%0 Journal Article %J Front Immunol %D 2024 %T Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. %A Niarakis, Anna %A Ostaszewski, Marek %A Mazein, Alexander %A Kuperstein, Inna %A Kutmon, Martina %A Gillespie, Marc E %A Funahashi, Akira %A Acencio, Marcio Luis %A Hemedan, Ahmed %A Aichem, Michael %A Klein, Karsten %A Czauderna, Tobias %A Burtscher, Felicia %A Yamada, Takahiro G %A Hiki, Yusuke %A Hiroi, Noriko F %A Hu, Finterly %A Pham, Nhung %A Ehrhart, Friederike %A Willighagen, Egon L %A Valdeolivas, Alberto %A Dugourd, Aurélien %A Messina, Francesco %A Esteban-Medina, Marina %A Peña-Chilet, Maria %A Rian, Kinza %A Soliman, Sylvain %A Aghamiri, Sara Sadat %A Puniya, Bhanwar Lal %A Naldi, Aurélien %A Helikar, Tomáš %A Singh, Vidisha %A Fernández, Marco Fariñas %A Bermudez, Viviam %A Tsirvouli, Eirini %A Montagud, Arnau %A Noël, Vincent %A Ponce-de-Leon, Miguel %A Maier, Dieter %A Bauch, Angela %A Gyori, Benjamin M %A Bachman, John A %A Luna, Augustin %A Piñero, Janet %A Furlong, Laura I %A Balaur, Irina %A Rougny, Adrien %A Jarosz, Yohan %A Overall, Rupert W %A Phair, Robert %A Perfetto, Livia %A Matthews, Lisa %A Rex, Devasahayam Arokia Balaya %A Orlic-Milacic, Marija %A Gomez, Luis Cristobal Monraz %A De Meulder, Bertrand %A Ravel, Jean Marie %A Jassal, Bijay %A Satagopam, Venkata %A Wu, Guanming %A Golebiewski, Martin %A Gawron, Piotr %A Calzone, Laurence %A Beckmann, Jacques S %A Evelo, Chris T %A D'Eustachio, Peter %A Schreiber, Falk %A Saez-Rodriguez, Julio %A Dopazo, Joaquin %A Kuiper, Martin %A Valencia, Alfonso %A Wolkenhauer, Olaf %A Kitano, Hiroaki %A Barillot, Emmanuel %A Auffray, Charles %A Balling, Rudi %A Schneider, Reinhard %K Computer Simulation %K COVID-19 %K drug repositioning %K Humans %K SARS-CoV-2 %K Systems biology %X

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.

%B Front Immunol %V 14 %P 1282859 %8 2023 %G eng %R 10.3389/fimmu.2023.1282859 %0 Journal Article %J Commun Biol %D 2023 %T Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging. %A Sola-García, Alejandro %A Cáliz-Molina, María Ángeles %A Espadas, Isabel %A Petr, Michael %A Panadero-Morón, Concepción %A González-Morán, Daniel %A Martín-Vázquez, María Eugenia %A Narbona-Pérez, Álvaro Jesús %A López-Noriega, Livia %A Martínez-Corrales, Guillermo %A López-Fernández-Sobrino, Raúl %A Carmona-Marin, Lina M %A Martínez-Force, Enrique %A Yanes, Oscar %A Vinaixa, Maria %A López-López, Daniel %A Reyes, José Carlos %A Dopazo, Joaquin %A Martín, Franz %A Gauthier, Benoit R %A Scheibye-Knudsen, Morten %A Capilla-González, Vivian %A Martín-Montalvo, Alejandro %X

ATP-citrate lyase is a central integrator of cellular metabolism in the interface of protein, carbohydrate, and lipid metabolism. The physiological consequences as well as the molecular mechanisms orchestrating the response to long-term pharmacologically induced Acly inhibition are unknown. We report here that the Acly inhibitor SB-204990 improves metabolic health and physical strength in wild-type mice when fed with a high-fat diet, while in mice fed with healthy diet results in metabolic imbalance and moderated insulin resistance. By applying a multiomic approach using untargeted metabolomics, transcriptomics, and proteomics, we determined that, in vivo, SB-204990 plays a role in the regulation of molecular mechanisms associated with aging, such as energy metabolism, mitochondrial function, mTOR signaling, and folate cycle, while global alterations on histone acetylation are absent. Our findings indicate a mechanism for regulating molecular pathways of aging that prevents the development of metabolic abnormalities associated with unhealthy dieting. This strategy might be explored for devising therapeutic approaches to prevent metabolic diseases.

