04620nas a2201141 4500008004100000022001400041245011000055210006900165260000900234300001200243490000700255520126600262653002401528653001301552653002301565653001101588653001501599653002001614100001901634700002301653700002201676700002101698700002001719700002301739700002101762700002601783700001901809700002001828700001901848700002201867700002301889700002401912700001701936700002101953700001701974700001601991700002402007700002502031700002502056700002302081700002302104700002702127700002402154700001602178700002102194700002602215700002502241700002102266700002102287700001902308700003202327700002102359700002202380700002002402700001902422700002602441700001802467700001802485700002302503700002102526700001902547700001902566700002202585700001802607700001902625700001802644700002302662700001802685700002002703700001902723700003302742700002602775700002702801700002502828700002302853700001802876700002302894700001702917700002402934700001802958700002202976700002502998700002003023700002303043700002003066700002603086700002003112700001903132700002203151700002203173700002003195700002303215700002103238700001803259700002403277710003503301856014203336 2024 eng d a1664-322400aDrug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.0 aDrugtarget identification in COVID19 disease mechanisms using co c2023 a12828590 v143 a
INTRODUCTION: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.
METHODS: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.
RESULTS: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.
DISCUSSION: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
10aComputer Simulation10aCOVID-1910adrug repositioning10aHumans10aSARS-CoV-210aSystems biology1 aNiarakis, Anna1 aOstaszewski, Marek1 aMazein, Alexander1 aKuperstein, Inna1 aKutmon, Martina1 aGillespie, Marc, E1 aFunahashi, Akira1 aAcencio, Marcio, Luis1 aHemedan, Ahmed1 aAichem, Michael1 aKlein, Karsten1 aCzauderna, Tobias1 aBurtscher, Felicia1 aYamada, Takahiro, G1 aHiki, Yusuke1 aHiroi, Noriko, F1 aHu, Finterly1 aPham, Nhung1 aEhrhart, Friederike1 aWillighagen, Egon, L1 aValdeolivas, Alberto1 aDugourd, Aurélien1 aMessina, Francesco1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aSoliman, Sylvain1 aAghamiri, Sara, Sadat1 aPuniya, Bhanwar, Lal1 aNaldi, Aurélien1 aHelikar, Tomáš1 aSingh, Vidisha1 aFernández, Marco, Fariñas1 aBermudez, Viviam1 aTsirvouli, Eirini1 aMontagud, Arnau1 aNoël, Vincent1 aPonce-de-Leon, Miguel1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aLuna, Augustin1 aPiñero, Janet1 aFurlong, Laura, I1 aBalaur, Irina1 aRougny, Adrien1 aJarosz, Yohan1 aOverall, Rupert, W1 aPhair, Robert1 aPerfetto, Livia1 aMatthews, Lisa1 aRex, Devasahayam, Arokia Bal1 aOrlic-Milacic, Marija1 aGomez, Luis, Cristobal1 aDe Meulder, Bertrand1 aRavel, Jean, Marie1 aJassal, Bijay1 aSatagopam, Venkata1 aWu, Guanming1 aGolebiewski, Martin1 aGawron, Piotr1 aCalzone, Laurence1 aBeckmann, Jacques, S1 aEvelo, Chris, T1 aD'Eustachio, Peter1 aSchreiber, Falk1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aKuiper, Martin1 aValencia, Alfonso1 aWolkenhauer, Olaf1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttp://clinbioinfosspa.es/content/drug-target-identification-covid-19-disease-mechanisms-using-computational-systems-biology-approaches-002674nas a2200337 4500008004100000022001400041245011500055210006900170260001600239520155800255100001601813700001901829700002001848700001901868700003101887700002201918700002501940700001701965700001701982700001901999700002102018700002702039700002002066700002202086700002202108700001902130700002002149700002102169710002002190856012602210 2022 eng d a1399-000400aCIBERER: Spanish National Network for Research on Rare Diseases: a highly productive collaborative initiative.0 aCIBERER Spanish National Network for Research on Rare Diseases a c2022 Jan 203 aCIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on Rare Diseases currently consists of 75 research groups belonging to universities, research centers and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical and cellular research of rare diseases. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this paper, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions towards the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to rare disease research. This article is protected by copyright. All rights reserved.
1 aLuque, Juan1 aMendes, Ingrid1 aGómez, Beatriz1 aMorte, Beatriz1 ade Heredia, Miguel, López1 aHerreras, Enrique1 aCorrochano, Virginia1 aBueren, Juan1 aGallano, Pia1 aArtuch, Rafael1 aFillat, Cristina1 aPérez-Jurado, Luis, A1 aMontoliu, Lluis1 aCarracedo, Ángel1 aMillán, José, M1 aWebb, Susan, M1 aPalau, Francesc1 aLapunzina, Pablo1 aCIBERER Network uhttp://clinbioinfosspa.es/content/ciberer-spanish-national-network-research-rare-diseases-highly-productive-collaborative07188nas a2202077 4500008004100000022001400041245010000055210006900155260001200224300001100236490000700247520130900254653002101563653002601584653002201610653001301632653001401645653001601659653002301675653003101698653003201729653001101761653002301772653002201795653002101817653001601838653003601854653001801890653003201908653001501940653002401955653001301979653002601992653001902018100002302037700001902060700002202079700002102101700001802122700002702140700001902167700002602186700002602212700001802238700001902256700001702275700002202292700002102314700001902335700002902354700001802383700001602401700002902417700001802446700002302464700002202487700002002509700002002529700002702549700002102576700001702597700001802614700002402632700002102656700001602677700002102693700001902714700002302733700002302756700002002779700001702799700001902816700002402835700002102859700002402880700001702904700002302921700002402944700001902968700002002987700001903007700001903026700002903045700002303074700002003097700001703117700001803134700002403152700003303176700002003209700002003229700001603249700001503265700001803280700001903298700002603317700002703343700002003370700002403390700001803414700002403432700002203456700002203478700001903500700002003519700002103539700001803560700001503578700002203593700002303615700001703638700001903655700002403674700001703698700001803715700001703733700002303750700001803773700001803791700002303809700002103832700001703853700002203870700002003892700001803912700001803930700002303948700002103971700002503992700001804017700002004035700001704055700001904072700001704091700002104108700002504129700002704154700002404181700001604205700002104221700002504242700001704267700002004284700001804304700002504322700002404347700002304371700002104394700001904415700002204434700001804456700001604474700002204490700002004512700001804532700002504550700001904575700002204594700002404616700002304640700002004663700002304683700002204706700002304728700002304751700002604774700002004800700002204820700002004842700002304862700002104885700001804906700002404924710003504948856012704983 2021 eng d a1744-429200aCOVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.0 aCOVID19 Disease Map a computational knowledge repository of viru c2021 10 ae103870 v173 aWe need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
10aAntiviral Agents10aComputational Biology10aComputer Graphics10aCOVID-1910aCytokines10aData Mining10aDatabases, Factual10aGene Expression Regulation10aHost Microbial Interactions10aHumans10aImmunity, Cellular10aImmunity, Humoral10aImmunity, Innate10aLymphocytes10aMetabolic Networks and Pathways10aMyeloid Cells10aProtein Interaction Mapping10aSARS-CoV-210aSignal Transduction10aSoftware10aTranscription Factors10aViral Proteins1 aOstaszewski, Marek1 aNiarakis, Anna1 aMazein, Alexander1 aKuperstein, Inna1 aPhair, Robert1 aOrta-Resendiz, Aurelio1 aSingh, Vidisha1 aAghamiri, Sara, Sadat1 aAcencio, Marcio, Luis1 aGlaab, Enrico1 aRuepp, Andreas1 aFobo, Gisela1 aMontrone, Corinna1 aBrauner, Barbara1 aFrishman, Goar1 aGómez, Luis, Cristóbal1 aSomers, Julia1 aHoch, Matti1 aGupta, Shailendra, Kumar1 aScheel, Julia1 aBorlinghaus, Hanna1 aCzauderna, Tobias1 aSchreiber, Falk1 aMontagud, Arnau1 ade Leon, Miguel, Ponce1 aFunahashi, Akira1 aHiki, Yusuke1 aHiroi, Noriko1 aYamada, Takahiro, G1 aDräger, Andreas1 aRenz, Alina1 aNaveez, Muhammad1 aBocskei, Zsolt1 aMessina, Francesco1 aBörnigen, Daniela1 aFergusson, Liam1 aConti, Marta1 aRameil, Marius1 aNakonecnij, Vanessa1 aVanhoefer, Jakob1 aSchmiester, Leonard1 aWang, Muying1 aAckerman, Emily, E1 aShoemaker, Jason, E1 aZucker, Jeremy1 aOxford, Kristie1 aTeuton, Jeremy1 aKocakaya, Ebru1 aSummak, Gökçe, Yağmur1 aHanspers, Kristina1 aKutmon, Martina1 aCoort, Susan1 aEijssen, Lars1 aEhrhart, Friederike1 aRex, Devasahayam, Arokia Bal1 aSlenter, Denise1 aMartens, Marvin1 aPham, Nhung1 aHaw, Robin1 aJassal, Bijay1 aMatthews, Lisa1 aOrlic-Milacic, Marija1 aRibeiro, Andrea, Senff1 aRothfels, Karen1 aShamovsky, Veronica1 aStephan, Ralf1 aSevilla, Cristoffer1 aVarusai, Thawfeek1 aRavel, Jean-Marie1 aFraser, Rupsha1 aOrtseifen, Vera1 aMarchesi, Silvia1 aGawron, Piotr1 aSmula, Ewa1 