correction: AAA-ATPase FIDGETIN-LIKE 1 and helicase FANCM antagonize meiotic crossovers by distinct mechanisms. PLoS Genet. 2015;11(9):e1005448.\nGriffiths S, Sharp R, Foote T, Bertin I, Wanous M, Reader S, Colas I, Moore G. Molecular characterization of Ph1 as a major chromosome pairing locus in polyploid wheat. Nature. 2006;439:749–52.\nGriffiths S, Wingen L, Edwards K. Populations axiom SNPs data - John Innes Centre, hdl:11529\u002F10996, CIMMYT Research Data & Software Repository Network, V6; 2017.\nHalldorsson BV, et al. The rate of meiotic gene conversion varies by sex and age. Nat Genet. 2016;48(11):1377–84.\nHartung F, Puchta H. The RecQ gene family in plants. J Plant Physiol. 2006;163(3):287–96.\nHartung F, Suer S, Puchta H. Two closely related RecQ helicases have antagonistic roles in homologous recombination and DNA repair in Arabidopsis thaliana. Proc Natl Acad Sci. 2007;104(47):18836–41.\nHiggins JD, Wright KM, Bomblies K, Franklin FCH. Cytological techniques to analyze meiosis in Arabidopsis arenosa for investigating adaptation to polyploidy. Front Plant Sci. 2013;4:546.\nHoek M, Myers M, Stillman B. An analysis of CAF-1-interacting proteins reveals dynamic and direct interactions with the KU complex and 140303 proteins. J Biol Chem. 2011;286(12):10876–87.\nHuang F, Mazina OM, Zentner IJ, Cocklin S, Mazin AV. Inhibition of homologous recombination in human cells by targeting RAD51 recombinase. J Med Chem. 2012;55(7):3011–20.\nJordan KW, et al. The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping. Plant J. 2018. https:\u002F\u002Fdoi.org\u002F10.1111\u002Ftpj.14009.\nKalab P, Heald R. The RanGTP gradient-a GPS for the mitotic spindle. J Cell Sci. 2008;121:1577–86.\nKarow, J., Constantinou, A., Li, Ji-Liang, L., West, S. & Hickson, I. The Bloom’s syndrome gene product promotes branch migration of Holliday junctions. PNAS, 97(12): 6504–6508 (2000).\nKrasileva KV, et al. Uncovering hidden variation in polyploid wheat. Proc Natl Acad Sci. 2017;114(6):913–21.\nLetunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.\nLi H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.\nLi H, et al. The sequence alignment\u002Fmap format and SAMtools. Bioinformatics. 2009;25:2078–9.\nLi HQ, Terada R, Li MR, Lida S. RecQ helicase enhances homologous recombination in plants. FEBS Lett. 2004;574(1–3):151–5.\nLi X, Tyler J. Nucleosome disassembly during human non-homologous end joining followed by concerted HIRA- and CAF-1 dependent reassembly. eLife. 2016;5:e15129.\nLöytynoja A, Goldman N. A model of evolution and structure for multiple sequence alignment. Philos Trans R Soc Lond Ser B Biol Sci. 2008;363:3913–9.\nMascher M, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544:427–33.\nMcKenna A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.\nMercier R, Mézard C, Jenczewski E, Macaisne N, Grelon M. The molecular biology of meiosis in plants. Annu Rev Plant Biol. 2015;66:297–327.\nPardo-Manuel De Villena F, Sapienza C. Recombination is proportional to the number of chromosome arms in mammals. Mamm Genome. 2001;12:318–22.\nQi J, Chen Y, Copenhaver G, Ma H. Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping. PNAS. 2014;111(27):10007–12.\nSchnable PS, Hsia A, Nikolau B. Genetic recombination in plants. Curr Opin Plant Biol. 1998;1:123–9.\nSéguéla-Arnaud M, et al. Multiple mechanisms limit meiotic crossovers: TOP3α and two BLM homologs antagonize crossovers in parallel to FANCM. Proc Natl Acad Sci U S A. 2015;112(15):4713–8.\nShalev G, Sitrit Y, Avivi-Ragolski N, Lichtenstein C, Levy A. Stimulation of homologous recombination in plants by expression of the bacterial resolvase RuvC. PNAS. 1999;96(13):7398–402.\nShi W, et al. The role of RPA2 phosphorylation in homologous recombination in response to replication arrest. Carcinogenesis. 2010;31(6):994–1002.\nStamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.\nSun Y, et al. Deep genome-wide measurement of meiotic gene conversion using tetrad analysis in Arabidopsis Thaliana. PLoS Genet. 2012;8(10):e1002968.\nSzostak JW, Orr-Weaver TL, Rothstein RJ, Stahl FW. The double- strand break repair model for recombination. Cell. 1983;33:25–35.\nTalbert PB, Henikoff S. Centromeres convert but don’t cross. PLoS Biol. 2010;8(3):e1000326.\nThe International Wheat Genome Sequencing Consortium (IWGSC) et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. 2018;361(6403):eaar7191.\nWaterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ. Jalview version 2 - a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtp033.\nWiedemann G, et al. RecQ helicases function in development, DNA repair, and gene targeting in Physcomitrella patens. Plant Cell. 2018;30:717–36.\nWilkinson PA, Winfield MO, Barker GLA, Allen AM, Burridge A, Coghill JA, Burridge A, Edwards KJ. CerealsDB 2.0: an integrated resource for plant breeders and scientists. BMC Bioinformatics. 2012;13:219.\nWingen LU, et al. Wheat landrace genome diversity. Genetics. 2017;205(4):1657–76.