%B Commun Biol %V 6 %P 250 %8 2023 Mar 08 %G eng %N 1 %R 10.1038/s42003-023-04625-4 %0 Journal Article %J Hum Mol Genet %D 2022 %T Novel genes and sex differences in COVID-19 severity. %A Cruz, Raquel %A Almeida, Silvia Diz-de %A Heredia, Miguel López %A Quintela, Inés %A Ceballos, Francisco C %A Pita, Guillermo %A Lorenzo-Salazar, José M %A González-Montelongo, Rafaela %A Gago-Domínguez, Manuela %A Porras, Marta Sevilla %A Castaño, Jair Antonio Tenorio %A Nevado, Julián %A Aguado, Jose María %A Aguilar, Carlos %A Aguilera-Albesa, Sergio %A Almadana, Virginia %A Almoguera, Berta %A Alvarez, Nuria %A Andreu-Bernabeu, Álvaro %A Arana-Arri, Eunate %A Arango, Celso %A Arranz, María J %A Artiga, Maria-Jesus %A Baptista-Rosas, Raúl C %A Barreda-Sánchez, María %A Belhassen-Garcia, Moncef %A Bezerra, Joao F %A Bezerra, Marcos A C %A Boix-Palop, Lucía %A Brión, Maria %A Brugada, Ramón %A Bustos, Matilde %A Calderón, Enrique J %A Carbonell, Cristina %A Castano, Luis %A Castelao, Jose E %A Conde-Vicente, Rosa %A Cordero-Lorenzana, M Lourdes %A Cortes-Sanchez, Jose L %A Corton, Marta %A Darnaude, M Teresa %A De Martino-Rodríguez, Alba %A Campo-Pérez, Victor %A Bustamante, Aranzazu Diaz %A Domínguez-Garrido, Elena %A Luchessi, André D %A Eirós, Rocío %A Sanabria, Gladys Mercedes Estigarribia %A Fariñas, María Carmen %A Fernández-Robelo, Uxía %A Fernández-Rodríguez, Amanda %A Fernández-Villa, Tania %A Gil-Fournier, Belén %A Gómez-Arrue, Javier %A Álvarez, Beatriz González %A Quirós, Fernan Gonzalez Bernaldo %A González-Peñas, Javier %A Gutiérrez-Bautista, Juan F %A Herrero, María José %A Herrero-Gonzalez, Antonio %A Jimenez-Sousa, María A %A Lattig, María Claudia %A Borja, Anabel Liger %A Lopez-Rodriguez, Rosario %A Mancebo, Esther %A Martín-López, Caridad %A Martín, Vicente %A Martinez-Nieto, Oscar %A Martinez-Lopez, Iciar %A Martinez-Resendez, Michel F %A Martinez-Perez, Ángel %A Mazzeu, Juliana A %A Macías, Eleuterio Merayo %A Minguez, Pablo %A Cuerda, Victor Moreno %A Silbiger, Vivian N %A Oliveira, Silviene F %A Ortega-Paino, Eva %A Parellada, Mara %A Paz-Artal, Estela %A Santos, Ney P C %A Pérez-Matute, Patricia %A Perez, Patricia %A Pérez-Tomás, M Elena %A Perucho, Teresa %A Pinsach-Abuin, Mel Lina %A Pompa-Mera, Ericka N %A Porras-Hurtado, Gloria L %A Pujol, Aurora %A León, Soraya Ramiro %A Resino, Salvador %A Fernandes, Marianne R %A Rodríguez-Ruiz, Emilio %A Rodriguez-Artalejo, Fernando %A Rodriguez-Garcia, José A %A Ruiz-Cabello, Francisco %A Ruiz-Hornillos, Javier %A Ryan, Pablo %A Soria, José Manuel %A Souto, Juan Carlos %A Tamayo, Eduardo %A Tamayo-Velasco, Alvaro %A Taracido-Fernandez, Juan Carlos %A Teper, Alejandro %A Torres-Tobar, Lilian %A Urioste, Miguel %A Valencia-Ramos, Juan %A Yáñez, Zuleima %A Zarate, Ruth %A Nakanishi, Tomoko %A Pigazzini, Sara %A Degenhardt, Frauke %A Butler-Laporte, Guillaume %A Maya-Miles, Douglas %A Bujanda, Luis %A Bouysran, Youssef %A Palom, Adriana %A Ellinghaus, David %A Martínez-Bueno, Manuel %A Rolker, Selina %A Amitrano, Sara %A Roade, Luisa %A Fava, Francesca %A Spinner, Christoph D %A Prati, Daniele %A Bernardo, David %A García, Federico %A Darcis, Gilles %A Fernández-Cadenas, Israel %A Holter, Jan Cato %A Banales, Jesus M %A Frithiof, Robert %A Duga, Stefano %A Asselta, Rosanna %A Pereira, Alexandre C %A Romero-Gómez, Manuel %A Nafría-Jiménez, Beatriz %A Hov, Johannes R %A Migeotte, Isabelle %A Renieri, Alessandra %A Planas, Anna M %A Ludwig, Kerstin U %A Buti, Maria %A Rahmouni, Souad %A Alarcón-Riquelme, Marta E %A Schulte, Eva C %A Franke, Andre %A Karlsen, Tom H %A Valenti, Luca %A Zeberg, Hugo %A Richards, Brent %A Ganna, Andrea %A Boada, Mercè %A Rojas, Itziar %A Ruiz, Agustín %A Sánchez, Pascual %A Real, Luis Miguel %A Guillén-Navarro, Encarna %A Ayuso, Carmen %A González-Neira, Anna %A Riancho, José A %A Rojas-Martinez, Augusto %A Flores, Carlos %A Lapunzina, Pablo %A Carracedo, Ángel %X

Here we describe the results of a genome-wide study conducted in 11 939 COVID-19 positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (p < 5x10-8) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (p = 1.3x10-22 and p = 8.1x10-12, respectively), and for variants in 9q21.32 near TLE1 only among females (p = 4.4x10-8). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (p = 2.7x10-8) and ARHGAP33 (p = 1.3x10-8), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, p = 4.1x10-8). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (<60 or ≥ 60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided.