aHeirendt, Laurent1 aSatagopam, Venkata1 aWu, Guanming1 aRiutta, Anders1 aGolebiewski, Martin1 aOwen, Stuart1 aGoble, Carole1 aHu, Xiaoming1 aOverall, Rupert, W1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aVega, Carlos1 aGrouès, Valentin1 aVazquez, Miguel1 aPorras, Pablo1 aLicata, Luana1 aIannuccelli, Marta1 aSacco, Francesca1 aNesterova, Anastasia1 aYuryev, Anton1 ade Waard, Anita1 aTurei, Denes1 aLuna, Augustin1 aBabur, Ozgun1 aSoliman, Sylvain1 aValdeolivas, Alberto1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aHelikar, Tomáš1 aPuniya, Bhanwar, Lal1 aModos, Dezso1 aTreveil, Agatha1 aOlbei, Marton1 aDe Meulder, Bertrand1 aBallereau, Stephane1 aDugourd, Aurélien1 aNaldi, Aurélien1 aNoël, Vincent1 aCalzone, Laurence1 aSander, Chris1 aDemir, Emek1 aKorcsmaros, Tamas1 aFreeman, Tom, C1 aAugé, Franck1 aBeckmann, Jacques, S1 aHasenauer, Jan1 aWolkenhauer, Olaf1 aWilighagen, Egon, L1 aPico, Alexander, R1 aEvelo, Chris, T1 aGillespie, Marc, E1 aStein, Lincoln, D1 aHermjakob, Henning1 aD'Eustachio, Peter1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aValencia, Alfonso1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttp://clinbioinfosspa.es/content/covid19-disease-map-computational-knowledge-repository-virus-host-interaction-mechanisms01023nas a2200313 4500008004100000022001400041245008100055210006900136260001200205300001400217490000700231653001500238653002600253653002400279653001100303653002300314653002000337653003200357100001500389700002000404700002600424700001700450700002400467700002100491700002600512700002500538710004000563856010600603 2021 eng d a1548-710500aDOME: recommendations for supervised machine learning validation in biology.0 aDOME recommendations for supervised machine learning validation c2021 10 a1122-11270 v1810aAlgorithms10aComputational Biology10aGuidelines as Topic10aHumans10aModels, Biological10aResearch Design10aSupervised Machine Learning1 aWalsh, Ian1 aFishman, Dmytro1 aGarcia-Gasulla, Dario1 aTitma, Tiina1 aPollastri, Gianluca1 aHarrow, Jennifer1 aPsomopoulos, Fotis, E1 aTosatto, Silvio, C E1 aELIXIR Machine Learning Focus Group uhttp://clinbioinfosspa.es/content/dome-recommendations-supervised-machine-learning-validation-biology03248nas a2201189 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2021 eng d a1061-403600aThe NCI Genomic Data Commons0 aNCI Genomic Data Commons cOct-02-20221 aHeath, Allison, P.1 aFerretti, Vincent1 aAgrawal, Stuti1 aAn, Maksim1 aAngelakos, James, C.1 aArya, Renuka1 aBajari, Rosita1 aBaqar, Bilal1 aBarnowski, Justin, H. B.1 aBurt, Jeffrey1 aCatton, Ann1 aChan, Brandon, F.1 aChu, Fay1 aCullion, Kim1 aDavidsen, Tanja1 aDo, Phuong-My1 aDompierre, Christian1 aFerguson, Martin, L.1 aFitzsimons, Michael, S.1 aFord, Michael1 aFukuma, Miyuki1 aGaheen, Sharon1 aGanji, Gajanan, L.1 aGarcia, Tzintzuni, I.1 aGeorge, Sameera, S.1 aGerhard, Daniela, S.1 aGerthoffert, Francois1 aGomez, Fauzi1 aHan, Kang1 aHernandez, Kyle, M.1 aIssac, Biju1 aJackson, Richard1 aJensen, Mark, A.1 aJoshi, Sid1 aKadam, Ajinkya1 aKhurana, Aishmit1 aKim, Kyle, M. J.1 aKraft, Victoria, E.1 aLi, Shenglai1 aLichtenberg, Tara, M.1 aLodato, Janice1 aLolla, Laxmi1 aMartinov, Plamen1 aMazzone, Jeffrey, A.1 aMiller, Daniel, P.1 aMiller, Ian1 aMiller, Joshua, S.1 aMiyauchi, Koji1 aMurphy, Mark, W.1 aNullet, Thomas1 aOgwara, Rowland, O.1 aOrtuño, Francisco, M.1 aPedrosa, Jesús1 aPham, Phuong, L.1 aPopov, Maxim, Y.1 aPorter, James, J.1 aPowell, Raymond1 aRademacher, Karl1 aReid, Colin, P.1 aRich, Samantha1 aRogel, Bessie1 aSahni, Himanso1 aSavage, Jeremiah, H.1 aSchmitt, Kyle, A.1 aSimmons, Trevar, J.1 aSislow, Joseph1 aSpring, Jonathan1 aStein, Lincoln1 aSullivan, Sean1 aTang, Yajing1 aThiagarajan, Mathangi1 aTroyer, Heather, D.1 aWang, Chang1 aWang, Zhining1 aWest, Bedford, L.1 aWilmer, Alex1 aWilson, Shane1 aWu, Kaman1 aWysocki, William, P.1 aXiang, Linda1 aYamada, Joseph, T.1 aYang, Liming1 aYu, Christine1 aYung, Christina, K.1 aZenklusen, Jean, Claude1 aZhang, Junjun1 aZhang, Zhenyu1 aZhao, Yuanheng1 aZubair, Ariz1 aStaudt, Louis, M.1 aGrossman, Robert, L. uhttp://www.nature.com/articles/s41588-021-00791-505404nas a2201477 4500008004100000022001400041245007800055210006900133260001200202300001400214490000700228520122900235653002601464653001401490653001101504653001501515653003501530653002001565653003801585100001901623700001901642700001801661700002301679700002401702700002301726700002101749700002801770700001801798700002501816700002401841700002701865700001801892700002301910700002001933700001501953700002201968700002001990700002702010700002102037700002202058700001802080700001902098700002702117700002002144700001902164700002002183700002102203700001702224700002102241700002302262700002002285700002202305700002002327700001802347700002302365700001802388700002102406700002602427700001902453700001702472700001802489700002402507700001902531700001602550700001702566700001602583700002102599700002102620700002102641700002102662700002202683700002202705700002102727700001602748700001702764700001502781700002102796700001702817700002402834700002202858700002002880700002402900700001802924700001602942700002302958700002102981700002403002700001703026700002603043700002403069700002503093700002403118700002203142700002103164700001703185700001903202700001803221700001803239700001303257700001703270700001603287700001903303700002703322700002003349700001703369700002203386700001903408700001903427700002603446700001903472700002903491700002103520700001603541700002203557700001603579700002003595700002403615700001903639700002403658700002203682700002003704700001803724710003303742710004903775856010203824 2021 eng d a1546-170X00aReporting guidelines for human microbiome research: the STORMS checklist.0 aReporting guidelines for human microbiome research the STORMS ch c2021 11 a1885-18920 v273 aThe particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.
10aComputational Biology10aDysbiosis10aHumans10aMicrobiota10aObservational Studies as Topic10aResearch Design10aTranslational Science, Biomedical1 aMirzayi, Chloe1 aRenson, Audrey1 aZohra, Fatima1 aElsafoury, Shaimaa1 aGeistlinger, Ludwig1 aKasselman, Lora, J1 aEckenrode, Kelly1 avan de Wijgert, Janneke1 aLoughman, Amy1 aMarques, Francine, Z1 aMacIntyre, David, A1 aArumugam, Manimozhiyan1 aAzhar, Rimsha1 aBeghini, Francesco1 aBergstrom, Kirk1 aBhatt, Ami1 aBisanz, Jordan, E1 aBraun, Jonathan1 aBravo, Hector, Corrada1 aBuck, Gregory, A1 aBushman, Frederic1 aCasero, David1 aClarke, Gerard1 aCollado, Maria, Carmen1 aCotter, Paul, D1 aCryan, John, F1 aDemmer, Ryan, T1 aDevkota, Suzanne1 aElinav, Eran1 aEscobar, Juan, S1 aFettweis, Jennifer1 aFinn, Robert, D1 aFodor, Anthony, A1 aForslund, Sofia1 aFranke, Andre1 aFurlanello, Cesare1 aGilbert, Jack1 aGrice, Elizabeth1 aHaibe-Kains, Benjamin1 aHandley, Scott1 aHerd, Pamela1 aHolmes, Susan1 aJacobs, Jonathan, P1 aKarstens, Lisa1 aKnight, Rob1 aKnights, Dan1 aKoren, Omry1 aKwon, Douglas, S1 aLangille, Morgan1 aLindsay, Brianna1 aMcGovern, Dermot1 aMcHardy, Alice, C1 aMcWeeney, Shannon1 aMueller, Noel, T1 aNezi, Luigi1 aOlm, Matthew1 aPalm, Noah1 aPasolli, Edoardo1 aRaes, Jeroen1 aRedinbo, Matthew, R1 aRühlemann, Malte1 aSartor, Balfour1 aSchloss, Patrick, D1 aSchriml, Lynn1 aSegal, Eran1 aShardell, Michelle1 aSharpton, Thomas1 aSmirnova, Ekaterina1 aSokol, Harry1 aSonnenburg, Justin, L1 aSrinivasan, Sujatha1 aThingholm, Louise, B1 aTurnbaugh, Peter, J1 aUpadhyay, Vaibhav1 aWalls, Ramona, L1 aWilmes, Paul1 aYamada, Takuji1 aZeller, Georg1 aZhang, Mingyu1 aZhao, Ni1 aZhao, Liping1 aBao, Wenjun1 aCulhane, Aedin1 aDevanarayan, Viswanath1 aDopazo, Joaquin1 aFan, Xiaohui1 aFischer, Matthias1 aJones, Wendell1 aKusko, Rebecca1 aMason, Christopher, E1 aMercer, Tim, R1 aSansone, Susanna-Assunta1 aScherer, Andreas1 aShi, Leming1 aThakkar, Shraddha1 aTong, Weida1 aWolfinger, Russ1 aHunter, Christopher1 aSegata, Nicola1 aHuttenhower, Curtis1 aDowd, Jennifer, B1 aJones, Heidi, E1 aWaldron, Levi1 aGenomic Standards Consortium1 aMassive Analysis and Quality Control Society uhttp://clinbioinfosspa.es/content/reporting-guidelines-human-microbiome-research-storms-checklist02932nas a2200613 4500008004100000022001400041245012600055210006900181260001500250300001500265490000700280520120500287653001801492653001101510653001301521653001101534653002101545653000901566653001401575653002001589653001301609653001501622653001801637100001301655700002401668700001201692700001701704700001601721700001701737700001601754700001601770700001201786700001401798700001601812700001501828700002101843700002101864700002201885700001501907700002301922700002101945700002101966700001601987700001602003700002102019700002502040700001902065700001702084700001402101700001802115700002602133710003102159856012802190 2020 eng d a2405-472000aCommunity Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.0 aCommunity Assessment of the Predictability of Cancer Protein and c2020 08 26 a186-195.e90 v113 aCancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.