\nWijnker E, et al. The genomic landscape of meiotic crossovers and gene conversions in Arabidopsis thaliana. eLife. 2013;2:e01426.\nYang S, et al. Great majority of recombination events in Arabidopsis are gene conversion events. Proc Natl Acad Sci U S A. 2012;109(51):20992–7.\nZhao Q, Brkljacic J, Meier I. Two distinct interacting classes of nuclear envelope–associated coiled-coil proteins are required for the tissue-specific nuclear envelope targeting of Arabidopsis RanGAP. Plant Cell. 2008;20(6):1639–51.\nZiolkowski PA, Underwood CJ, Lambing C, Martinez-Garcia M, Lawrence EJ, et al. Natural variation and dosage of the HEI10 meiotic E3 ligase control Arabidopsis crossover recombination. 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Accessed 23 Mar 2021.",{"doi":379},{"id":899,"createTime":900,"updateTime":901,"relativeEntities":902,"slug":903,"properties":904,"entityType":125,"verifyStatus":126,"verifyTime":915,"verifyNote":128,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":916,"fullTextUrl":20,"authors":917,"publicationType":311,"publisherRelationship":972,"citationCount":21,"citationInfo":1017,"publishDate":1020,"publishYear":1018,"citationAnalyzeStatus":361,"lastCitationAnalyze":901,"indexDatabases":1021,"openAccess":20,"references":20,"isForceReanalyzing":364},"989c3063-90b5-4856-813a-cb3b150a12f3","2023-11-26T22:59:31.572+00:00","2026-07-28T00:18:57.591+00:00",[],"SCA-recovering-single-cell-heterogeneity-through-information-based-dimensionality-reduction",{"abstract":905,"title":907,"gsPaper":909,"references":911,"doi":913},{"EN":906},"Dimensionality reduction summarizes the complex transcriptomic landscape of single-cell datasets for downstream analyses. Current approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly defined populations. Here, we present surprisal component analysis (SCA), a technique that newly leverages the information-theoretic notion of surprisal for dimensionality reduction to promote more meaningful signal extraction. For example, SCA uncovers clinically important cytotoxic T-cell subpopulations that are indistinguishable using existing pipelines. We also demonstrate that SCA substantially improves downstream imputation. SCA’s efficient information-theoretic paradigm has broad applications to the study of complex biological tissues in health and disease.",{"EN":908},"SCA: recovering single-cell heterogeneity through information-based dimensionality reduction",{"VOID":910},"[\"4683478148027923159\"]",{"VOID":912},"Park JH, Lee HK. Function of γδ T cells in tumor immunology and their application to cancer therapy. 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ArrayExpress; 2020. https:\u002F\u002Fwww.ebi.ac.uk\u002Fbiostudies\u002Farrayexpress\u002Fstudies\u002FE-MTAB-8735. Accessed Nov 2020.\nHao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Comprehensive integration of single-cell data. Gene Expression Omnibus; 2019. https:\u002F\u002Fidentifiers.org\u002Fgeo:GSE128639. Accessed Jan 2021.",{"VOID":914},"10.1186\u002Fs13059-023-02998-7","2024-08-31T00:07:28.570+00:00","https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02998-7",[918,951],{"id":919,"sortIndex":21,"researcher":20,"roles":920,"affiliations":921,"properties":946},"5b7d26fd-e8ca-4cbe-b020-f35d93c239b1",[134],[922,930,938],{"id":923,"sortIndex":21,"affiliation":924,"properties":20},"d89c1860-e69d-4229-8c7a-58c5b5f68cc5",{"id":923,"createTime":20,"updateTime":20,"relativeEntities":925,"slug":20,"properties":926,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":929,"statistic":20},[],{"title":927},{"VI":928},"Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, 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Targeting a CAR to the TRAC locus with CRISPR\u002FCas9 enhances tumour rejection. Nature. 2017;543:113–7.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":1242},"10.1007\u002Fs10440-022-00541-7",{"id":1238,"text":1244,"url":1240,"identifiers":1245},"Traxler EA, Yao Y, Wang Y-D, Woodard KJ, Kurita R, Nakamura Y, et al. A genome-editing strategy to treat β-hemoglobinopathies that recapitulates a mutation associated with a benign genetic condition. Nat Med. 2016;22:987–90.",{"doi":1242},{"id":1238,"text":1247,"url":1240,"identifiers":1248},"Silva G, Poirot L, Galetto R, Smith J, Montoya G, Duchateau P, et al. Meganucleases and other tools for targeted genome engineering: perspectives and challenges for gene therapy. Curr Gene Ther. 2011;11:11–27.",{"doi":1242},{"id":1238,"text":1250,"url":1240,"identifiers":1251},"Urnov FD, Rebar EJ, Holmes MC, Zhang SH, Gregory PD. Genome editing with engineered zinc finger nucleases. 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Nat Methods. 2015;12:939–42.",{"doi":1242},{"id":1238,"text":1265,"url":1240,"identifiers":1266},"Fu Y, Sander JD, Reyon D, Cascio VM, Joung KJ. Improving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nat Biotechnol. 