%B Hum Mol Genet %8 2022 Jun 16 %G eng %R 10.1093/hmg/ddac132 %0 Journal Article %J Mol Syst Biol %D 2021 %T COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. %A Ostaszewski, Marek %A Niarakis, Anna %A Mazein, Alexander %A Kuperstein, Inna %A Phair, Robert %A Orta-Resendiz, Aurelio %A Singh, Vidisha %A Aghamiri, Sara Sadat %A Acencio, Marcio Luis %A Glaab, Enrico %A Ruepp, Andreas %A Fobo, Gisela %A Montrone, Corinna %A Brauner, Barbara %A Frishman, Goar %A Monraz Gómez, Luis Cristóbal %A Somers, Julia %A Hoch, Matti %A Kumar Gupta, Shailendra %A Scheel, Julia %A Borlinghaus, Hanna %A Czauderna, Tobias %A Schreiber, Falk %A Montagud, Arnau %A Ponce de Leon, Miguel %A Funahashi, Akira %A Hiki, Yusuke %A Hiroi, Noriko %A Yamada, Takahiro G %A Dräger, Andreas %A Renz, Alina %A Naveez, Muhammad %A Bocskei, Zsolt %A Messina, Francesco %A Börnigen, Daniela %A Fergusson, Liam %A Conti, Marta %A Rameil, Marius %A Nakonecnij, Vanessa %A Vanhoefer, Jakob %A Schmiester, Leonard %A Wang, Muying %A Ackerman, Emily E %A Shoemaker, Jason E %A Zucker, Jeremy %A Oxford, Kristie %A Teuton, Jeremy %A Kocakaya, Ebru %A Summak, Gökçe Yağmur %A Hanspers, Kristina %A Kutmon, Martina %A Coort, Susan %A Eijssen, Lars %A Ehrhart, Friederike %A Rex, Devasahayam Arokia Balaya %A Slenter, Denise %A Martens, Marvin %A Pham, Nhung %A Haw, Robin %A Jassal, Bijay %A Matthews, Lisa %A Orlic-Milacic, Marija %A Senff Ribeiro, Andrea %A Rothfels, Karen %A Shamovsky, Veronica %A Stephan, Ralf %A Sevilla, Cristoffer %A Varusai, Thawfeek %A Ravel, Jean-Marie %A Fraser, Rupsha %A Ortseifen, Vera %A Marchesi, Silvia %A Gawron, Piotr %A Smula, Ewa %A Heirendt, Laurent %A Satagopam, Venkata %A Wu, Guanming %A Riutta, Anders %A Golebiewski, Martin %A Owen, Stuart %A Goble, Carole %A Hu, Xiaoming %A Overall, Rupert W %A Maier, Dieter %A Bauch, Angela %A Gyori, Benjamin M %A Bachman, John A %A Vega, Carlos %A Grouès, Valentin %A Vazquez, Miguel %A Porras, Pablo %A Licata, Luana %A Iannuccelli, Marta %A Sacco, Francesca %A Nesterova, Anastasia %A Yuryev, Anton %A de Waard, Anita %A Turei, Denes %A Luna, Augustin %A Babur, Ozgun %A Soliman, Sylvain %A Valdeolivas, Alberto %A Esteban-Medina, Marina %A Peña-Chilet, Maria %A Rian, Kinza %A Helikar, Tomáš %A Puniya, Bhanwar Lal %A Modos, Dezso %A Treveil, Agatha %A Olbei, Marton %A De Meulder, Bertrand %A Ballereau, Stephane %A Dugourd, Aurélien %A Naldi, Aurélien %A Noël, Vincent %A Calzone, Laurence %A Sander, Chris %A Demir, Emek %A Korcsmaros, Tamas %A Freeman, Tom C %A Augé, Franck %A Beckmann, Jacques S %A Hasenauer, Jan %A Wolkenhauer, Olaf %A Wilighagen, Egon L %A Pico, Alexander R %A Evelo, Chris T %A Gillespie, Marc E %A Stein, Lincoln D %A Hermjakob, Henning %A D'Eustachio, Peter %A Saez-Rodriguez, Julio %A Dopazo, Joaquin %A Valencia, Alfonso %A Kitano, Hiroaki %A Barillot, Emmanuel %A Auffray, Charles %A Balling, Rudi %A Schneider, Reinhard %K Antiviral Agents %K Computational Biology %K Computer Graphics %K COVID-19 %K Cytokines %K Data Mining %K Databases, Factual %K Gene Expression Regulation %K Host Microbial Interactions %K Humans %K Immunity, Cellular %K Immunity, Humoral %K Immunity, Innate %K Lymphocytes %K Metabolic Networks and Pathways %K Myeloid Cells %K Protein Interaction Mapping %K SARS-CoV-2 %K Signal Transduction %K Software %K Transcription Factors %K Viral Proteins %X

We 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.