10aCrowdsourcing10aFemale10aGenomics10aHumans10aMachine Learning10aMale10aNeoplasms10aPhosphoproteins10aProteins10aProteomics10aTranscriptome1 aYang, Mi1 aPetralia, Francesca1 aLi, Zhi1 aLi, Hongyang1 aMa, Weiping1 aSong, Xiaoyu1 aKim, Sunkyu1 aLee, Heewon1 aYu, Han1 aLee, Bora1 aBae, Seohui1 aHeo, Eunji1 aKaczmarczyk, Jan1 aStępniak, Piotr1 aWarchoł, Michał1 aYu, Thomas1 aCalinawan, Anna, P1 aBoutros, Paul, C1 aPayne, Samuel, H1 aReva, Boris1 aBoja, Emily1 aRodriguez, Henry1 aStolovitzky, Gustavo1 aGuan, Yuanfang1 aKang, Jaewoo1 aWang, Pei1 aFenyö, David1 aSaez-Rodriguez, Julio1 aNCI-CPTAC-DREAM Consortium uhttp://clinbioinfosspa.es/content/community-assessment-predictability-cancer-protein-and-phosphoprotein-levels-genomics-and01793nas a2200577 4500008004100000022001400041245011100055210006900166260001500235300000800250490000600258653002000264653002600284653002700310653001300337653002300350653003200373653003100405653001100436653003000447653002300477653001400500653002100514653001500535100002300550700002200573700002300595700002100618700001900639700002300658700002300681700002500704700002000729700001900749700002000768700002100788700001600809700002200825700002200847700002300869700002000892700002700912700002300939700002200962700002100984700002001005700002101025700001801046700002401064856012701088 2020 eng d a2052-446300aCOVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.0 aCOVID19 Disease Map building a computational repository of SARSC c2020 05 05 a1360 v710aBetacoronavirus10aComputational Biology10aCoronavirus Infections10aCOVID-1910aDatabases, Factual10aHost Microbial Interactions10aHost-Pathogen Interactions10aHumans10aInternational Cooperation10aModels, Biological10aPandemics10aPneumonia, Viral10aSARS-CoV-21 aOstaszewski, Marek1 aMazein, Alexander1 aGillespie, Marc, E1 aKuperstein, Inna1 aNiarakis, Anna1 aHermjakob, Henning1 aPico, Alexander, R1 aWillighagen, Egon, L1 aEvelo, Chris, T1 aHasenauer, Jan1 aSchreiber, Falk1 aDräger, Andreas1 aDemir, Emek1 aWolkenhauer, Olaf1 aFurlong, Laura, I1 aBarillot, Emmanuel1 aDopazo, Joaquin1 aOrta-Resendiz, Aurelio1 aMessina, Francesco1 aValencia, Alfonso1 aFunahashi, Akira1 aKitano, Hiroaki1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard uhttp://clinbioinfosspa.es/content/covid-19-disease-map-building-computational-repository-sars-cov-2-virus-host-interaction01358nas a2200433 4500008004100000022001400041245006500055210006400120260001200184300001200196490000800208653001500216653002800231653003100259100002600290700002800316700001700344700002600361700001800387700001300405700002000418700002100438700002000459700002100479700002200500700002400522700002100546700002200567700002000589700001800609700001900627700002300646700001900669700002500688700002200713700002300735710007100758856009500829 2020 eng d a1476-468700aTransparency and reproducibility in artificial intelligence.0 aTransparency and reproducibility in artificial intelligence c2020 10 aE14-E160 v58610aAlgorithms10aArtificial Intelligence10aReproducibility of Results1 aHaibe-Kains, Benjamin1 aAdam, George, Alexandru1 aHosny, Ahmed1 aKhodakarami, Farnoosh1 aWaldron, Levi1 aWang, Bo1 aMcIntosh, Chris1 aGoldenberg, Anna1 aKundaje, Anshul1 aGreene, Casey, S1 aBroderick, Tamara1 aHoffman, Michael, M1 aLeek, Jeffrey, T1 aKorthauer, Keegan1 aHuber, Wolfgang1 aBrazma, Alvis1 aPineau, Joelle1 aTibshirani, Robert1 aHastie, Trevor1 aIoannidis, John, P A1 aQuackenbush, John1 aAerts, Hugo, J W L1 aMassive Analysis Quality Control (MAQC) Society Board of Directors uhttp://clinbioinfosspa.es/content/transparency-and-reproducibility-artificial-intelligence03264nas a2200685 4500008004100000022001400041245011800055210006900173260001500242300000900257490000700266520115400273653001901427653005101446653001701497653002201514653002101536653002601557653002201583653002001605653003001625653001901655653001301674653001101687653003101698653001301729653001401742653002101756653003501777653004101812653002201853100002301875700001701898700001901915700001801934700002301952700001901975700001501994700001702009700001602026700002002042700001602062700002402078700001902102700001802121700002402139700001802163700002502181700001702206700001902223700002302242700002702265700002002292700002502312700002002337700002102357700002602378710005702404856011702461 2019 eng d a2041-172300aCommunity assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.0 aCommunity assessment to advance computational prediction of canc c2019 06 17 a26740 v103 aThe effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
10aADAM17 Protein10aAntineoplastic Combined Chemotherapy Protocols10aBenchmarking10aBiomarkers, Tumor10aCell Line, Tumor10aComputational Biology10aDatasets as Topic10aDrug Antagonism10aDrug Resistance, Neoplasm10aDrug Synergism10aGenomics10aHumans10aMolecular Targeted Therapy10amutation10aNeoplasms10apharmacogenetics10aPhosphatidylinositol 3-Kinases10aPhosphoinositide-3 Kinase Inhibitors10aTreatment Outcome1 aMenden, Michael, P1 aWang, Dennis1 aMason, Mike, J1 aSzalai, Bence1 aBulusu, Krishna, C1 aGuan, Yuanfang1 aYu, Thomas1 aKang, Jaewoo1 aJeon, Minji1 aWolfinger, Russ1 aNguyen, Tin1 aZaslavskiy, Mikhail1 aJang, In, Sock1 aGhazoui, Zara1 aAhsen, Mehmet, Eren1 aVogel, Robert1 aNeto, Elias, Chaibub1 aNorman, Thea1 aK Y Tang, Eric1 aGarnett, Mathew, J1 aDi Veroli, Giovanni, Y1 aFawell, Stephen1 aStolovitzky, Gustavo1 aGuinney, Justin1 aDry, Jonathan, R1 aSaez-Rodriguez, Julio1 aAstraZeneca-Sanger Drug Combination DREAM Consortium uhttp://clinbioinfosspa.es/content/community-assessment-advance-computational-prediction-cancer-drug-combinations01484nas a2200421 4500008004100000245011900041210006900160260001600229490000600245110005300251700001800304700001800322700002300340700002500363700001600388700001900404700001500423700001800438700001900456700002400475700001900499700002200518700001600540700003100556700001800587700001800605700001800623700001900641700002200660700002300682700002700705700002700732700001900759700002400778700002500802700002600827856020900853 2018 eng d00aA crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection0 acrowdsourced analysis to identify ab initio molecular signatures cJan-12-20180 v91 aThe Respiratory Viral DREAM Challenge Consortium1 aFourati, Slim1 aTalla, Aarthi1 aMahmoudian, Mehrad1 aBurkhart, Joshua, G.1 aKlén, Riku1 aHenao, Ricardo1 aYu, Thomas1 aAydın, Zafer1 aYeung, Ka, Yee1 aAhsen, Mehmet, Eren1 aAlmugbel, Reem1 aJahandideh, Samad1 aLiang, Xiao1 aNordling, Torbjörn, E. M.1 aShiga, Motoki1 aStanescu, Ana1 aVogel, Robert1 aPandey, Gaurav1 aChiu, Christopher1 aMcClain, Micah, T.1 aWoods, Christopher, W.1 aGinsburg, Geoffrey, S.1 aElo, Laura, L.1 aTsalik, Ephraim, L.1 aMangravite, Lara, M.1 aSieberts, Solveig, K. uhttp://www.nature.com/articles/s41467-018-06735-8http://www.nature.com/articles/s41467-018-06735-8.pdfhttp://www.nature.com/articles/s41467-018-06735-8.pdfhttp://www.nature.com/articles/s41467-018-06735-802576nas a2200517 4500008004100000022001400041245005200055210005100107260001500158300001200173490000800185520112500193653002301318653001701341653001101358653002001369653002501389653002301414653001801437653001301455653001501468653001701483653002101500653002001521653002701541653001401568100002401582700001801606700002201624700002801646700002601674700002101700700002001721700002401741700003101765700002001796700001701816700001801833700002401851700002101875700002001896700002601916700002301942700001801965856007501983 2018 eng d a1476-468700aGenomics of the origin and evolution of Citrus.0 aGenomics of the origin and evolution of Citrus c2018 02 15 a311-3160 v5543 aThe genus Citrus, comprising some of the most widely cultivated fruit crops worldwide, includes an uncertain number of species. Here we describe ten natural citrus species, using genomic, phylogenetic and biogeographic analyses of 60 accessions representing diverse citrus germ plasms, and propose that citrus diversified during the late Miocene epoch through a rapid southeast Asian radiation that correlates with a marked weakening of the monsoons. A second radiation enabled by migration across the Wallace line gave rise to the Australian limes in the early Pliocene epoch. Further identification and analyses of hybrids and admixed genomes provides insights into the genealogy of major commercial cultivars of citrus. Among mandarins and sweet orange, we find an extensive network of relatedness that illuminates the domestication of these groups. Widespread pummelo admixture among these mandarins and its correlation with fruit size and acidity suggests a plausible role of pummelo introgression in the selection of palatable mandarins. This work provides a new evolutionary framework for the genus Citrus.
10aAsia, Southeastern10aBiodiversity10acitrus10aCrop Production10aEvolution, Molecular10aGenetic Speciation10aGenome, Plant10aGenomics10aHaplotypes10aHeterozygote10aHistory, Ancient10aHuman Migration10aHybridization, Genetic10aPhylogeny1 aWu, Guohong, Albert1 aTerol, Javier1 aIbañez, Victoria1 aLópez-García, Antonio1 aPérez-Román, Estela1 aBorredá, Carles1 aDomingo, Concha1 aTadeo, Francisco, R1 aCarbonell-Caballero, José1 aAlonso, Roberto1 aCurk, Franck1 aDu, Dongliang1 aOllitrault, Patrick1 aRoose, Mikeal, L1 aDopazo, Joaquin1 aGmitter, Frederick, G1 aRokhsar, Daniel, S1 aTalon, Manuel uhttp://clinbioinfosspa.es/content/genomics-origin-and-evolution-citrus03008nas a2200433 4500008004100000022001400041245016000055210006900215260001300284300001200297490000700309520160000316653001601916653003801932653001501970653001701985653001902002653002702021653001502048653002602063653002602089653001002115100002402125700002402149700001902173700002002192700002202212700002102234700002202255700002902277700002002306700001802326700002002344700002402364700001902388700002102407700001902428856012702447 2017 eng d a1573-502800aIntegration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.).0 aIntegration of transcriptomic and metabolic data reveals hub tra c2017 Jul a549-5640 v943 aBy integration of transcriptional and metabolic profiles we identified pathways and hubs transcription factors regulated during drought conditions in sunflower, useful for applications in molecular and/or biotechnological breeding. Drought is one of the most important environmental stresses that effects crop productivity in many agricultural regions. Sunflower is tolerant to drought conditions but the mechanisms involved in this tolerance remain unclear at the molecular level. The aim of this study was to characterize and integrate transcriptional and metabolic pathways related to drought stress in sunflower plants, by using a system biology approach. Our results showed a delay in plant senescence with an increase in the expression level of photosynthesis related genes as well as higher levels of sugars, osmoprotectant amino acids and ionic nutrients under drought conditions. In addition, we identified transcription factors that were upregulated during drought conditions and that may act as hubs in the transcriptional network. Many of these transcription factors belong to families implicated in the drought response in model species. The integration of transcriptomic and metabolomic data in this study, together with physiological measurements, has improved our understanding of the biological responses during droughts and contributes to elucidate the molecular mechanisms involved under this environmental condition. These findings will provide useful biotechnological tools to improve stress tolerance while maintaining crop yield under restricted water availability.