2014;32:nbt.2808.",{"doi":1242},{"id":1238,"text":1268,"url":1240,"identifiers":1269},"Ran AF, Hsu PD, Lin C-Y, Gootenberg JS, Konermann S, Trevino AE, et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell. 2013;154:1380–9.",{"doi":1242},{"id":1238,"text":1271,"url":1240,"identifiers":1272},"Tsai SQ, Wyvekens N, Khayter C, Foden JA, Thapar V, Reyon D, et al. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat Biotechnol. 2014;32:569–76.",{"doi":1242},{"id":1238,"text":1274,"url":1240,"identifiers":1275},"Guilinger JP, Thompson DB, Liu DR. Fusion of catalytically inactive Cas9 to FokI nuclease improves the specificity of genome modification. Nat Biotechnol. 2014;32:nbt.2909.",{"doi":1242},{"id":1238,"text":1277,"url":1240,"identifiers":1278},"Kleinstiver BP, Pattanayak V, Prew MS, Tsai SQ, Nguyen NT, Zheng Z, et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature. 2016;529:490–5.",{"doi":1242},{"id":1238,"text":1280,"url":1240,"identifiers":1281},"Slaymaker IM, Gao L, Zetsche B, Scott DA, Yan WX, Zhang F. Rationally engineered Cas9 nucleases with improved specificity. Science. 2016;351:84–8.",{"doi":1242},{"id":1238,"text":1283,"url":1240,"identifiers":1284},"Tsai Q, Joung J. Defining and improving the genome-wide specificities of CRISPR-Cas9 nucleases. Nat Rev Genet. 2016;17:300–12.",{"doi":1242},{"id":1238,"text":1286,"url":1240,"identifiers":1287},"Hacein-Bey-Abina S, Kalle VC, Schmidt M, McCormack M, Wulffraat N, Leboulch P, et al. LMO2-associated clonal T cell proliferation in two patients after gene therapy for SCID-X1. Science. 2003;302:415–9.",{"doi":1242},{"id":1289,"text":1290,"url":1291,"identifiers":1292},"00456015-2238-4b25-a2ee-3c93b441cc09","Vermulst M, Bielas JH, Loeb LA. Quantification of random mutations in the mitochondrial genome. Methods. 2008;46:263–8.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS104620230800193X",{"doi":1293},"10.1016\u002Fj.ymeth.2008.10.008",{"id":1238,"text":1295,"url":1240,"identifiers":1296},"Fraietta JA, Nobles CL, Sammons MA, Lundh S, Carty SA, Reich TJ, et al. Disruption of TET2 promotes the therapeutic efficacy of CD19-targeted T cells. Nature. 2018;558:307–12.",{"doi":1242},{"id":1238,"text":1298,"url":1240,"identifiers":1299},"Tebas P, Stein D, Tang WW, Frank I, Wang SQ, Lee G, et al. Gene editing of CCR5 in autologous CD4 T cells of persons infected with HIV. N Engl J Med. 2014;370:901–10.",{"doi":1242},{"id":1238,"text":1301,"url":1240,"identifiers":1302},"Tsai SQ, Zheng Z, Nguyen NT, Liebers M, Topkar VV, Thapar V, et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotechnol. 2015;33:187–97.",{"doi":1242},{"id":1238,"text":1304,"url":1240,"identifiers":1305},"Frock RL, Hu J, Meyers RM, Ho Y-J, Kii E, Alt FW. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat Biotechnol. 2015;33:179–86.",{"doi":1242},{"id":1238,"text":1307,"url":1240,"identifiers":1308},"Crosetto N, Mitra A, Silva M, Bienko M, Dojer N, Wang Q, et al. Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing. Nat Methods. 2013;10:361–5.",{"doi":1242},{"id":1238,"text":1310,"url":1240,"identifiers":1311},"Ran AF, Cong L, Yan WX, Scott DA, Gootenberg JS, Kriz AJ, et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature. 2015;520:nature14299.",{"doi":1242},{"id":1238,"text":1313,"url":1240,"identifiers":1314},"Yan WX, Mirzazadeh R, Garnerone S, Scott D, Schneider MW, Kallas T, et al. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat Commun. 2017;8:15058.",{"doi":1242},{"id":1238,"text":1316,"url":1240,"identifiers":1317},"Wang X, Wang Y, Wu X, Wang J, Wang Y, Qiu Z, et al. Unbiased detection of off-target cleavage by CRISPR-Cas9 and TALENs using integrase-defective lentiviral vectors. Nat Biotechnol. 2015;33:175–8.",{"doi":1242},{"id":1238,"text":1319,"url":1240,"identifiers":1320},"Tsai SQ, Nguyen NT, Malagon-Lopez J, Topkar VV, Aryee MJ, Joung KJ. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat Methods. 2017;14:607–14.",{"doi":1242},{"id":1238,"text":1322,"url":1240,"identifiers":1323},"Park J, Childs L, Kim D, Hwang G-H, Kim S, Kim S-T, et al. 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Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat Biotechnol. 2014;32:246–51.",{"doi":1242},{"id":1238,"text":1349,"url":1240,"identifiers":1350},"Xie M, Hong C, Zhang B, Lowdon RF, Xing X, Li D, et al. DNA hypomethylation within specific transposable element families associates with tissue-specific enhancer landscape. Nat Genet. 2013;45:ng.2649.",{"doi":1242},{"id":1238,"text":1352,"url":1240,"identifiers":1353},"Sundaram V, Cheng Y, Ma Z, Li D, Xing X, Edge P, et al. Widespread contribution of transposable elements to the innovation of gene regulatory networks. Genome Res. 2014;24:1963–76.",{"doi":1242},{"id":1238,"text":1355,"url":1240,"identifiers":1356},"Kosicki M, Tomberg K, Bradley A. Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements. Nat Biotechnol. 2018;36:765–71.",{"doi":1242},{"id":1238,"text":1358,"url":1240,"identifiers":1359},"Adikusuma F, Piltz S, Corbett MA, Turvey M, McColl SR, Helbig KJ, et al. Large deletions induced by Cas9 cleavage. Nature. 