%B Mol Syst Biol %V 17 %P e10387 %8 2021 10 %G eng %N 10 %1 https://www.ncbi.nlm.nih.gov/pubmed/34664389?dopt=Abstract %R 10.15252/msb.202110387 %0 Journal Article %J Nature Genetics %D 2021 %T The NCI Genomic Data Commons %A Heath, Allison P. %A Ferretti, Vincent %A Agrawal, Stuti %A An, Maksim %A Angelakos, James C. %A Arya, Renuka %A Bajari, Rosita %A Baqar, Bilal %A Barnowski, Justin H. B. %A Burt, Jeffrey %A Catton, Ann %A Chan, Brandon F. %A Chu, Fay %A Cullion, Kim %A Davidsen, Tanja %A Do, Phuong-My %A Dompierre, Christian %A Ferguson, Martin L. %A Fitzsimons, Michael S. %A Ford, Michael %A Fukuma, Miyuki %A Gaheen, Sharon %A Ganji, Gajanan L. %A Garcia, Tzintzuni I. %A George, Sameera S. %A Gerhard, Daniela S. %A Gerthoffert, Francois %A Gomez, Fauzi %A Han, Kang %A Hernandez, Kyle M. %A Issac, Biju %A Jackson, Richard %A Jensen, Mark A. %A Joshi, Sid %A Kadam, Ajinkya %A Khurana, Aishmit %A Kim, Kyle M. J. %A Kraft, Victoria E. %A Li, Shenglai %A Lichtenberg, Tara M. %A Lodato, Janice %A Lolla, Laxmi %A Martinov, Plamen %A Mazzone, Jeffrey A. %A Miller, Daniel P. %A Miller, Ian %A Miller, Joshua S. %A Miyauchi, Koji %A Murphy, Mark W. %A Nullet, Thomas %A Ogwara, Rowland O. %A Ortuño, Francisco M. %A Pedrosa, Jesús %A Pham, Phuong L. %A Popov, Maxim Y. %A Porter, James J. %A Powell, Raymond %A Rademacher, Karl %A Reid, Colin P. %A Rich, Samantha %A Rogel, Bessie %A Sahni, Himanso %A Savage, Jeremiah H. %A Schmitt, Kyle A. %A Simmons, Trevar J. %A Sislow, Joseph %A Spring, Jonathan %A Stein, Lincoln %A Sullivan, Sean %A Tang, Yajing %A Thiagarajan, Mathangi %A Troyer, Heather D. %A Wang, Chang %A Wang, Zhining %A West, Bedford L. %A Wilmer, Alex %A Wilson, Shane %A Wu, Kaman %A Wysocki, William P. %A Xiang, Linda %A Yamada, Joseph T. %A Yang, Liming %A Yu, Christine %A Yung, Christina K. %A Zenklusen, Jean Claude %A Zhang, Junjun %A Zhang, Zhenyu %A Zhao, Yuanheng %A Zubair, Ariz %A Staudt, Louis M. %A Grossman, Robert L. %B Nature Genetics %8 Oct-02-2022 %G eng %U http://www.nature.com/articles/s41588-021-00791-5 %! Nat Genet %R 10.1038/s41588-021-00791-5 %0 Journal Article %J Nat Commun %D 2021 %T Orchestrating and sharing large multimodal data for transparent and reproducible research. %A Mammoliti, Anthony %A Smirnov, Petr %A Nakano, Minoru %A Safikhani, Zhaleh %A Eeles, Christopher %A Seo, Heewon %A Nair, Sisira Kadambat %A Mer, Arvind S %A Smith, Ian %A Ho, Chantal %A Beri, Gangesh %A Kusko, Rebecca %A Lin, Eva %A Yu, Yihong %A Martin, Scott %A Hafner, Marc %A Haibe-Kains, Benjamin %X

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

%B Nat Commun %V 12 %P 5797 %8 2021 10 04 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/34608132?dopt=Abstract %R 10.1038/s41467-021-25974-w %0 Journal Article %J Nat Med %D 2021 %T Reporting guidelines for human microbiome research: the STORMS checklist. %A Mirzayi, Chloe %A Renson, Audrey %A Zohra, Fatima %A Elsafoury, Shaimaa %A Geistlinger, Ludwig %A Kasselman, Lora J %A Eckenrode, Kelly %A van de Wijgert, Janneke %A Loughman, Amy %A Marques, Francine Z %A MacIntyre, David A %A Arumugam, Manimozhiyan %A Azhar, Rimsha %A Beghini, Francesco %A Bergstrom, Kirk %A Bhatt, Ami %A Bisanz, Jordan E %A Braun, Jonathan %A Bravo, Hector Corrada %A Buck, Gregory A %A Bushman, Frederic %A Casero, David %A Clarke, Gerard %A Collado, Maria Carmen %A Cotter, Paul D %A Cryan, John F %A Demmer, Ryan T %A Devkota, Suzanne %A Elinav, Eran %A Escobar, Juan S %A Fettweis, Jennifer %A Finn, Robert D %A Fodor, Anthony A %A Forslund, Sofia %A Franke, Andre %A Furlanello, Cesare %A Gilbert, Jack %A Grice, Elizabeth %A Haibe-Kains, Benjamin %A Handley, Scott %A Herd, Pamela %A Holmes, Susan %A Jacobs, Jonathan P %A Karstens, Lisa %A Knight, Rob %A Knights, Dan %A Koren, Omry %A Kwon, Douglas S %A Langille, Morgan %A Lindsay, Brianna %A McGovern, Dermot %A McHardy, Alice C %A McWeeney, Shannon %A Mueller, Noel T %A Nezi, Luigi %A Olm, Matthew %A Palm, Noah %A Pasolli, Edoardo %A Raes, Jeroen %A Redinbo, Matthew R %A Rühlemann, Malte %A Balfour Sartor, R %A Schloss, Patrick D %A Schriml, Lynn %A Segal, Eran %A Shardell, Michelle %A Sharpton, Thomas %A Smirnova, Ekaterina %A Sokol, Harry %A Sonnenburg, Justin L %A Srinivasan, Sujatha %A Thingholm, Louise B %A Turnbaugh, Peter J %A Upadhyay, Vaibhav %A Walls, Ramona L %A Wilmes, Paul %A Yamada, Takuji %A Zeller, Georg %A Zhang, Mingyu %A Zhao, Ni %A Zhao, Liping %A Bao, Wenjun %A Culhane, Aedin %A Devanarayan, Viswanath %A Dopazo, Joaquin %A Fan, Xiaohui %A Fischer, Matthias %A Jones, Wendell %A Kusko, Rebecca %A Mason, Christopher E %A Mercer, Tim R %A Sansone, Susanna-Assunta %A Scherer, Andreas %A Shi, Leming %A Thakkar, Shraddha %A Tong, Weida %A Wolfinger, Russ %A Hunter, Christopher %A Segata, Nicola %A Huttenhower, Curtis %A Dowd, Jennifer B %A Jones, Heidi E %A Waldron, Levi %K Computational Biology %K Dysbiosis %K Humans %K Microbiota %K Observational Studies as Topic %K Research Design %K Translational Science, Biomedical %X

The 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.