10aChlorophyll10aGene Expression Regulation, Plant10aHelianthus10aPlant Leaves10aPlant Proteins10aProtein Array Analysis10aRNA, Plant10aStress, Physiological10aTranscription Factors10aWater1 aMoschen, Sebastián1 aDi Rienzo, Julio, A1 aHiggins, Janet1 aTohge, Takayuki1 aWatanabe, Mutsumi1 aGonzalez, Sergio1 aRivarola, Máximo1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aHopp, Esteban1 aHoefgen, Rainer1 aFernie, Alisdair, R1 aPaniego, Norma1 aFernandez, Paula1 aHeinz, Ruth, A uhttp://clinbioinfosspa.es/content/integration-transcriptomic-and-metabolic-data-reveals-hub-transcription-factors-involved02094nas a2200349 4500008004100000022001400041245011200055210006900167260000900236300001000245490000600255520101800261100002001279700003401299700002701333700002701360700002201387700002301409700002401432700002101456700001901477700002001496700002301516700002201539700001901561700003301580700002001613700002301633700002001656700002101676856004701697 2016 eng d a2041-172300aExtension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq).0 aExtension of human lncRNA transcripts by RACE coupled with longr c2016 a123390 v73 aLong non-coding RNAs (lncRNAs) constitute a large, yet mostly uncharacterized fraction of the mammalian transcriptome. Such characterization requires a comprehensive, high-quality annotation of their gene structure and boundaries, which is currently lacking. Here we describe RACE-Seq, an experimental workflow designed to address this based on RACE (rapid amplification of cDNA ends) and long-read RNA sequencing. We apply RACE-Seq to 398 human lncRNA genes in seven tissues, leading to the discovery of 2,556 on-target, novel transcripts. About 60% of the targeted loci are extended in either 5’ or 3’, often reaching genomic hallmarks of gene boundaries. Analysis of the novel transcripts suggests that lncRNAs are as long, have as many exons and undergo as much alternative splicing as protein-coding genes, contrary to current assumptions. Overall, we show that RACE-Seq is an effective tool to annotate an organism’s deep transcriptome, and compares favourably to other targeted sequencing techniques.1 aLagarde, Julien1 aUszczynska-Ratajczak, Barbara1 aSantoyo-López, Javier1 aGonzalez, Jose, Manuel1 aTapanari, Electra1 aMudge, Jonathan, M1 aSteward, Charles, A1 aWilming, Laurens1 aTanzer, Andrea1 aHowald, Cédric1 aChrast, Jacqueline1 aVela-Boza, Alicia1 aRueda, Antonio1 aLópez-Domingo, Francisco, J1 aDopazo, Joaquin1 aReymond, Alexandre1 aGuigó, Roderic1 aHarrow, Jennifer uhttp://www.nature.com/articles/ncomms1233901158nas a2200313 4500008004100000245011100041210006900152260001600221490000600237100002000243700003400263700002700297700002700324700002200351700002400373700002500397700002100422700001900443700002000462700002300482700002200505700001900527700003300546700002000579700002300599700002000622700002100642856018100663 2016 eng d00aExtension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq)0 aExtension of human lncRNA transcripts by RACE coupled with longr cJan-11-20160 v71 aLagarde, Julien1 aUszczynska-Ratajczak, Barbara1 aSantoyo-López, Javier1 aGonzalez, Jose, Manuel1 aTapanari, Electra1 aMudge, Jonathan, M.1 aSteward, Charles, A.1 aWilming, Laurens1 aTanzer, Andrea1 aHowald, Cédric1 aChrast, Jacqueline1 aVela-Boza, Alicia1 aRueda, Antonio1 aLopez-Domingo, Francisco, J.1 aDopazo, Joaquin1 aReymond, Alexandre1 aGuigó, Roderic1 aHarrow, Jennifer uhttp://www.nature.com/articles/ncomms12339http://www.nature.com/articles/ncomms12339.pdfhttp://www.nature.com/articles/ncomms12339.pdfhttp://www.nature.com/articles/ncomms1233903249nas a2200493 4500008004100000022001400041245010800055210006900163260001300232300001100245490000700256520165600263653004101919653003001960653003801990653001802028653001702046653001502063653000902078653001702087653004402104653001702148653003302165653001902198653002602217100002402243700002602267700002402293700002802317700002002345700002202365700002102387700002102408700002202429700002902451700002002480700002702500700002002527700002402547700001902571700002102590700001902611856012502630 2016 eng d a1467-765200aIntegrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.0 aIntegrating transcriptomic and metabolomic analysis to understan c2016 Feb a719-340 v143 aLeaf senescence is a complex process, which has dramatic consequences on crop yield. In sunflower, gap between potential and actual yields reveals the economic impact of senescence. Indeed, sunflower plants are incapable of maintaining their green leaf area over sustained periods. This study characterizes the leaf senescence process in sunflower through a systems biology approach integrating transcriptomic and metabolomic analyses: plants being grown under both glasshouse and field conditions. Our results revealed a correspondence between profile changes detected at the molecular, biochemical and physiological level throughout the progression of leaf senescence measured at different plant developmental stages. Early metabolic changes were detected prior to anthesis and before the onset of the first senescence symptoms, with more pronounced changes observed when physiological and molecular variables were assessed under field conditions. During leaf development, photosynthetic activity and cell growth processes decreased, whereas sucrose, fatty acid, nucleotide and amino acid metabolisms increased. Pathways related to nutrient recycling processes were also up-regulated. Members of the NAC, AP2-EREBP, HB, bZIP and MYB transcription factor families showed high expression levels, and their expression level was highly correlated, suggesting their involvement in sunflower senescence. The results of this study thus contribute to the elucidation of the molecular mechanisms involved in the onset and progression of leaf senescence in sunflower leaves as well as to the identification of candidate genes involved in this process.
10aGas Chromatography-Mass Spectrometry10aGene Expression Profiling10aGene Expression Regulation, Plant10aGene ontology10aGenes, Plant10aHelianthus10aIons10ametabolomics10aOligonucleotide Array Sequence Analysis10aPlant Leaves10aPrincipal Component Analysis10aRNA, Messenger10aTranscription Factors1 aMoschen, Sebastián1 aLuoni, Sofía, Bengoa1 aDi Rienzo, Julio, A1 aCaro, María, Del Pilar1 aTohge, Takayuki1 aWatanabe, Mutsumi1 aHollmann, Julien1 aGonzalez, Sergio1 aRivarola, Máximo1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aHopp, Horacio, Esteban1 aHoefgen, Rainer1 aFernie, Alisdair, R1 aPaniego, Norma1 aFernandez, Paula1 aHeinz, Ruth, A uhttp://clinbioinfosspa.es/content/integrating-transcriptomic-and-metabolomic-analysis-understand-natural-leaf-senescence03376nas a2200793 4500008004100000022001400041245011500055210006900170260001600239520112300255653001101378653000801389653002001397100001901417700002601436700001201462700001801474700002201492700002701514700002201541700002201563700001901585700002901604700002301633700002001656700002001676700002301696700002501719700002201744700002201766700002101788700004001809700001201849700001701861700001201878700001601890700001901906700001801925700002101943700001601964700002001980700002402000700002002024700001802044700001302062700001702075700001802092700002102110700002402131700002002155700002102175700001702196700002102213700002502234700002102259700001902280700001902299700002102318700001602339700001402355700001702369700001502386700001302401700003102414700002102445700002102466700002002487856007502507 2015 eng d a1548-710500aCombining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.0 aCombining tumor genome simulation with crowdsourcing to benchmar c2015 May 183 aThe detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.10acancer10aNGS10avariant calling1 aEwing, Adam, D1 aHoulahan, Kathleen, E1 aHu, Yin1 aEllrott, Kyle1 aCaloian, Cristian1 aYamaguchi, Takafumi, N1 aBare, Christopher1 aP’ng, Christine1 aWaggott, Daryl1 aSabelnykova, Veronica, Y1 aKellen, Michael, R1 aNorman, Thea, C1 aHaussler, David1 aFriend, Stephen, H1 aStolovitzky, Gustavo1 aMargolin, Adam, A1 aStuart, Joshua, M1 aBoutros, Paul, C1 aparticipants, ICGC-TCGA, DREAM Soma1 aXi, Liu1 aDewal, Ninad1 aFan, Yu1 aWang, Wenyi1 aWheeler, David1 aWilm, Andreas1 aTing, Grace, Hui1 aLi, Chenhao1 aBertrand, Denis1 aNagarajan, Niranjan1 aChen, Qing-Rong1 aHsu, Chih-Hao1 aHu, Ying1 aYan, Chunhua1 aKibbe, Warren1 aMeerzaman, Daoud1 aCibulskis, Kristian1 aRosenberg, Mara1 aBergelson, Louis1 aKiezun, Adam1 aRadenbaugh, Amie1 aSertier, Anne-Sophie1 aFerrari, Anthony1 aTonton, Laurie1 aBhutani, Kunal1 aHansen, Nancy, F1 aWang, Difei1 aSong, Lei1 aLai, Zhongwu1 aLiao, Yang1 aShi, Wei1 aCarbonell-Caballero, José1 aDopazo, Joaquín1 aLau, Cheryl, C K1 aGuinney, Justin uhttp://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html02147nas a2200301 4500008004100000022001400041245009600055210006900151260001600220520124900236100002101485700002401506700001401530700001401544700002201558700001701580700001701597700001701614700002301631700001701654700001801671700001501689700001901704700001801723700001501741700001801756856007101774 2015 eng d a1546-169600aPrediction of human population responses to toxic compounds by a collaborative competition.0 aPrediction of human population responses to toxic compounds by a c2015 Aug 103 aThe ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.1 aEduati, Federica1 aMangravite, Lara, M1 aWang, Tao1 aTang, Hao1 aBare, Christopher1 aHuang, Ruili1 aNorman, Thea1 aKellen, Mike1 aMenden, Michael, P1 aYang, Jichen1 aZhan, Xiaowei1 aZhong, Rui1 aXiao, Guanghua1 aXia, Menghang1 aAbdo, Nour1 aKosyk, Oksana uhttp://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html02676nas a2200541 4500008004100000022001400041245013100055210006900186260000900255300000900264490000600273520115300279653001201432100002001444700002001464700001601484700001701500700002501517700001601542700002001558700001801578700002101596700001801617700002001635700002001655700002101675700001401696700001501710700001801725700001901743700002601762700002701788700002701815700001601842700001301858700002101871700002601892700001801918700001601936700001801952700001501970700001501985700001302000700001502013700001302028700001602041856007702057 2014 eng d a2041-172300aAssessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.0 aAssessing technical performance in differential gene expression c2014 a51250 v53 aThere is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ’dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.10aRNA-seq1 aMunro, Sarah, A1 aLund, Steven, P1 aPine, Scott1 aBinder, Hans1 aClevert, Djork-Arné1 aConesa, Ana1 aDopazo, Joaquin1 aFasold, Mario1 aHochreiter, Sepp1 aHong, Huixiao1 aJafari, Nadereh1 aKreil, David, P1 aLabaj, Paweł, P1 aLi, Sheng1 aLiao, Yang1 aLin, Simon, M1 aMeehan, Joseph1 aMason, Christopher, E1 aSantoyo-López, Javier1 aSetterquist, Robert, A1 aShi, Leming1 aShi, Wei1 aSmyth, Gordon, K1 aStralis-Pavese, Nancy1 aSu, Zhenqiang1 aTong, Weida1 aWang, Charles1 aWang, Jian1 aXu, Joshua1 aYe, Zhan1 aYang, Yong1 aYu, Ying1 aSalit, Marc uhttp://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html03418nas a2200253 4500008004100000022001400041245011200055210006900167260000900236300000800245490000700253520257000260100002502830700003202855700001602887700001702903700002102920700002502941700002202966700001802988700002203006700001803028856011803046 2012 eng d a1471-216400aIdentification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics.0 aIdentification of yeast genes that confer resistance to chitosan c2012 a2670 v133 aBACKGROUND: Chitosan oligosaccharide (COS), a deacetylated derivative of chitin, is an abundant, and renewable natural polymer. COS has higher antimicrobial properties than chitosan and is presumed to act by disrupting/permeabilizing the cell membranes of bacteria, yeast and fungi. COS is relatively non-toxic to mammals. By identifying the molecular and genetic targets of COS, we hope to gain a better understanding of the antifungal mode of action of COS. RESULTS: Three different chemogenomic fitness assays, haploinsufficiency (HIP), homozygous deletion (HOP), and multicopy suppression (MSP) profiling were combined with a transcriptomic analysis to gain insight in to the mode of action and mechanisms of resistance to chitosan oligosaccharides. The fitness assays identified 39 yeast deletion strains sensitive to COS and 21 suppressors of COS sensitivity. The genes identified are involved in processes such as RNA biology (transcription, translation and regulatory mechanisms), membrane functions (e.g. signalling, transport and targeting), membrane structural components, cell division, and proteasome processes. The transcriptomes of control wild type and 5 suppressor strains overexpressing ARL1, BCK2, ERG24, MSG5, or RBA50, were analyzed in the presence and absence of COS. Some of the up-regulated transcripts in the suppressor overexpressing strains exposed to COS included genes involved in transcription, cell cycle, stress response and the Ras