2018;560:E8–9.",{"doi":1242},{"id":1238,"text":1361,"url":1240,"identifiers":1362},"Zheng Z, Liebers M, Zhelyazkova B, Cao Y, Panditi D, Lynch KD, et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat Med. 2014;20:1479–84.",{"doi":1242},{"id":1364,"text":1365,"url":1366,"identifiers":1367},"59c04a70-4ff1-4868-8fba-929a30b228be","Giannoukos G, Ciulla DM, Marco E, Abdulkerim HS, Barrera LA, Bothmer A, et al. UDiTaS™, a genome editing detection method for indels and genome rearrangements. 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Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature. 2009;459:108–12.",{"doi":1242},{"id":1238,"text":1382,"url":1240,"identifiers":1383},"Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci U S A. 2010;107:21931–6.",{"doi":1242},{"id":1238,"text":1385,"url":1240,"identifiers":1386},"Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins DR, et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet. 2007;39:311–8.",{"doi":1242},{"id":1238,"text":1388,"url":1240,"identifiers":1389},"di Iulio J, Bartha I, Wong EH, Yu H-C, Lavrenko V, Yang D, et al. The human noncoding genome defined by genetic diversity. 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Nat Biomed Eng. 2017;1:937.",{"doi":1242},{"id":1238,"text":1439,"url":1240,"identifiers":1440},"Yang L, Grishin D, Wang G, Aach J, Zhang C-Z, Chari R, et al. Targeted and genome-wide sequencing reveal single nucleotide variations impacting specificity of Cas9 in human stem cells. Nat Commun. 2014;5:5507.",{"doi":1242},{"id":1442,"createTime":1443,"updateTime":1444,"relativeEntities":1445,"slug":1446,"properties":1447,"entityType":125,"verifyStatus":126,"verifyTime":1458,"verifyNote":128,"languages":1459,"translateLanguages":20,"viewCount":21,"primaryUrl":1461,"fullTextUrl":20,"authors":1462,"publicationType":311,"publisherRelationship":1507,"citationCount":20,"citationInfo":20,"publishDate":1547,"publishYear":1548,"citationAnalyzeStatus":1549,"lastCitationAnalyze":1444,"indexDatabases":1550,"openAccess":20,"references":1551,"isForceReanalyzing":364},"4a759092-3249-43ab-8157-7df992e51632","2024-04-11T20:10:32.353+00:00","2026-07-27T04:43:25.610+00:00",[],"Large-scale-and-high-confidence-proteomic-analysis-of-human-seminal-plasma",{"abstract":1448,"title":1450,"gsPaper":1452,"keywords":1454,"doi":1456},{"EN":1449},"The development of mass spectrometric (MS) techniques now allows the investigation of very complex protein mixtures ranging from subcellular structures to tissues. Body fluids are also popular targets of proteomic analysis because of their potential for biomarker discovery. Seminal plasma has not yet received much attention from the proteomics community but its characterization could provide a future reference for virtually all studies involving human sperm. The fluid is essential for the survival of spermatozoa and their successful journey through the female reproductive tract. Here we report the high-confidence identification of 923 proteins in seminal fluid from a single individual. Fourier transform MS enabled parts per million mass accuracy, and two consecutive stages of MS fragmentation allowed confident identification of proteins even by single peptides. Analysis with GoMiner annotated two-thirds of the seminal fluid proteome and revealed a large number of extracellular proteins including many proteases. Other proteins originated from male accessory glands and have important roles in spermatozoan survival. This high-confidence characterization of seminal plasma content provides an inventory of proteins with potential roles in fertilization. When combined with quantitative proteomics methodologies, it should be useful for studies of fertilization, male infertility, and prostatic and testicular cancers.",{"EN":1451},"Large-scale and high-confidence proteomic analysis of human seminal 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Germany",[],{"id":1476,"sortIndex":21,"affiliation":1477,"properties":20},"6b26f637-73b5-4231-a9dc-54f91ed27297",{"id":1476,"createTime":20,"updateTime":20,"relativeEntities":1478,"slug":20,"properties":1479,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1482,"statistic":20},[],{"title":1480},{"EN":1481},"Center for Experimental BioInformatics (CEBI), Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark",[],{"title":1484},{"EN":1485},"Bartosz 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MA, Killian GJ: Effect of homologous and heterologous seminal plasma on the fertilizing ability of ejaculated bull spermatozoa assessed by penetration of zona-free bovine oocytes. 