%B Nat Med %V 27 %P 1885-1892 %8 2021 11 %G eng %N 11 %1 https://www.ncbi.nlm.nih.gov/pubmed/34789871?dopt=Abstract %R 10.1038/s41591-021-01552-x %0 Journal Article %J Cell Syst %D 2020 %T Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. %A Yang, Mi %A Petralia, Francesca %A Li, Zhi %A Li, Hongyang %A Ma, Weiping %A Song, Xiaoyu %A Kim, Sunkyu %A Lee, Heewon %A Yu, Han %A Lee, Bora %A Bae, Seohui %A Heo, Eunji %A Kaczmarczyk, Jan %A Stępniak, Piotr %A Warchoł, Michał %A Yu, Thomas %A Calinawan, Anna P %A Boutros, Paul C %A Payne, Samuel H %A Reva, Boris %A Boja, Emily %A Rodriguez, Henry %A Stolovitzky, Gustavo %A Guan, Yuanfang %A Kang, Jaewoo %A Wang, Pei %A Fenyö, David %A Saez-Rodriguez, Julio %K Crowdsourcing %K Female %K Genomics %K Humans %K Machine Learning %K Male %K Neoplasms %K Phosphoproteins %K Proteins %K Proteomics %K Transcriptome %X

Cancer 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.

%B Cell Syst %V 11 %P 186-195.e9 %8 2020 08 26 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/32710834?dopt=Abstract %R 10.1016/j.cels.2020.06.013 %0 Journal Article %J F1000Res %D 2020 %T The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research. %A Salgado, David %A Armean, Irina M %A Baudis, Michael %A Beltran, Sergi %A Capella-Gutíerrez, Salvador %A Carvalho-Silva, Denise %A Dominguez Del Angel, Victoria %A Dopazo, Joaquin %A Furlong, Laura I %A Gao, Bo %A Garcia, Leyla %A Gerloff, Dietlind %A Gut, Ivo %A Gyenesei, Attila %A Habermann, Nina %A Hancock, John M %A Hanauer, Marc %A Hovig, Eivind %A Johansson, Lennart F %A Keane, Thomas %A Korbel, Jan %A Lauer, Katharina B %A Laurie, Steve %A Leskošek, Brane %A Lloyd, David %A Marqués-Bonet, Tomás %A Mei, Hailiang %A Monostory, Katalin %A Piñero, Janet %A Poterlowicz, Krzysztof %A Rath, Ana %A Samarakoon, Pubudu %A Sanz, Ferran %A Saunders, Gary %A Sie, Daoud %A Swertz, Morris A %A Tsukanov, Kirill %A Valencia, Alfonso %A Vidak, Marko %A Yenyxe González, Cristina %A Ylstra, Bauke %A Béroud, Christophe %K Computational Biology %K DNA Copy Number Variations %K High-Throughput Nucleotide Sequencing %K Humans %X

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While "High-Throughput" sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.

%B F1000Res %V 9 %8 2020 %G eng %1 https://www.ncbi.nlm.nih.gov/pubmed/34367618?dopt=Abstract %& 1229 %R 10.12688/f1000research.24887.1 %0 Journal Article %J Nat Commun %D 2019 %T Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. %A Menden, Michael P %A Wang, Dennis %A Mason, Mike J %A Szalai, Bence %A Bulusu, Krishna C %A Guan, Yuanfang %A Yu, Thomas %A Kang, Jaewoo %A Jeon, Minji %A Wolfinger, Russ %A Nguyen, Tin %A Zaslavskiy, Mikhail %A Jang, In Sock %A Ghazoui, Zara %A Ahsen, Mehmet Eren %A Vogel, Robert %A Neto, Elias Chaibub %A Norman, Thea %A Tang, Eric K Y %A Garnett, Mathew J %A Veroli, Giovanni Y Di %A Fawell, Stephen %A Stolovitzky, Gustavo %A Guinney, Justin %A Dry, Jonathan R %A Saez-Rodriguez, Julio %K ADAM17 Protein %K Antineoplastic Combined Chemotherapy Protocols %K Benchmarking %K Biomarkers, Tumor %K Cell Line, Tumor %K Computational Biology %K Datasets as Topic %K Drug Antagonism %K Drug Resistance, Neoplasm %K Drug Synergism %K Genomics %K Humans %K Molecular Targeted Therapy %K mutation %K Neoplasms %K pharmacogenetics %K Phosphatidylinositol 3-Kinases %K Phosphoinositide-3 Kinase Inhibitors %K Treatment Outcome %X

The 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.