signal transduction pathway. Down-regulated transcripts included those encoding protein folding components and respiratory chain proteins. The COS-induced transcriptional response is distinct from previously described environmental stress responses (i.e. thermal, salt, osmotic and oxidative stress) and pre-treatment with these well characterized environmental stressors provided little or any resistance to COS. CONCLUSIONS: Overexpression of the ARL1 gene, a member of the Ras superfamily that regulates membrane trafficking, provides protection against COS-induced cell membrane permeability and damage. We found that the ARL1 COS-resistant over-expression strain was as sensitive to Amphotericin B, Fluconazole and Terbinafine as the wild type cells and that when COS and Fluconazole are used in combination they act in a synergistic fashion. The gene targets of COS identified in this study indicate that COS’s mechanism of action is different from other commonly studied fungicides that target membranes, suggesting that COS may be an effective fungicide for drug-resistant fungal pathogens.1 aJaime, María, D L A1 aLopez-Llorca, Luis, Vicente1 aConesa, Ana1 aLee, Anna, Y1 aProctor, Michael1 aHeisler, Lawrence, E1 aGebbia, Marinella1 aGiaever, Guri1 aWestwood, Timothy1 aNislow, Corey uhttp://clinbioinfosspa.es/content/identification-yeast-genes-confer-resistance-chitosan-oligosaccharide-cos-using03755nas a2200625 4500008004100000022001400041245009800055210006900153260001600222300001100238490000600249520178700255653001002042653004902052653001102101653002102112653004902133653002502182653002902207653002402236653002802260653001102288653001902299653003802318653003402356653001302390653002302403653001102426653001502437653001102452653003602463653003702499653002902536653003802565653002002603653002002623653002002643653001702663100002102680700002002701700002402721700002702745700002102772700002202793700002202815700002502837700001702862700002002879700002102899700001902920700001902939700002102958700002602979856012403005 2010 eng d a1932-620300aExploring the link between germline and somatic genetic alterations in breast carcinogenesis.0 aExploring the link between germline and somatic genetic alterati c2010 Nov 22 ae140780 v53 aRecent genome-wide association studies (GWASs) have identified candidate genes contributing to cancer risk through low-penetrance mutations. Many of these genes were unexpected and, intriguingly, included well-known players in carcinogenesis at the somatic level. To assess the hypothesis of a germline-somatic link in carcinogenesis, we evaluated the distribution of somatic gene labels within the ordered results of a breast cancer risk GWAS. This analysis suggested frequent influence on risk of genetic variation in loci encoding for "driver kinases" (i.e., kinases encoded by genes that showed higher somatic mutation rates than expected by chance and, therefore, whose deregulation may contribute to cancer development and/or progression). Assessment of these predictions using a population-based case-control study in Poland replicated the association for rs3732568 in EPHB1 (odds ratio (OR) = 0.79; 95% confidence interval (CI): 0.63-0.98; P(trend) = 0.031). Analyses by early age at diagnosis and by estrogen receptor α (ERα) tumor status indicated potential associations for rs6852678 in CDKL2 (OR = 0.32, 95% CI: 0.10-1.00; P(recessive) = 0.044) and rs10878640 in DYRK2 (OR = 2.39, 95% CI: 1.32-4.30; P(dominant) = 0.003), and for rs12765929, rs9836340, rs4707795 in BMPR1A, EPHA3 and EPHA7, respectively (ERα tumor status P(interaction)<0.05). The identification of three novel candidates as EPH receptor genes might indicate a link between perturbed compartmentalization of early neoplastic lesions and breast cancer risk and progression. Together, these data may lay the foundations for replication in additional populations and could potentially increase our knowledge of the underlying molecular mechanisms of breast carcinogenesis.
10aAdult10aBone Morphogenetic Protein Receptors, Type I10aBreast10aBreast Neoplasms10aCalcium-Calmodulin-Dependent Protein Kinases10aCase-Control Studies10aCyclin-Dependent Kinases10aDisease Progression10aEstrogen Receptor alpha10aFemale10aGene Frequency10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGenotype10aGerm-Line Mutation10aHumans10aOdds Ratio10aPoland10aPolymorphism, Single Nucleotide10aProtein Serine-Threonine Kinases10aProtein-Tyrosine Kinases10aReceptor Protein-Tyrosine Kinases10aReceptor, EphA310aReceptor, EphA710aReceptor, EphB110aRisk Factors1 aBonifaci, Núria1 aGórski, Bohdan1 aMasojć, Bartlomiej1 aWokołorczyk, Dominika1 aJakubowska, Anna1 aDębniak, Tadeusz1 aBerenguer, Antoni1 aMusach, Jordi, Serra1 aBrunet, Joan1 aDopazo, Joaquin1 aNarod, Steven, A1 aLubiński, Jan1 aLázaro, Conxi1 aCybulski, Cezary1 aPujana, Miguel, Angel uhttp://clinbioinfosspa.es/content/exploring-link-between-germline-and-somatic-genetic-alterations-breast-carcinogenesis07842nas a2202545 4500008004100000245014500041210006900186260001300255300001100268490000700279520110300286100001601389700002201405700002201427700002101449700001701470700002301487700001801510700001801528700002601546700001901572700002501591700002201616700002301638700002301661700001801684700001901702700001701721700001201738700002201750700002301772700002301795700001401818700002401832700002001856700001701876700001601893700001701909700002001926700002201946700001701968700001701985700001502002700001602017700001502033700002402048700002102072700001802093700002702111700001802138700002002156700002002176700001902196700001802215700001602233700001702249700001502266700002002281700002102301700001802322700001402340700002402354700002002378700002202398700002002420700002702440700001102467700001402478700001302492700001802505700001702523700002602540700001302566700001302579700002302592700001902615700002202634700002202656700001802678700001902696700002102715700001502736700002102751700001602772700001602788700001702804700002702821700002302848700002102871700001702892700002702909700002002936700001702956700001502973700001402988700002203002700001703024700001903041700001703060700001703077700001503094700002203109700001603131700002703147700002003174700002403194700002003218700002603238700001703264700001503281700001603296700001403312700001703326700002603343700001603369700002203385700001703407700001503424700002103439700003003460700002103490700001903511700001603530700002603546700001203572700002303584700001403607700002303621700002103644700001803665700002003683700002403703700001503727700002403742700002103766700002003787700002103807700001903828700001703847700001103864700001703875700001803892700001303910700001703923700001303940700001403953700001703967700001303984700001903997700003204016700002004048700001904068700001904087700002404106700002004130700002104150700002404171700002104195700002404216700001804240700001904258700001704277700002004294700001704314700002104331700001804352700001704370700001204387700002504399700001904424700002404443700002604467700002504493700001504518700002004533700001704553700001904570700002304589700001604612700002104628700001904649700002004668700001704688700001604705700001504721700001804736700001704754700002204771700001804793700002004811700002504831700002304856700001804879700001504897700001704912700001404929700002204943700002104965700001904986700001605005700001905021700001505040700001205055700001405067700001505081700001705096700001405113700001505127700001505142700001705157700001605174700001605190700002605206856006405232 2010 eng d00aThe MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.0 aMicroArray Quality Control MAQCII study of common practices for c2010 Aug a827-380 v283 aGene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
1 aShi, Leming1 aCampbell, Gregory1 aJones, Wendell, D1 aCampagne, Fabien1 aWen, Zhining1 aWalker, Stephen, J1 aSu, Zhenqiang1 aChu, Tzu-Ming1 aGoodsaid, Federico, M1 aPusztai, Lajos1 aShaughnessy, John, D1 aOberthuer, André1 aThomas, Russell, S1 aPaules, Richard, S1 aFielden, Mark1 aBarlogie, Bart1 aChen, Weijie1 aDu, Pan1 aFischer, Matthias1 aFurlanello, Cesare1 aGallas, Brandon, D1 aGe, Xijin1 aMegherbi, Dalila, B1 aSymmans, Fraser1 aWang, May, D1 aZhang, John1 aBitter, Hans1 aBrors, Benedikt1 aBushel, Pierre, R1 aBylesjo, Max1 aChen, Minjun1 aCheng, Jie1 aCheng, Jing1 aChou, Jeff1 aDavison, Timothy, S1 aDelorenzi, Mauro1 aDeng, Youping1 aDevanarayan, Viswanath1 aDix, David, J1 aDopazo, Joaquin1 aDorff, Kevin, C1 aElloumi, Fathi1 aFan, Jianqing1 aFan, Shicai1 aFan, Xiaohui1 aFang, Hong1 aGonzaludo, Nina1 aHess, Kenneth, R1 aHong, Huixiao1 aHuan, Jun1 aIrizarry, Rafael, A1 aJudson, Richard1 aJuraeva, Dilafruz1 aLababidi, Samir1 aLambert, Christophe, G1 aLi, Li1 aLi, Yanen1 aLi, Zhen1 aLin, Simon, M1 aLiu, Guozhen1 aLobenhofer, Edward, K1 aLuo, Jun1 aLuo, Wen1 aMcCall, Matthew, N1 aNikolsky, Yuri1 aPennello, Gene, A1 aPerkins, Roger, G1 aPhilip, Reena1 aPopovici, Vlad1 aPrice, Nathan, D1 aQian, Feng1 aScherer, Andreas1 aShi, Tieliu1 aShi, Weiwei1 aSung, Jaeyun1 aThierry-Mieg, Danielle1 aThierry-Mieg, Jean1 aThodima, Venkata1 aTrygg, Johan1 aVishnuvajjala, Lakshmi1 aWang, Sue, Jane1 aWu, Jianping1 aWu, Yichao1 aXie, Qian1 aYousef, Waleed, A1 aZhang, Liang1 aZhang, Xuegong1 aZhong, Sheng1 aZhou, Yiming1 aZhu, Sheng1 aArasappan, Dhivya1 aBao, Wenjun1 aLucas, Anne, Bergstrom1 aBerthold, Frank1 aBrennan, Richard, J1 aBuness, Andreas1 aCatalano, Jennifer, G1 aChang, Chang1 aChen, Rong1 aCheng, Yiyu1 aCui, Jian1 aCzika, Wendy1 aDemichelis, Francesca1 aDeng, Xutao1 aDosymbekov, Damir1 aEils, Roland1 aFeng, Yang1 aFostel, Jennifer1 aFulmer-Smentek, Stephanie1 aFuscoe, James, C1 aGatto, Laurent1 aGe, Weigong1 aGoldstein, Darlene, R1 aGuo, Li1 aHalbert, Donald, N1 aHan, Jing1 aHarris, Stephen, C1 aHatzis, Christos1 aHerman, Damir1 aHuang, Jianping1 aJensen, Roderick, V1 aJiang, Rui1 aJohnson, Charles, D1 aJurman, Giuseppe1 aKahlert, Yvonne1 aKhuder, Sadik, A1 aKohl, Matthias1 aLi, Jianying1 aLi, Li1 aLi, Menglong1 aLi, Quan-Zhen1 aLi, Shao1 aLi, Zhiguang1 aLiu, Jie1 aLiu, Ying1 aLiu, Zhichao1 aMeng, Lu1 aMadera, Manuel1 aMartinez-Murillo, Francisco1 aMedina, Ignacio1 aMeehan, Joseph1 aMiclaus, Kelci1 aMoffitt, Richard, A1 aMontaner, David1 aMukherjee, Piali1 aMulligan, George, J1 aNeville, Padraic1 aNikolskaya, Tatiana1 aNing, Baitang1 aPage, Grier, P1 aParker, Joel1 aParry, Mitchell1 aPeng, Xuejun1 aPeterson, Ron, L1 aPhan, John, H1 aQuanz, Brian1 aRen, Yi1 aRiccadonna, Samantha1 aRoter, Alan, H1 aSamuelson, Frank, W1 aSchumacher, Martin, M1 aShambaugh, Joseph, D1 aShi, Qiang1 aShippy, Richard1 aSi, Shengzhu1 aSmalter, Aaron1 aSotiriou, Christos1 aSoukup, Mat1 aStaedtler, Frank1 aSteiner, Guido1 aStokes, Todd, H1 aSun, Qinglan1 aTan, Pei-Yi1 aTang, Rong1 aTezak, Zivana1 aThorn, Brett1 aTsyganova, Marina1 aTurpaz, Yaron1 aVega, Silvia, C1 aVisintainer, Roberto1 avon Frese, Juergen1 aWang, Charles1 aWang, Eric1 aWang, Junwei1 aWang, Wei1 aWestermann, Frank1 aWilley, James, C1 aWoods, Matthew1 aWu, Shujian1 aXiao, Nianqing1 aXu, Joshua1 aXu, Lei1 aYang, Lun1 aZeng, Xiao1 aZhang, Jialu1 aZhang, Li1 aZhang, Min1 aZhao, Chen1 aPuri, Raj, K1 aScherf, Uwe1 aTong, Weida1 aWolfinger, Russell, D uhttp://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html02044nas a2200181 4500008004100000245008700041210006900128260001300197300001100210490000700221520141100228100002401639700002201663700002501685700002101710700001701731856011401748 2009 eng d00aAlignment of multiple protein structures based on sequence and structure features.0 aAlignment of multiple protein structures based on sequence and s c2009 Sep a569-740 v223 aComparing the structures of proteins is crucial to gaining insight into protein evolution and function. Here, we align the sequences of multiple protein structures by a dynamic programming optimization of a scoring function that is a sum of an affine gap penalty and terms dependent on various sequence and structure features (SALIGN). The features include amino acid residue type, residue position, residue accessible surface area, residue secondary structure state and the conformation of a short segment centered on the residue. The multiple alignment is built by following the ’guide’ tree constructed from the matrix of all pairwise protein alignment scores. Importantly, the method does not depend on the exact values of various parameters, such as feature weights and gap penalties, because the optimal alignment across a range of parameter values is found. Using multiple structure alignments in the HOMSTRAD database, SALIGN was benchmarked against MUSTANG for multiple alignments as well as against TM-align and CE for pairwise alignments. On the average, SALIGN produces a 15% improvement in structural overlap over HOMSTRAD and 14% over MUSTANG, and yields more equivalent structural positions than TM-align and CE in 90% and 95% of cases, respectively. The utility of accurate multiple structure alignment is illustrated by its application to comparative protein structure modeling.