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[http:\u002F\u002Fproteome.biochem.mpg.de\u002F]",{"id":1635,"createTime":1636,"updateTime":1637,"relativeEntities":1638,"slug":1639,"properties":1640,"entityType":125,"verifyStatus":126,"verifyTime":1651,"verifyNote":128,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1652,"fullTextUrl":20,"authors":1653,"publicationType":311,"publisherRelationship":1803,"citationCount":21,"citationInfo":1848,"publishDate":1851,"publishYear":1849,"citationAnalyzeStatus":558,"lastCitationAnalyze":1637,"indexDatabases":1852,"openAccess":20,"references":20,"isForceReanalyzing":364},"b2953f65-270a-4e5f-80e9-d726a29fd9f0","2024-01-11T20:59:43.605+00:00","2026-07-26T22:14:07.880+00:00",[],"Global-analysis-of-alternative-splicing-regulation-by-insulin-and-wingless-signaling-in-Drosophilacells",{"abstract":1641,"title":1643,"gsPaper":1645,"references":1647,"doi":1649},{"EN":1642},"Despite the prevalence and biological relevance of both signaling pathways and alternative pre-mRNA splicing, our knowledge of how intracellular signaling impacts on alternative splicing regulation remains fragmentary. We report a genome-wide analysis using splicing-sensitive microarrays of changes in alternative splicing induced by activation of two distinct signaling pathways, insulin and wingless, in Drosophila cells in culture. Alternative splicing changes induced by insulin affect more than 150 genes and more than 50 genes are regulated by wingless activation. About 40% of the genes showing changes in alternative splicing also show regulation of mRNA levels, suggesting distinct but also significantly overlapping programs of transcriptional and post-transcriptional regulation. Distinct functional sets of genes are regulated by each pathway and, remarkably, a significant overlap is observed between functional categories of genes regulated transcriptionally and at the level of alternative splicing. Functions related to carbohydrate metabolism and cellular signaling are enriched among genes regulated by insulin and wingless, respectively. Computational searches identify pathway-specific sequence motifs enriched near regulated 5' splice sites. Taken together, our data indicate that signaling cascades trigger pathway-specific and biologically coherent regulatory programs of alternative splicing regulation. They also reveal that alternative splicing can provide a novel molecular mechanism for crosstalk between different signaling pathways.",{"EN":1644},"Global analysis of alternative splicing regulation by insulin and wingless signaling in Drosophilacells",{"VOID":1646},"[\"5833654529368205329\"]",{"VOID":1648},"Gerhart J: 1998 Warkany lecture: signaling pathways in development. Teratology. 1999, 60: 226-239. 10.1002\u002F(SICI)1096-9926(199910)60:4\u003C226::AID-TERA7>3.0.CO;2-W.\nClevers H: Wnt\u002Fbeta-catenin signaling in development and disease. Cell. 2006, 127: 469-480. 10.1016\u002Fj.cell.2006.10.018.\nNeumann C, Cohen S: Morphogens and pattern formation. 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PLoS Comput Biol. 2005, 1: e67-10.1371\u002Fjournal.pcbi.0010067.\nSupplementary Website for \"Sequence motifs overrepresented at the 5' end of intronic regions associated with splice-junctions regulated by activation of the wingless and insulin pathways\". [http:\u002F\u002Ffunctionalgenomics.upf.edu\u002Fsupplements\u002Fhartmann]\nCalarco JA, Xing Y, Caceres M, Calarco JP, Xiao X, Pan Q, Lee C, Preuss TM, Blencowe BJ: Global analysis of alternative splicing differences between humans and chimpanzees. Genes Dev. 2007, 21: 2963-2975. 10.1101\u002Fgad.1606907.\nPan Q, Shai O, Misquitta C, Zhang W, Saltzman AL, Mohammad N, Babak T, Siu H, Hughes TR, Morris QD, Frey BJ, Blencowe BJ: Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. 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J Biol Chem. 2001, 276: 22648-22654. 10.1074\u002Fjbc.M101260200.\nPatel NA, Kaneko S, Apostolatos HS, Bae SS, Watson JE, Davidowitz K, Chappell DS, Birnbaum MJ, Cheng JQ, Cooper DR: Molecular and genetic studies imply Akt-mediated signaling promotes protein kinase CbetaII alternative splicing via phosphorylation of serine\u002Farginine-rich splicing factor SRp40. J Biol Chem. 2005, 280: 14302-14309. 10.1074\u002Fjbc.M411485200.\nSato S, Idogawa M, Honda K, Fujii G, Kawashima H, Takekuma K, Hoshika A, Hirohashi S, Yamada T: beta-catenin interacts with the FUS proto-oncogene product and regulates pre-mRNA splicing. Gastroenterology. 2005, 129: 1225-1236. 10.1053\u002Fj.gastro.2005.07.025.\nShitashige M, Naishiro Y, Idogawa M, Honda K, Ono M, Hirohashi S, Yamada T: Involvement of splicing factor-1 in beta-catenin\u002FT-cell factor-4-mediated gene transactivation and pre-mRNA splicing. Gastroenterology. 2007, 132: 1039-1054. 10.1053\u002Fj.gastro.2007.01.007.\nClemens JC, Worby CA, Simonson-Leff N, Muda M, Maehama T, Hemmings BA, Dixon JE: Use of double-stranded RNA interference in Drosophila cell lines to dissect signal transduction pathways. Proc Natl Acad Sci USA. 2000, 97: 6499-6503. 10.1073\u002Fpnas.110149597.\nPrimer Selection Program. [http:\u002F\u002Ffokker.wi.mit.edu\u002Fprimer3\u002Finput.htm]\nPfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29: e45-10.1093\u002Fnar\u002F29.9.e45.\nBray N, Pachter L: MAVID: constrained ancestral alignment of multiple sequences. Genome Res. 2004, 14: 693-699. 10.1101\u002Fgr.1960404.\nFalcon S, Gentleman R: Using GOstats to test gene lists for GO term association. Bioinformatics. 