%B Nat Commun %V 10 %P 2674 %8 2019 06 17 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/31209238?dopt=Abstract %R 10.1038/s41467-019-09799-2 %0 Journal Article %J Nature Communications %D 2018 %T A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection %A Fourati, Slim %A Talla, Aarthi %A Mahmoudian, Mehrad %A Burkhart, Joshua G. %A Klén, Riku %A Henao, Ricardo %A Yu, Thomas %A Aydın, Zafer %A Yeung, Ka Yee %A Ahsen, Mehmet Eren %A Almugbel, Reem %A Jahandideh, Samad %A Liang, Xiao %A Nordling, Torbjörn E. M. %A Shiga, Motoki %A Stanescu, Ana %A Vogel, Robert %A Pandey, Gaurav %A Chiu, Christopher %A McClain, Micah T. %A Woods, Christopher W. %A Ginsburg, Geoffrey S. %A Elo, Laura L. %A Tsalik, Ephraim L. %A Mangravite, Lara M. %A Sieberts, Solveig K. %B Nature Communications %V 9 %8 Jan-12-2018 %G eng %U http://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/s41467-018-06735-8 %N 1 %! Nat Commun %R 10.1038/s41467-018-06735-8 %0 Journal Article %J Nature methods %D 2015 %T Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. %A Ewing, Adam D %A Houlahan, Kathleen E %A Hu, Yin %A Ellrott, Kyle %A Caloian, Cristian %A Yamaguchi, Takafumi N %A Bare, J Christopher %A P’ng, Christine %A Waggott, Daryl %A Sabelnykova, Veronica Y %A Kellen, Michael R %A Norman, Thea C %A Haussler, David %A Friend, Stephen H %A Stolovitzky, Gustavo %A Margolin, Adam A %A Stuart, Joshua M %A Boutros, Paul C %E ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants %E Liu Xi %E Ninad Dewal %E Yu Fan %E Wenyi Wang %E David Wheeler %E Andreas Wilm %E Grace Hui Ting %E Chenhao Li %E Denis Bertrand %E Niranjan Nagarajan %E Qing-Rong Chen %E Chih-Hao Hsu %E Ying Hu %E Chunhua Yan %E Warren Kibbe %E Daoud Meerzaman %E Kristian Cibulskis %E Mara Rosenberg %E Louis Bergelson %E Adam Kiezun %E Amie Radenbaugh %E Anne-Sophie Sertier %E Anthony Ferrari %E Laurie Tonton %E Kunal Bhutani %E Nancy F Hansen %E Difei Wang %E Lei Song %E Zhongwu Lai %E Liao, Yang %E Shi, Wei %E Carbonell-Caballero, José %E Joaquín Dopazo %E Cheryl C K Lau %E Justin Guinney %K cancer %K NGS %K variant calling %X The 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/. %B Nature methods %8 2015 May 18 %G eng %U http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html %R 10.1038/nmeth.3407 %0 Journal Article %J Nature biotechnology %D 2015 %T Prediction of human population responses to toxic compounds by a collaborative competition. %A Eduati, Federica %A Mangravite, Lara M %A Wang, Tao %A Tang, Hao %A Bare, J Christopher %A Huang, Ruili %A Norman, Thea %A Kellen, Mike %A Menden, Michael P %A Yang, Jichen %A Zhan, Xiaowei %A Zhong, Rui %A Xiao, Guanghua %A Xia, Menghang %A Abdo, Nour %A Kosyk, Oksana %X The 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. %B Nature biotechnology %8 2015 Aug 10 %G eng %U http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html %R 10.1038/nbt.3299 %0 Journal Article %J Nature communications %D 2014 %T Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. %A Munro, Sarah A %A Lund, Steven P %A Pine, P Scott %A Binder, Hans %A Clevert, Djork-Arné %A Ana Conesa %A Dopazo, Joaquin %A Fasold, Mario %A Hochreiter, Sepp %A Hong, Huixiao %A Jafari, Nadereh %A Kreil, David P %A Labaj, Paweł P %A Li, Sheng %A Liao, Yang %A Lin, Simon M %A Meehan, Joseph %A Mason, Christopher E %A Santoyo-López, Javier %A Setterquist, Robert A %A Shi, Leming %A Shi, Wei %A Smyth, Gordon K %A Stralis-Pavese, Nancy %A Su, Zhenqiang %A Tong, Weida %A Wang, Charles %A Wang, Jian %A Xu, Joshua %A Ye, Zhan %A Yang, Yong %A Yu, Ying %A Salit, Marc %K RNA-seq %X There 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. %B Nature communications %V 5 %P 5125 %8 2014 %G eng %U http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html %R 10.1038/ncomms6125 %0 Journal Article %J Hum Mol Genet %D 2011 %T Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells. %A Yung, Sun %A Ledran, Maria %A Moreno-Gimeno, Inmaculada %A Conesa, Ana %A Montaner, David %A Dopazo, Joaquin %A Dimmick, Ian %A Slater, Nicholas J %A Marenah, Lamin %A Real, Pedro J %A Paraskevopoulou, Iliana %A Bisbal, Viviana %A Burks, Deborah %A Santibanez-Koref, Mauro %A Moreno, Ruben %A Mountford, Joanne %A Menendez, Pablo %A Armstrong, Lyle %A Lako, Majlinda %K Acute Disease %K Anemia, Hemolytic %K Animals %K Basic Helix-Loop-Helix Transcription Factors %K Cell Differentiation %K Cell Line %K Cell Lineage %K Cluster Analysis %K Embryonic Stem Cells %K Erythroid Cells %K Flow Cytometry %K Gene Expression Profiling %K Hematopoietic Stem Cells %K Humans %K Mice %K Myeloid Cells %K Paracrine Communication %K Proto-Oncogene Proteins %K Reverse Transcriptase Polymerase Chain Reaction %K rho GTP-Binding Proteins %K Signal Transduction %K Stem Cell Transplantation %K T-Cell Acute Lymphocytic Leukemia Protein 1 %K Transcriptome %X

Understanding the transcriptional cues that direct differentiation of human embryonic stem cells (hESCs) and human-induced pluripotent stem cells to defined and functional cell types is essential for future clinical applications. In this study, we have compared transcriptional profiles of haematopoietic progenitors derived from hESCs at various developmental stages of a feeder- and serum-free differentiation method and show that the largest transcriptional changes occur during the first 4 days of differentiation. Data mining on the basis of molecular function revealed Rho-GTPase signalling as a key regulator of differentiation. Inhibition of this pathway resulted in a significant reduction in the numbers of emerging haematopoietic progenitors throughout the differentiation window, thereby uncovering a previously unappreciated role for Rho-GTPase signalling during human haematopoietic development. Our analysis indicated that SCL was the 11th most upregulated transcript during the first 4 days of the hESC differentiation process. Overexpression of SCL in hESCs promoted differentiation to meso-endodermal lineages, the emergence of haematopoietic and erythro-megakaryocytic progenitors and accelerated erythroid differentiation. Importantly, intrasplenic transplantation of SCL-overexpressing hESC-derived haematopoietic cells enhanced recovery from induced acute anaemia without significant cell engraftment, suggesting a paracrine-mediated effect.