1 aMadhusudhan, M., S.1 aWebb, Benjamin, M1 aMarti-Renom, Marc, A1 aEswar, Narayanan1 aSali, Andrej uhttp://clinbioinfosspa.es/content/alignment-multiple-protein-structures-based-sequence-and-structure-features02308nas a2200325 4500008004100000245009900041210006900140300001200209490000700221520117300228653001501401653003601416653004401452653003601496653009601532653005201628100001501680700001401695700001701709700001601726700001401742700001901756700001501775700001501790700001601805700002401821700001801845700001301863856010601876 2009 eng d00aMODBASE, a database of annotated comparative protein structure models and associated resources0 aMODBASE a database of annotated comparative protein structure mo aD347-540 v373 aMODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).10a*Databases10aMolecular Mutation Polymorphism10aProtein Genomics Humans Ligands *Models10aProtein User-Computer Interface10aSingle Nucleotide Protein Folding Protein Interaction Domains and Motifs *Protein Structure10aTertiary Proteins/genetics *Structural Homology1 aPieper, U.1 aEswar, N.1 aWebb, B., M.1 aEramian, D.1 aKelly, L.1 aBarkan, D., T.1 aCarter, H.1 aMankoo, P.1 aKarchin, R.1 aMarti-Renom, M., A.1 aDavis, F., P.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1894828200811nas a2200265 4500008004100000022001400041245005600055210005500111300001100166490000700177100002100184700002300205700002100228700002100249700002300270700002300293700001800316700001500334700002000349700002300369700002700392700001600419700002100435856008900456 2009 eng d a1557-810000aModeling and managing experimental data using FuGE.0 aModeling and managing experimental data using FuGE a239-510 v131 aJones, Andrew, R1 aLister, Allyson, L1 aHermida, Leandro1 aWilkinson, Peter1 aEisenacher, Martin1 aBelhajjame, Khalid1 aGibson, Frank1 aLord, Phil1 aPocock, Matthew1 aRosenfelder, Heiko1 aSantoyo-López, Javier1 aWipat, Anil1 aPaton, Norman, W uhttp://clinbioinfosspa.es/content/modeling-and-managing-experimental-data-using-fuge00979nas a2200325 4500008004100000020001400041245008100055210006900136260004400205300001400249490000600263653001900269653001500288653001000303100002300313700002200336700002200358700001800380700002200398700001700420700002700437700002100464700001700485700002100502700001700523700003100540700001800571700002300589856004100612 2009 eng d a1548-709100aStatistical methods for analysis of high-throughput RNA interference screens0 aStatistical methods for analysis of highthroughput RNA interfere bNature Publishing Groupc2009/08//print a569 - 5750 v610agene silencing10aregulation10asiRNA1 aBirmingham, Amanda1 aSelfors, Laura, M1 aForster, Thorsten1 aWrobel, David1 aKennedy, Caleb, J1 aShanks, Emma1 aSantoyo-López, Javier1 aDunican, Dara, J1 aLong, Aideen1 aKelleher, Dermot1 aSmith, Queta1 aBeijersbergen, Roderick, L1 aGhazal, Peter1 aShamu, Caroline, E uhttp://dx.doi.org/10.1038/nmeth.135102717nas a2200385 4500008004100000022001400041245008300055210006900138260001300207300001200220490000700232520153300239653001201772653002601784653002201810653002301832653002801855653001001883653001301893653002701906653003101933653001301964653002701977100001802004700003302022700001802055700002102073700002902094700002402123700002302147700001802170700002002188700001602208856010702224 2008 eng d a1362-496200aHigh-throughput functional annotation and data mining with the Blast2GO suite.0 aHighthroughput functional annotation and data mining with the Bl c2008 Jun a3420-350 v363 aFunctional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.
10aAnimals10aComputational Biology10aComputer Graphics10aDatabases, Genetic10aExpressed Sequence Tags10aGenes10aGenomics10aSequence Analysis, DNA10aSequence Analysis, Protein10aSoftware10aVocabulary, Controlled1 aGötz, Stefan1 aGarcía-Gómez, Juan, Miguel1 aTerol, Javier1 aWilliams, Tim, D1 aNagaraj, Shivashankar, H1 aNueda, Maria, José1 aRobles, Montserrat1 aTalon, Manuel1 aDopazo, Joaquin1 aConesa, Ana uhttp://clinbioinfosspa.es/content/high-throughput-functional-annotation-and-data-mining-blast2go-suite03685nas a2200937 4500008004100000022001400041245007800055210006900133260001300202300001100215490000600226520100100232653002601233653003201259653002301291653003801314653001301352653002601365653002401391110002301415700002301438700001901461700001801480700002501498700001801523700001901541700002101560700001601581700001601597700002901613700001701642700001901659700002201678700002501700700003101725700002501756700001601781700001901797700001601816700002001832700002601852700002501878700001901903700001901922700001801941700001901959700001401978700001901992700002002011700002002031700001702051700002002068700002102088700002402109700002102133700002102154700002102175700002202196700001802218700002002236700002302256700002402279700002502303700002002328700002002348700002002368700002002388700002202408700002002430700002302450700001702473700001602490700002702506700001802533700001802551700001902569700002002588700001802608700002302626856009802649 2008 eng d a1477-405400aInteroperability with Moby 1.0--it's better than sharing your toothbrush!0 aInteroperability with Moby 10its better than sharing your toothb c2008 May a220-310 v93 aThe BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.
10aComputational Biology10aDatabase Management Systems10aDatabases, Factual10aInformation Storage and Retrieval10aInternet10aProgramming Languages10aSystems Integration1 aBioMoby Consortium1 aWilkinson, Mark, D1 aSenger, Martin1 aKawas, Edward1 aBruskiewich, Richard1 aGouzy, Jerome1 aNoirot, Celine1 aBardou, Philippe1 aNg, Ambrose1 aHaase, Dirk1 aSaiz, Enrique, de Andres1 aWang, Dennis1 aGibbons, Frank1 aGordon, Paul, M K1 aSensen, Christoph, W1 aCarrasco, Jose, Manuel Rod1 aFernández, José, M1 aShen, Lixin1 aLinks, Matthew1 aNg, Michael1 aOpushneva, Nina1 aNeerincx, Pieter, B T1 aLeunissen, Jack, A M1 aErnst, Rebecca1 aTwigger, Simon1 aUsadel, Bjorn1 aGood, Benjamin1 aWong, Yan1 aStein, Lincoln1 aCrosby, William1 aKarlsson, Johan1 aRoyo, Romina1 aPárraga, Iván1 aRamírez, Sergio1 aGelpi, Josep, Lluis1 aTrelles, Oswaldo1 aPisano, David, G1 aJimenez, Natalia1 aKerhornou, Arnaud1 aRosset, Roman1 aZamacola, Leire1 aTárraga, Joaquín1 aHuerta-Cepas, Jaime1 aCarazo, Jose, María1 aDopazo, Joaquin1 aGuigó, Roderic1 aNavarro, Arcadi1 aOrozco, Modesto1 aValencia, Alfonso1 aClaros, Gonzalo1 aPérez, Antonio, J1 aAldana, Jose1 aRojano, Mar1 aCruz, Raul, Fernandez-1 aNavas, Ismael1 aSchiltz, Gary1 aFarmer, Andrew1 aGessler, Damian1 aSchoof, Heiko1 aGroscurth, Andreas uhttp://clinbioinfosspa.es/content/interoperability-moby-10-its-better-sharing-your-toothbrush03310nas a2200841 4500008004100000245008100041210007100122300001100193490000600204520100100210653007501211653010801286100002201394700001501416700001401431700002001445700001401465700001501479700001501494700001101509700001401520700001401534700001301548700001601561700001901577700001901596700002101615700002201636700001301658700001401671700001101685700001801696700002101714700002201735700001401757700001601771700001501787700001301802700001301815700001401828700001501842700001701857700001301874700001601887700001601903700001801919700001601937700001901953700001601972700001801988700001502006700001702021700001602038700002102054700001902075700001502094700001402109700001602123700001502139700001702154700001902171700001802190700001502208700001902223700002602242700001402268700001602282700001502298700001602313700001502329700001802344856010602362 2008 eng d00aInteroperability with Moby 1.0–it’s better than sharing your toothbrush!0 aInteroperability with Moby 10–it s better than sharing your toot a220-310 v93 aThe BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.