2007, 23: 257-258. 10.1093\u002Fbioinformatics\u002Fbtl567.",{"VOID":1650},"10.1186\u002Fgb-2009-10-1-r11","2024-05-16T09:30:52.337+00:00","https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fgb-2009-10-1-r11",[1654,1678,1701,1728,1757,1774],{"id":1655,"sortIndex":21,"researcher":20,"roles":1656,"affiliations":1657,"properties":1675},"5659087d-221e-486c-9abd-d123c0ab49d3",[134],[1658,1666],{"id":1659,"sortIndex":21,"affiliation":1660,"properties":20},"a707696b-f8df-46a7-b8d5-71ccd01059b9",{"id":1659,"createTime":20,"updateTime":20,"relativeEntities":1661,"slug":20,"properties":1662,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1665,"statistic":20},[],{"title":1663},{"VI":1664},"Centre de Regulació Genòmica, Parc de Recerca Biomèdica de Barcelona, Barcelona, 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USA",{"id":1717,"sortIndex":84,"affiliation":1718,"properties":1724},"683c6273-a986-46b8-aa7f-8f31949ffcff",{"id":1717,"createTime":20,"updateTime":20,"relativeEntities":1719,"slug":20,"properties":1720,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1723,"statistic":20},[],{"title":1721},{"VI":1722},"Stowers Institute for Medical Research, Kansas City, USA",[],{},{"title":1726},{"VI":1727},"Marco 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Catalana de Recerca i Estudis Avançats, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain",[],{},{"title":1801},{"VI":1802},"Juan Valcárcel",{"url":1652,"publisher":1804,"properties":1843},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1805,"slug":10,"properties":1806,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1809,"manageAffiliations":1818,"indexDatabases":1824,"url":20,"thumbnailPath":20,"statistic":1838,"gsStatistic":20,"type":103,"analyzePriority":20},[],{"issn":1807,"title":1808},{"VOID":13},{"EN":15},[1810,1814],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1811,"label":1812,"description":1813,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1815,"label":1816,"description":1817,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[1819],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":1820,"slug":20,"properties":1821,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1823,"statistic":20},[],{"title":1822},{"EN":41},[],[1825,1831],{"id":45,"indexDatabase":1826,"url":56,"indexYears":57,"academicFieldIds":20,"indexDatabaseRanking":58},{"id":47,"createTime":20,"updateTime":20,"relativeEntities":1827,"label":1828,"description":1829,"key":53,"publicationTags":1830,"standard":20},[],{"EN":50,"VI":50},{"EN":50,"VI":52},[55],{"id":60,"indexDatabase":1832,"url":73,"indexYears":20,"academicFieldIds":1837,"indexDatabaseRanking":20},{"id":62,"createTime":20,"updateTime":20,"relativeEntities":1833,"label":1834,"description":1835,"key":69,"publicationTags":1836,"standard":20},[],{"EN":65,"VI":65},{"EN":67,"VI":68},[71,72],[75,76],{"impactFactor":21,"impactFactorByYear":1839,"i10Index":83,"i10IndexLast5Year":84,"totalPublication":85,"totalPublicationByYear":1840,"totalCitation":95,"totalCitationByYear":1841,"totalCitationPerPublication":99,"totalCitationPerPublicationByYear":1842,"hindexLast5Year":83,"hindex":83},{"2018":79,"2019":80,"2022":81,"2023":82},{"2000":87,"2001":87,"2002":88,"2003":89,"2004":90,"2005":89,"2006":89,"2007":90,"2008":91,"2009":91,"2010":90,"2011":83,"2012":89,"2014":84,"2016":92,"2017":87,"2018":83,"2019":91,"2020":91,"2021":93,"2022":89,"2023":90,"2024":94},{"2017":97,"2021":98},{"2017":101,"2021":102},{"pages":1844,"volume":1846},{"VOID":1845},"1-14",{"VOID":1847},"10",{"total":21,"publishYear":1849,"statisticByYear":1850},2009,{},"2009-01-29",[71,58],{"id":1854,"createTime":1855,"updateTime":1856,"relativeEntities":1857,"slug":1858,"properties":1859,"entityType":125,"verifyStatus":126,"verifyTime":1870,"verifyNote":128,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1871,"fullTextUrl":20,"authors":1872,"publicationType":311,"publisherRelationship":2000,"citationCount":2045,"citationInfo":2046,"publishDate":2052,"publishYear":2047,"citationAnalyzeStatus":558,"lastCitationAnalyze":2053,"indexDatabases":2054,"openAccess":20,"references":20,"isForceReanalyzing":364},"7b102520-9c69-4322-8140-2972dfe09a22","2024-01-11T10:54:56.945+00:00","2026-07-23T23:02:20.180+00:00",[],"Whole-genome-sequencing-of-glioblastoma-reveals-enrichment-of-non-coding-constraint-mutations-in-known-and-novel-genes",{"abstract":1860,"title":1862,"gsPaper":1864,"references":1866,"doi":1868},{"EN":1861},"Glioblastoma (GBM) has one of the worst 5-year survival rates of all cancers. While genomic studies of the disease have been performed, alterations in the non-coding regulatory regions of GBM have largely remained unexplored. We apply whole-genome sequencing (WGS) to identify non-coding mutations, with regulatory potential in GBM, under the hypothesis that regions of evolutionary constraint are likely to be functional, and somatic mutations are likely more damaging than in unconstrained