%B Hum Mol Genet %V 20 %P 4932-46 %8 2011 Dec 15 %G eng %N 24 %1 https://www.ncbi.nlm.nih.gov/pubmed/21937587?dopt=Abstract %R 10.1093/hmg/ddr431 %0 Journal Article %J The ISME journal %D 2010 %T Fine-scale evolution: genomic, phenotypic and ecological differentiation in two coexisting Salinibacter ruber strains. %A Peña, Arantxa %A Teeling, Hanno %A Huerta-Cepas, Jaime %A Santos, Fernando %A Yarza, Pablo %A Brito-Echeverría, Jocelyn %A Lucio, Marianna %A Schmitt-Kopplin, Philippe %A Meseguer, Inmaculada %A Schenowitz, Chantal %A Dossat, Carole %A Barbe, Valerie %A Joaquín Dopazo %A Rosselló-Mora, Ramon %A Schüler, Margarete %A Glöckner, Frank Oliver %A Amann, Rudolf %A Gabaldón, Toni %A Antón, Josefa %X

Genomic and metagenomic data indicate a high degree of genomic variation within microbial populations, although the ecological and evolutive meaning of this microdiversity remains unknown. Microevolution analyses, including genomic and experimental approaches, are so far very scarce for non-pathogenic bacteria. In this study, we compare the genomes, metabolomes and selected ecological traits of the strains M8 and M31 of the hyperhalophilic bacterium Salinibacter ruber that contain ribosomal RNA (rRNA) gene and intergenic regions that are identical in sequence and were simultaneously isolated from a Mediterranean solar saltern. Comparative analyses indicate that S. ruber genomes present a mosaic structure with conserved and hypervariable regions (HVRs). The HVRs or genomic islands, are enriched in transposases, genes related to surface properties, strain-specific genes and highly divergent orthologous. However, the many indels outside the HVRs indicate that genome plasticity extends beyond them. Overall, 10% of the genes encoded in the M8 genome are absent from M31 and could stem from recent acquisitions. S. ruber genomes also harbor 34 genes located outside HVRs that are transcribed during standard growth and probably derive from lateral gene transfers with Archaea preceding the M8/M31 divergence. Metabolomic analyses, phage susceptibility and competition experiments indicate that these genomic differences cannot be considered neutral from an ecological perspective. The results point to the avoidance of competition by micro-niche adaptation and response to viral predation as putative major forces that drive microevolution within these Salinibacter strains. In addition, this work highlights the extent of bacterial functional diversity and environmental adaptation, beyond the resolution of the 16S rRNA and internal transcribed spacers regions.The ISME Journal advance online publication, 18 February 2010; doi:10.1038/ismej.2010.6.