10aComputational Biology/*methods *Database Management Systems *Databases10aFactual Information Storage and Retrieval/*methods *Internet *Programming Languages Systems Integration1 aWilkinson, M., D.1 aSenger, M.1 aKawas, E.1 aBruskiewich, R.1 aGouzy, J.1 aNoirot, C.1 aBardou, P.1 aNg, A.1 aHaase, D.1 aEde, Saiz1 aWang, D.1 aGibbons, F.1 aGordon, P., M.1 aSensen, C., W.1 aCarrasco, J., M.1 aFernandez, J., M.1 aShen, L.1 aLinks, M.1 aNg, M.1 aOpushneva, N.1 aNeerincx, P., B.1 aLeunissen, J., A.1 aErnst, R.1 aTwigger, S.1 aUsadel, B.1 aGood, B.1 aWong, Y.1 aStein, L.1 aCrosby, W.1 aKarlsson, J.1 aRoyo, R.1 aParraga, I.1 aRamirez, S.1 aGelpi, J., L.1 aTrelles, O.1 aPisano, D., G.1 aJimenez, N.1 aKerhornou, A.1 aRosset, R.1 aZamacola, L.1 aTarraga, J.1 aHuerta-Cepas, J.1 aCarazo, J., M.1 aDopazo, J.1 aGuigo, R.1 aNavarro, A.1 aOrozco, M.1 aValencia, A.1 aClaros, M., G.1 aPerez, A., J.1 aAldana, J.1 aRojano, M., M.1 aCruz, Fernandez-Santa1 aNavas, I.1 aSchiltz, G.1 aFarmer, A.1 aGessler, D.1 aSchoof, H.1 aGroscurth, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1823880403856nas a2200385 4500008004100000245011000041210006900151300000700220490000600227520253000233653016602763653002902929653009302958100001403051700001503065700002203080700001503102700001403117700001503131700001303146700001503159700001403174700001503188700002103203700001303224700001403237700001503251700001703266700001903283700001503302700001603317700001703333700001403350856010603364 2007 eng d00aAnalysis of 13000 unique Citrus clusters associated with fruit quality, production and salinity tolerance0 aAnalysis of 13000 unique Citrus clusters associated with fruit q a310 v83 aBACKGROUND: Improvement of Citrus, the most economically important fruit crop in the world, is extremely slow and inherently costly because of the long-term nature of tree breeding and an unusual combination of reproductive characteristics. Aside from disease resistance, major commercial traits in Citrus are improved fruit quality, higher yield and tolerance to environmental stresses, especially salinity. RESULTS: A normalized full length and 9 standard cDNA libraries were generated, representing particular treatments and tissues from selected varieties (Citrus clementina and C. sinensis) and rootstocks (C. reshni, and C. sinenis x Poncirus trifoliata) differing in fruit quality, resistance to abscission, and tolerance to salinity. The goal of this work was to provide a large expressed sequence tag (EST) collection enriched with transcripts related to these well appreciated agronomical traits. Towards this end, more than 54000 ESTs derived from these libraries were analyzed and annotated. Assembly of 52626 useful sequences generated 15664 putative transcription units distributed in 7120 contigs, and 8544 singletons. BLAST annotation produced significant hits for more than 80% of the hypothetical transcription units and suggested that 647 of these might be Citrus specific unigenes. The unigene set, composed of 13000 putative different transcripts, including more than 5000 novel Citrus genes, was assigned with putative functions based on similarity, GO annotations and protein domains CONCLUSION: Comparative genomics with Arabidopsis revealed the presence of putative conserved orthologs and single copy genes in Citrus and also the occurrence of both gene duplication events and increased number of genes for specific pathways. In addition, phylogenetic analysis performed on the ammonium transporter family and glycosyl transferase family 20 suggested the existence of Citrus paralogs. Analysis of the Citrus gene space showed that the most important metabolic pathways known to affect fruit quality were represented in the unigene set. Overall, the similarity analyses indicated that the sequences of the genes belonging to these varieties and rootstocks were essentially identical, suggesting that the differential behaviour of these species cannot be attributed to major sequence divergences. This Citrus EST assembly contributes both crucial information to discover genes of agronomical interest and tools for genetic and genomic analyses, such as the development of new markers and microarrays.10aAcclimatization/*genetics Amino Acid Motifs Citrus/*genetics Cluster Analysis Expressed Sequence Tags Fruit/genetics Gene Duplication *Gene Expression Regulation10aPlant Gene Library Genes10aPlant Genomics Molecular Sequence Data Multigene Family Phylogeny *Salts/adverse effects1 aTerol, J.1 aConesa, A.1 aColmenero, J., M.1 aCercos, M.1 aTadeo, F.1 aAgusti, J.1 aAlos, E.1 aAndres, F.1 aSoler, G.1 aBrumos, J.1 aIglesias, D., J.1 aGotz, S.1 aLegaz, F.1 aArgout, X.1 aCourtois, B.1 aOllitrault, P.1 aDossat, C.1 aWincker, P.1 aMorillon, R.1 aTalon, M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1725432702714nas a2200253 4500008004100000245009200041210006900133300001300202490000700215520168800222653010801910653001202018653001902030653005802049653012102107100001802228700001502246700002302261700002202284700001902306700001402325700001502339856010602354 2007 eng d00aDiscovering gene expression patterns in time course microarray experiments by ANOVA-SCA0 aDiscovering gene expression patterns in time course microarray e a1792-8000 v233 aMOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.10aAlgorithms *Analysis of Variance Computational Biology/*methods Computer Simulation Data Interpretation10aGenetic10aGenetic Models10aStatistical Gene Expression Profiling/*methods Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods Principal Component Analysis Time Factors Transcription1 aNueda, M., J.1 aConesa, A.1 aWesterhuis, J., A.1 aHoefsloot, H., C.1 aSmilde, A., K.1 aTalon, M.1 aFerrer, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1751925002580nas a2200253 4500008004100000245009100041210006900132300001200201490000700213520154400220653002301764653025501787100001702042700001702059700002502076700001802101700002102119700002002140700001302160700002002173700001302193700001402206856010602220 2007 eng d00aPeroxisomeDB: a database for the peroxisomal proteome, functional genomics and disease0 aPeroxisomeDB a database for the peroxisomal proteome functional aD815-220 v353 aPeroxisomes are essential organelles of eukaryotic origin, ubiquitously distributed in cells and organisms, playing key roles in lipid and antioxidant metabolism. Loss or malfunction of peroxisomes causes more than 20 fatal inherited conditions. We have created a peroxisomal database (http://www.peroxisomeDB.org) that includes the complete peroxisomal proteome of Homo sapiens and Saccharomyces cerevisiae, by gathering, updating and integrating the available genetic and functional information on peroxisomal genes. PeroxisomeDB is structured in interrelated sections ’Genes’, ’Functions’, ’Metabolic pathways’ and ’Diseases’, that include hyperlinks to selected features of NCBI, ENSEMBL and UCSC databases. We have designed graphical depictions of the main peroxisomal metabolic routes and have included updated flow charts for diagnosis. Precomputed BLAST, PSI-BLAST, multiple sequence alignment (MUSCLE) and phylogenetic trees are provided to assist in direct multispecies comparison to study evolutionary conserved functions and pathways. Highlights of the PeroxisomeDB include new tools developed for facilitating (i) identification of novel peroxisomal proteins, by means of identifying proteins carrying peroxisome targeting signal (PTS) motifs, (ii) detection of peroxisomes in silico, particularly useful for screening the deluge of newly sequenced genomes. PeroxisomeDB should contribute to the systematic characterization of the peroxisomal proteome and facilitate system biology approaches on the organelle.10aAnimals *Databases10aProtein Genomics Humans Internet Mice Peroxisomal Disorders/*genetics Peroxisomes/*metabolism Protein Sorting Signals Proteome/chemistry/*genetics/*physiology Rats Saccharomyces cerevisiae Proteins/genetics/physiology Software User-Computer Interface1 aSchluter, A.1 aFourcade, S.1 aDomenech-Estevez, E.1 aGabaldón, T.1 aHuerta-Cepas, J.1 aBerthommier, G.1 aRipp, R.1 aWanders, R., J.1 aPoch, O.1 aPujol, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1713519001778nas a2200205 4500008004100000245007500041210006900116300001300185490000800198520097700206653011101183653003901294653004101333100002001374700001401394700002401408700001701432700001701449856010601466 2007 eng d00aProtein translocation into peroxisomes by ring-shaped import receptors0 aProtein translocation into peroxisomes by ringshaped import rece a4795-8020 v5813 aFolded and functional proteins destined for translocation from the cytosol into the peroxisomal matrix are recognized by two different peroxisomal import receptors, Pex5p and Pex7p. Both cargo-loaded receptors dock on the same translocon components, followed by cargo release and receptor recycling, as part of the complete translocation process. Recent structural and functional evidence on the Pex5p receptor has provided insight on the molecular requirements of specific cargo recognition, while the remaining processes still remain largely elusive. Comparison of experimental structures of Pex5p and a structural model of Pex7p reveal that both receptors are built by ring-like arrangements with cargo binding sites, central to the respective structures. Although, molecular insight into the complete peroxisomal translocon still remains to be determined, emerging data allow to deduce common molecular principles that may hold for other translocation systems as well.10aAmino Acid Sequence Binding Sites Humans Molecular Sequence Data Peroxisomes/*metabolism Protein Structure10aCytoplasmic and Nuclear/*chemistry10aTertiary Protein Transport Receptors1 aStanley, W., A.1 aFodor, K.1 aMarti-Renom, M., A.1 aSchliebs, W.1 aWilmanns, M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1788404202657nas a2200277 4500008004100000245007600041210006900117300001200186490000600198520157700204653011001781653008001891653001701971653001801988653005402006653006902060100001802129700002102147700001502168700001802183700001402201700001802215700002102233700001902254856010602273 2007 eng d00aSpatial differentiation in the vegetative mycelium of Aspergillus niger0 aSpatial differentiation in the vegetative mycelium of Aspergillu a2311-220 v63 aFungal mycelia are exposed to heterogenic substrates. The substrate in the central part of the colony has been (partly) degraded, whereas it is still unexplored at the periphery of the mycelium. We here assessed whether substrate heterogeneity is a main determinant of spatial gene expression in colonies of Aspergillus niger. This question was addressed by analyzing whole-genome gene expression in five concentric zones of 7-day-old maltose- and xylose-grown colonies. Expression profiles at the periphery and the center were clearly different. More than 25% of the active genes showed twofold differences in expression between the inner and outermost zones of the colony. Moreover, 9% of the genes were expressed in only one of the five concentric zones, showing that a considerable part of the genome is active in a restricted part of the colony only. Statistical analysis of expression profiles of colonies that had either been or not been transferred to fresh xylose-containing medium showed that differential expression in a colony is due to the heterogeneity of the medium (e.g., genes involved in secretion, genes encoding proteases, and genes involved in xylose metabolism) as well as to medium-independent mechanisms (e.g., genes involved in nitrate metabolism and genes involved in cell wall synthesis and modification). Thus, we conclude that the mycelia of 7-day-old colonies of A. niger are highly differentiated. This conclusion is also indicated by the fact that distinct zones of the colony grow and secrete proteins, even after transfer to fresh medium.10aAspergillus niger/*metabolism Cell Wall/metabolism Fungal Proteins/metabolism *Gene Expression Regulation10aBiological Mycelium/*metabolism Oligonucleotide Array Sequence Analysis RNA10aFungal Genes10aFungal Genome10aFungal Glucans/chemistry Maltose/chemistry Models10aFungal Time Factors Trans-Activators/metabolism Xylose/chemistry1 aLevin, A., M.1 ade Vries, R., P.1 aConesa, A.1 ade Bekker, C.1 aTalon, M.1 aMenke, H., H.1 avan Peij, N., N.1 aWosten, H., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1795151302005nas a2200253 4500008004100000245005800041210005800099300001300157490001400170520105600184653008801240653002101328653012901349653003101478100001401509700001301523700002401536700002401560700001601584700001701600700001501617700001301632856010601645 2006 eng d00aComparative protein structure modeling using Modeller0 aComparative protein structure modeling using Modeller aUnit 5 60 vChapter 53 aFunctional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.10aAlgorithms Amino Acid Sequence Computer Simulation Crystallography/*methods *Models10aChemical *Models10aMolecular Molecular Sequence Data Protein Conformation Protein Folding Proteins/*chemistry/*ultrastructure Sequence Analysis10aProtein/*methods *Software1 aEswar, N.1 aWebb, B.1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aEramian, D.1 aShen, M., Y.1 aPieper, U.