regions. We validate our GBM cohort, finding similar copy number aberrations and mutated genes based on coding mutations as previous studies. Performing analysis on non-coding constraint mutations and their position relative to nearby genes, we find a significant enrichment of non-coding constraint mutations in the neighborhood of 78 genes that have previously been implicated in GBM. Among them, SEMA3C and DYNC1I1 show the highest frequencies of alterations, with multiple mutations overlapping transcription factor binding sites. We find that a non-coding constraint mutation in the SEMA3C promoter reduces the DNA binding capacity of the region. We also identify 1776 other genes enriched for non-coding constraint mutations with likely regulatory potential, providing additional candidate GBM genes. The mutations in the top four genes, DLX5, DLX6, FOXA1, and ISL1, are distributed over promoters, UTRs, and multiple transcription factor binding sites. These results suggest that non-coding constraint mutations could play an essential role in GBM, underscoring the need to connect non-coding genomic variation to biological function and disease pathology.",{"EN":1863},"Whole-genome sequencing of glioblastoma reveals enrichment of non-coding constraint mutations in known and novel genes",{"VOID":1865},"[\"2677768453129414305\"]",{"VOID":1867},"Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, Curtis C, Watts C, Tavare S. 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In addition, PHIDIAS allows submission, search and analysis of PHI genes and molecular networks curated from peer-reviewed literature. PHIDIAS is publicly available at \n                  http:\u002F\u002Fwww.phidias.us\n                  \n                .",{"EN":2065},"PHIDIAS: a pathogen-host interaction data integration and analysis system",{"VOID":2067},"[\"12141494655484792700\"]",{"VOID":2069},"Becker K, Hu Y, Biller-Andorno N: Infectious diseases - a global challenge. Int J Med Microbiol. 2006, 296: 179-185. 10.1016\u002Fj.ijmm.2005.12.015.\nXiang Z, Zheng W, He Y: BBP: Brucella genome annotation with literature mining and curation. BMC Bioinformatics. 2006, 7: 347-10.1186\u002F1471-2105-7-347.\nBateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S, Khanna A, Marshall M, Moxon S, Sonnhammer EL, et al: The Pfam protein families database. Nucleic Acids Res. 2004, 32: D138-141. 10.1093\u002Fnar\u002Fgkh121.\nLetunic I, Copley RR, Pils B, Pinkert S, Schultz J, Bork P: SMART 5: domains in the context of genomes and networks. Nucleic Acids Res. 2006, 34: D257-260. 10.1093\u002Fnar\u002Fgkj079.\nTatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al: The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003, 4: 41-10.1186\u002F1471-2105-4-41.\nMarchler-Bauer A, Anderson JB, Derbyshire MK, DeWeese-Scott C, Gonzales NR, Gwadz M, Hao L, He S, Hurwitz DI, Jackson JD, et al: CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res. 2007, 35: D237-240. 10.1093\u002Fnar\u002Fgkl951.\nHe Y, Vines RR, Wattam AR, Abramochkin GV, Dickerman AW, Eckart JD, Sobral BW: PIML: the Pathogen Information Markup Language. Bioinformatics. 2005, 21: 116-121. 10.1093\u002Fbioinformatics\u002Fbth462.\nHe Y, Rush HG, Liepman RS, Xiang Z, Colby LA: Pathobiology and management of laboratory rodents administered CDC Category A agents. Comparative Med. 2007, 57: 18-32.\nKanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, 32: D277-280. 10.1093\u002Fnar\u002Fgkh063.\nKarp PD, Riley M, Saier M, Paulsen IT, Collado-Vides J, Paley SM, Pellegrini-Toole A, Bonavides C, Gama-Castro S: The EcoCyc Database. Nucleic Acids Res. 2002, 30: 56-58. 10.1093\u002Fnar\u002F30.1.56.\nKrieger CJ, Zhang P, Mueller LA, Wang A, Paley S, Arnaud M, Pick J, Rhee SY, Karp PD: MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 2004, 32: D438-442. 10.1093\u002Fnar\u002Fgkh100.\nBader GD, Betel D, Hogue CW: BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003, 31: 248-250. 10.1093\u002Fnar\u002Fgkg056.\nThe Molecular Interaction Network Markup Language(MINetML). [http:\u002F\u002Fpathport.vbi.vt.edu\u002Fxml\u002Fmolecules\u002Fmolecules.dtd]\nStromback L, Lambrix P: Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX. Bioinformatics. 2005, 21: 4401-4407. 10.1093\u002Fbioinformatics\u002Fbti718.\nBarrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R: NCBI GEO: mining millions of expression profiles - database and tools. Nucleic Acids Res. 2005, 33: D562-566. 10.1093\u002Fnar\u002Fgki022.\nParkinson H, Sarkans U, Shojatalab M, Abeygunawardena N, Contrino S, Coulson R, Farne A, Lara GG, Holloway E, Kapushesky M, et al: ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2005, 33: D553-555. 10.1093\u002Fnar\u002Fgki056.\nRoop RM, Bellaire BH, Valderas MW, Cardelli JA: Adaptation of the brucellae to their intracellular niche. Mol Microbiol. 2004, 52: 621-630. 10.1111\u002Fj.1365-2958.2004.04017.x.