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1842876702539nas a2200265 4500008004100000245016000041210006900201300001200270490000700282520126500289653009401554653019301648653013501841653002601976100002102002700001702023700001902040700002002059700001502079700001502094700002002109700001802129700002002147856010602167 2006 eng d00aDevelopment of the GENIPOL European flounder (Platichthys flesus) microarray and determination of temporal transcriptional responses to cadmium at low dose0 aDevelopment of the GENIPOL European flounder Platichthys flesus a6479-880 v403 aWe have constructed a high density, 13 270-clone cDNA array for the sentinel fish species European flounder (Platichthys flesus), combining clones from suppressive subtractive hybridization and a liver cDNA library; DNA sequences of 5211 clones were determined. Fish were treated by single intraperitoneal injection with 50 micrograms cadmium chloride per kilogram body weight, a dose relevant to environmental exposures, and hepatic gene expression changes were determined at 1, 2, 4, 8, and 16 days postinjection in comparison to saline-treated controls. Gene expression responses were confirmed by real-time reverse transcription polymerase chain reaction (RT-PCR). Blast2GO gene ontology analysis highlighted a general induction of the unfolded protein response, response to oxidative stress, protein synthesis, transport, and degradation pathways, while apoptosis, cell cycle, cytoskeleton, and cytokine genes were also affected. Transcript levels of cytochrome P450 1A (CYP1A) were repressed and vitellogenin altered, real-time PCR showed induction of metallothionein. We thus describe the establishment of a useful resource for ecotoxicogenomics and the determination of the temporal molecular responses to cadmium, a prototypical heavy metal pollutant.10aAnimals Cadmium Chloride/administration & dosage/*pharmacology Dose-Response Relationship10aDevelopmental/drug effects Liver/drug effects/growth & development/metabolism Oligonucleotide Array Sequence Analysis/*methods Reverse Transcriptase Polymerase Chain Reaction Transcription10aDrug Environmental Monitoring/methods Flounder/*genetics/growth & development Gene Expression Profiling Gene Expression Regulation10aGenetic/*drug effects1 aWilliams, T., D.1 aDiab, A., M.1 aGeorge, S., G.1 aGodfrey, R., E.1 aSabine, V.1 aConesa, A.1 aMinchin, S., D.1 aWatts, P., C.1 aChipman, J., K. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1712058402671nas a2200349 4500008004100000245009900041210006900140300001100209490000700220520147800227653002901705653002701734653004401761653005601805653004001861653008301901100001501984700001401999700001802013700001602031700002402047700001402071700002402085700001602109700001702125700001602142700001702158700001402175700001302189700001302202856010602215 2006 eng d00aMODBASE: a database of annotated comparative protein structure models and associated resources0 aMODBASE a database of annotated comparative protein structure mo aD291-50 v343 aMODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).10aBinding Sites *Databases10aMolecular Polymorphism10aProtein Humans Internet Ligands *Models10aProtein Systems Integration User-Computer Interface10aSingle Nucleotide Protein Structure10aTertiary Proteins/*chemistry/genetics/metabolism Software *Structural Homology1 aPieper, U.1 aEswar, N.1 aDavis, F., P.1 aBraberg, H.1 aMadhusudhan, M., S.1 aRossi, A.1 aMarti-Renom, M., A.1 aKarchin, R.1 aWebb, B., M.1 aEramian, D.1 aShen, M., Y.1 aKelly, L.1 aMelo, F.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1638186900525nas a2200121 4500008004100000245009500041210006900136260003500205100001500240700001500255700001200270856012100282 2005 eng d00aData and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions0 aData and Predictive Model Integration an Overview of Key Concept bWiley, F. Azuaje and J. Dopazo1 aAzuaje, F.1 aDopazo, J.1 aWang, H uhttp://clinbioinfosspa.es/content/data-and-predictive-model-integration-overview-key-concepts-problems-and-solutions00551nas a2200133 4500008004100000245011100041210006900152300001000221100001200231700001500243700001900258700001500277856012500292 2004 eng d00aGene expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationship0 aGene expression Correlation and Gene OntologyBased Similarity An a25-311 aWang, H1 aAzuaje, F.1 aBodenreider, O1 aDopazo, J. uhttp://clinbioinfosspa.es/content/gene-expression-correlation-and-gene-ontology-based-similarity-assessment-quantitative03269nas a2200337 4500008004100000245010000041210006900141300001200210490000700222520201600229653008002245653005102325653005202376653014002428100001502568700001402583700001602597700002402613700001802637700001902655700001702674700001402691700002402705700001402729700001302743700001902756700001802775700001902793700001302812856010602825 2004 eng d00aMODBASE, a database of annotated comparative protein structure models, and associated resources0 aMODBASE a database of annotated comparative protein structure mo aD217-220 v323 aMODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).10aAmino Acid Sequence Animals Binding Sites *Computational Biology *Databases10aMolecular Molecular Sequence Data Polymorphism10aProtein Genomics Humans Internet Ligands Models10aSingle Nucleotide Protein Binding Protein Conformation Proteins/*chemistry/genetics Sequence Alignment Software User-Computer Interface1 aPieper, U.1 aEswar, N.1 aBraberg, H.1 aMadhusudhan, M., S.1 aDavis, F., P.1 aStuart, A., C.1 aMirkovic, N.1 aRossi, A.1 aMarti-Renom, M., A.1 aFiser, A.1 aWebb, B.1 aGreenblatt, D.1 aHuang, C., C.1 aFerrin, T., E.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1468139802302nas a2200205 4500008004100000245012700041210006900168300001100237490000700248520137200255653006101627653007901688653013001767100001701897700002401914700001801938700001301956700002101969856010601990 2004 eng d00aStructure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition0 aStructurebased assessment of missense mutations in human BRCA1 i a3790-70 v643 aThe BRCA1 gene from individuals at risk of breast and ovarian cancers can be screened for the presence of mutations. However, the cancer association of most alleles carrying missense mutations is unknown, thus creating significant problems for genetic counseling. To increase our ability to identify cancer-associated mutations in BRCA1, we set out to use the principles of protein three-dimensional structure as well as the correlation between the cancer-associated mutations and those that abolish transcriptional activation. Thirty-one of 37 missense mutations of known impact on the transcriptional activation function of BRCA1 are readily rationalized in structural terms. Loss-of-function mutations involve nonconservative changes in the core of the BRCA1 C-terminus (BRCT) fold or are localized in a groove that presumably forms a binding site involved in the transcriptional activation by BRCA1; mutations that do not abolish transcriptional activation are either conservative changes in the core or are on the surface outside of the putative binding site. Next, structure-based rules for predicting functional consequences of a given missense mutation were applied to 57 germ-line BRCA1 variants of unknown cancer association. Such a structure-based approach may be helpful in an integrated effort to identify mutations that predispose individuals to cancer.10aBRCA1 Genetic Predisposition to Disease Humans *Mutation10aBRCA1 Protein/*chemistry/genetics Breast Neoplasms/*genetics Female *Genes10aMissense Ovarian Neoplasms/*genetics Pedigree Protein Conformation Structure-Activity Relationship Transcriptional Activation1 aMirkovic, N.1 aMarti-Renom, M., A.1 aWeber, B., L.1 aSali, A.1 aMonteiro, A., N. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1517298502774nas a2200313 4500008004100000245014600041210006900187300001300256490000800269520115600277653004801433653003001481653005001511653023501561653012201796653010201918653014402020100002002164700001502184700002102199700002202220700001702242700002402259700001302283700002002296700001902316700001902335856010602354 2002 eng d00aUse of single point mutations in domain I of beta 2-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies0 aUse of single point mutations in domain I of beta 2glycoprotein a7097-1030 v1693 aAutoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope.10aAmino Acid Substitution/genetics Antibodies10aAntibody/genetics Binding10aAntiphospholipid/blood/*metabolism Antibodies10aCompetitive/genetics/immunology Enzyme-Linked Immunosorbent Assay/methods Epitopes/analysis/*immunology/metabolism Glycine/genetics Glycoproteins/biosynthesis/*genetics/*immunology/isolation & purification/metabolism Humans Models10aMolecular Peptide Fragments/genetics/immunology/isolation & purification/metabolism *Point Mutation Protein Structure10aMonoclonal/blood/metabolism Antiphospholipid Syndrome/immunology Arginine/genetics *Binding Sites10aTertiary/genetics Recombinant Proteins/biosynthesis/immunology/isolation & purification/metabolism Static Electricity beta 2-Glycoprotein I1 aIverson, G., M.1 aReddel, S.1 aVictoria, E., J.1 aCockerill, K., A.1 aWang, Y., X.1 aMarti-Renom, M., A.1 aSali, A.1 aMarquis, D., M.1 aKrilis, S., A.1 aLinnik, M., D. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1247114601688nas a2200169 4500008004100000245010100041210006900142300001100211490000800222520084700230653026001077100001501337700001601352700002701368700001701395856010601412 2001 eng d00aC-terminal propeptide of the Caldariomyces fumago chloroperoxidase: an intramolecular chaperone?0 aCterminal propeptide of the Caldariomyces fumago chloroperoxidas a117-200 v5033 aThe Caldariomyces fumago chloroperoxidase (CPO) is synthesised as a 372-aa precursor which undergoes two proteolytic processing events: removal of a 21-aa N-terminal signal peptide and of a 52-aa C-terminal propeptide. The Aspergillus niger expression system developed for CPO was used to get insight into the function of this C-terminal propeptide. A. niger transformants expressing a CPO protein from which the C-terminal propeptide was deleted failed in producing any extracellular CPO activity, although the CPO polypeptide was synthesised. Expression of the full-length gene in an A. niger strain lacking the KEX2-like protease PclA also resulted in the production of CPO cross-reactive material into the culture medium, but no CPO activity. Based on these results, a function of the C-terminal propeptide in CPO maturation is indicated.10aAmino Acid Sequence Ascomycota/*enzymology/genetics Aspergillus niger/genetics Base Sequence Chloride Peroxidase/biosynthesis/*chemistry/genetics DNA Primers/genetics Enzyme Precursors/biosynthesis/chemistry/genetics Gene Expression Molecular Chaperones/b1 aConesa, A.1 aWeelink, G.1 avan den Hondel, C., A.1 aPunt, P., J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11513866