\nDelVecchio VG, Kapatral V, Redkar RJ, Patra G, Mujer C, Los T, Ivanova N, Anderson I, Bhattacharyya A, Lykidis A, et al: The genome sequence of the facultative intracellular pathogen Brucella melitensis. Proc Natl Acad Sci USA. 2002, 99: 443-448. 10.1073\u002Fpnas.221575398.\nPaulsen IT, Seshadri R, Nelson KE, Eisen JA, Heidelberg JF, Read TD, Dodson RJ, Umayam L, Brinkac LM, Beanan MJ, et al: The Brucella suis genome reveals fundamental similarities between animal and plant pathogens and symbionts. Proc Natl Acad Sci USA. 2002, 99: 13148-13153. 10.1073\u002Fpnas.192319099.\nHalling SM, Peterson-Burch BD, Bricker BJ, Zuerner RL, Qing Z, Li LL, Kapur V, Alt DP, Olsen SC: Completion of the genome sequence of Brucella abortus and comparison to the highly similar genomes of Brucella melitensis and Brucella suis. J Bacteriol. 2005, 187: 2715-2726. 10.1128\u002FJB.187.8.2715-2726.2005.\nChain PS, Comerci DJ, Tolmasky ME, Larimer FW, Malfatti SA, Vergez LM, Aguero F, Land ML, Ugalde RA, Garcia E: Whole-genome analyses of speciation events in pathogenic brucellae. Infect Immun. 2005, 73: 8353-8361. 10.1128\u002FIAI.73.12.8353-8361.2005.\nBioPerl. [http:\u002F\u002Fwww.bioperl.org]\nStein LD, Mungall C, Shu S, Caudy M, Mangone M, Day A, Nickerson E, Stajich JE, Harris TW, Arva A, Lewis S: The generic genome browser: a building block for a model organism system database. Genome Res. 2002, 12: 1599-1610. 10.1101\u002Fgr.403602.\nWinsor GL, Lo R, Sui SJ, Ung KS, Huang S, Cheng D, Ching WK, Hancock RE, Brinkman FS: Pseudomonas aeruginosa Genome Database and PseudoCAP: facilitating community-based, continually updated, genome annotation. Nucleic Acids Res. 2005, 33: D338-343. 10.1093\u002Fnar\u002Fgki047.\nGee JM, Valderas MW, Kovach ME, Grippe VK, Robertson GT, Ng WL, Richardson JM, Winkler ME, Roop RM: The Brucella abortus Cu, Zn superoxide dismutase is required for optimal resistance to oxidative killing by murine macrophages and wild-type virulence in experimentally infected mice. Infect Immun. 2005, 73: 2873-2880. 10.1128\u002FIAI.73.5.2873-2880.2005.\nHe Y, Vemulapalli R, Schurig GG: Recombinant Ochrobactrum anthropi expressing Brucella abortus Cu, Zn superoxide dismutase protects mice against B. abortus infection only after switching of immune responses to Th1 type. Infect Immun. 2002, 70: 2535-2543. 10.1128\u002FIAI.70.5.2535-2543.2002.\nPassalacqua KD, Bergman NH, Herring-Palmer A, Hanna P: The superoxide dismutases of Bacillus anthracis do not cooperatively protect against endogenous superoxide stress. J Bacteriol. 2006, 188: 3837-3848. 10.1128\u002FJB.00239-06.\nNCBI CDD Download. [ftp:\u002F\u002Fftp.ncbi.nih.gov\u002Fpub\u002Fmmdb\u002Fcdd\u002Fcdd.tar.gz]\nNCBI Toolkit Download. [ftp:\u002F\u002Fftp.ncbi.nlm.nih.gov\u002Ftoolbox]\nMarchler-Bauer A, Panchenko AR, Shoemaker BA, Thiessen PA, Geer LY, Bryant SH: CDD: a database of conserved domain alignments with links to domain three-dimensional structure. Nucleic Acids Res. 2002, 30: 281-283. 10.1093\u002Fnar\u002F30.1.281.\nAgranoff D, Monahan IM, Mangan JA, Butcher PD, Krishna S: Mycobacterium tuberculosis expresses a novel pH-dependent divalent cation transporter belonging to the Nramp family. J Exp Med. 1999, 190: 717-724. 10.1084\u002Fjem.190.5.717.\nBoechat N, Lagier-Roger B, Petit S, Bordat Y, Rauzier J, Hance AJ, Gicquel B, Reyrat JM: Disruption of the gene homologous to mammalian Nramp1 in Mycobacterium tuberculosis does not affect virulence in mice. Infect Immun. 2002, 70: 4124-4131. 10.1128\u002FIAI.70.8.4124-4131.2002.\nZaharik ML, Cullen VL, Fung AM, Libby SJ, Kujat Choy SL, Coburn B, Kehres DG, Maguire ME, Fang FC, Finlay BB: The Salmonella enterica serovar typhimurium divalent cation transport systems MntH and SitABCD are essential for virulence in an Nramp1G169 murine typhoid model. Infect Immun. 2004, 72: 5522-5525. 10.1128\u002FIAI.72.9.5522-5525.2004.\nHayashi T, Makino K, Ohnishi M, Kurokawa K, Ishii K, Yokoyama K, Han CG, Ohtsubo E, Nakayama K, Murata T, et al: Complete genome sequence of enterohemorrhagic Escherichia coli O157:H7 and genomic comparison with a laboratory strain K-12. DNA Res. 2001, 8: 11-22. 10.1093\u002Fdnares\u002F8.1.11.\nNCBI BLAST Download. [http:\u002F\u002Fwww.ncbi.nih.gov\u002FBLAST\u002Fdownload.shtml]\nPathInfo Web Service. [http:\u002F\u002Fstaff.vbi.vt.edu\u002Fpathport\u002Fservices\u002Fwsdls\u002Fpathinfo.wsdl]\nForst CV: Host-pathogen systems biology. Drug Discov Today. 2006, 11: 220-227. 10.1016\u002FS1359-6446(05)03735-9.\nMINet Web Service. [http:\u002F\u002Fwww.vbi.vt.edu\u002F~pathport\u002Fservices\u002Fwsdls\u002Fpathway.wsdl]\nBrazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, Abeygunawardena N, Holloway E, Kapushesky M, Kemmeren P, Lara GG, et al: ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2003, 31: 68-71. 10.1093\u002Fnar\u002Fgkg091.\nPetersen R: Linux: The Complete Reference. 2000, Emeryville, CA: McGraw-Hill Osborne Media, 4\nWinnenburg R, Baldwin TK, Urban M, Rawlings C, Kohler J, Hammond-Kosack KE: PHI-base: a new database for pathogen host interactions. 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