%B The ISME journal %8 2010 Feb 18 %G eng %0 Journal Article %J Nature biotechnology %D 2010 %T The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. %A Shi, Leming %A Campbell, Gregory %A Jones, Wendell D %A Campagne, Fabien %A Wen, Zhining %A Walker, Stephen J %A Su, Zhenqiang %A Chu, Tzu-Ming %A Goodsaid, Federico M %A Pusztai, Lajos %A Shaughnessy, John D %A Oberthuer, André %A Thomas, Russell S %A Paules, Richard S %A Fielden, Mark %A Barlogie, Bart %A Chen, Weijie %A Du, Pan %A Fischer, Matthias %A Furlanello, Cesare %A Gallas, Brandon D %A Ge, Xijin %A Megherbi, Dalila B %A Symmans, W Fraser %A Wang, May D %A Zhang, John %A Bitter, Hans %A Brors, Benedikt %A Bushel, Pierre R %A Bylesjo, Max %A Chen, Minjun %A Cheng, Jie %A Cheng, Jing %A Chou, Jeff %A Davison, Timothy S %A Delorenzi, Mauro %A Deng, Youping %A Devanarayan, Viswanath %A Dix, David J %A Dopazo, Joaquin %A Dorff, Kevin C %A Elloumi, Fathi %A Fan, Jianqing %A Fan, Shicai %A Fan, Xiaohui %A Fang, Hong %A Gonzaludo, Nina %A Hess, Kenneth R %A Hong, Huixiao %A Huan, Jun %A Irizarry, Rafael A %A Judson, Richard %A Juraeva, Dilafruz %A Lababidi, Samir %A Lambert, Christophe G %A Li, Li %A Li, Yanen %A Li, Zhen %A Lin, Simon M %A Liu, Guozhen %A Lobenhofer, Edward K %A Luo, Jun %A Luo, Wen %A McCall, Matthew N %A Nikolsky, Yuri %A Pennello, Gene A %A Perkins, Roger G %A Philip, Reena %A Popovici, Vlad %A Price, Nathan D %A Qian, Feng %A Scherer, Andreas %A Shi, Tieliu %A Shi, Weiwei %A Sung, Jaeyun %A Thierry-Mieg, Danielle %A Thierry-Mieg, Jean %A Thodima, Venkata %A Trygg, Johan %A Vishnuvajjala, Lakshmi %A Wang, Sue Jane %A Wu, Jianping %A Wu, Yichao %A Xie, Qian %A Yousef, Waleed A %A Zhang, Liang %A Zhang, Xuegong %A Zhong, Sheng %A Zhou, Yiming %A Zhu, Sheng %A Arasappan, Dhivya %A Bao, Wenjun %A Lucas, Anne Bergstrom %A Berthold, Frank %A Brennan, Richard J %A Buness, Andreas %A Catalano, Jennifer G %A Chang, Chang %A Chen, Rong %A Cheng, Yiyu %A Cui, Jian %A Czika, Wendy %A Demichelis, Francesca %A Deng, Xutao %A Dosymbekov, Damir %A Eils, Roland %A Feng, Yang %A Fostel, Jennifer %A Fulmer-Smentek, Stephanie %A Fuscoe, James C %A Gatto, Laurent %A Ge, Weigong %A Goldstein, Darlene R %A Guo, Li %A Halbert, Donald N %A Han, Jing %A Harris, Stephen C %A Hatzis, Christos %A Herman, Damir %A Huang, Jianping %A Jensen, Roderick V %A Jiang, Rui %A Johnson, Charles D %A Jurman, Giuseppe %A Kahlert, Yvonne %A Khuder, Sadik A %A Kohl, Matthias %A Li, Jianying %A Li, Li %A Li, Menglong %A Li, Quan-Zhen %A Li, Shao %A Li, Zhiguang %A Liu, Jie %A Liu, Ying %A Liu, Zhichao %A Meng, Lu %A Madera, Manuel %A Martinez-Murillo, Francisco %A Medina, Ignacio %A Meehan, Joseph %A Miclaus, Kelci %A Moffitt, Richard A %A Montaner, David %A Mukherjee, Piali %A Mulligan, George J %A Neville, Padraic %A Nikolskaya, Tatiana %A Ning, Baitang %A Page, Grier P %A Parker, Joel %A Parry, R Mitchell %A Peng, Xuejun %A Peterson, Ron L %A Phan, John H %A Quanz, Brian %A Ren, Yi %A Riccadonna, Samantha %A Roter, Alan H %A Samuelson, Frank W %A Schumacher, Martin M %A Shambaugh, Joseph D %A Shi, Qiang %A Shippy, Richard %A Si, Shengzhu %A Smalter, Aaron %A Sotiriou, Christos %A Soukup, Mat %A Staedtler, Frank %A Steiner, Guido %A Stokes, Todd H %A Sun, Qinglan %A Tan, Pei-Yi %A Tang, Rong %A Tezak, Zivana %A Thorn, Brett %A Tsyganova, Marina %A Turpaz, Yaron %A Vega, Silvia C %A Visintainer, Roberto %A von Frese, Juergen %A Wang, Charles %A Wang, Eric %A Wang, Junwei %A Wang, Wei %A Westermann, Frank %A Willey, James C %A Woods, Matthew %A Wu, Shujian %A Xiao, Nianqing %A Xu, Joshua %A Xu, Lei %A Yang, Lun %A Zeng, Xiao %A Zhang, Jialu %A Zhang, Li %A Zhang, Min %A Zhao, Chen %A Puri, Raj K %A Scherf, Uwe %A Tong, Weida %A Wolfinger, Russell D %X

Gene 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.

%B Nature biotechnology %V 28 %P 827-38 %8 2010 Aug %G eng %U http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html %0 Journal Article %J Nucleic Acids Res %D 2005 %T GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data %A Vaquerizas, J. M. %A L. Conde %A Yankilevich, P. %A Cabezon, A. %A Minguez, P. %A Diaz-Uriarte, R. %A Fatima Al-Shahrour %A Herrero, J. %A Dopazo, J. %K gepas %K microarray data analysis %X

The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With >76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

%B Nucleic Acids Res %V 33 %P W616-20 %G eng %U http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15980548 %0 Journal Article %J J Biol Chem %D 2003 %T Examining the role of glutamic acid 183 in chloroperoxidase catalysis %A Yi, X. %A A. Conesa %A Punt, P. J. %A Hager, L. P. %K Aspergillus niger/metabolism Catalase/metabolism Catalysis Chloride Peroxidase/*chemistry/*metabolism Chlorine/metabolism Chromatography %K Ion Exchange Circular Dichroism Crystallography %K Polyacrylamide Gel Fungi/enzymology Glutamic Acid/*chemistry Histidine/chemistry/metabolism Hydrogen-Ion Concentration Immunoblotting Isoelectric Focusing Mutation Oxidoreductases/metabolism Plasmids/metabolism %K X-Ray Electrophoresis %X Site-directed mutagenesis has been used to investigate the role of glutamic acid 183 in chloroperoxidase catalysis. Based on the x-ray crystallographic structure of chloroperoxidase, Glu-183 is postulated to function on distal side of the heme prosthetic group as an acid-base catalyst in facilitating the reaction between the peroxidase and hydrogen peroxide with the formation of Compound I. In contrast, the other members of the heme peroxidase family use a histidine residue in this role. Plasmids have now been constructed in which the codon for Glu-183 is replaced with a histidine codon. The mutant recombinant gene has been expressed in Aspergillus niger. An analysis of the produced mutant gene shows that the substitution of Glu-183 with a His residue is detrimental to the chlorination and dismutation activity of chloroperoxidase. The activity is reduced by 85 and 50% of wild type activity, respectively. However, quite unexpectedly, the epoxidation activity of the mutant enzyme is significantly enhanced approximately 2.5-fold. These results show that Glu-183 is important but not essential for the chlorination activity of chloroperoxidase. It is possible that the increased epoxidation of the mutant enzyme is based on an increase in the hydrophobicity of the active site. %B J Biol Chem %V 278 %P 13855-9 %G eng %U http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12576477 %0 Journal Article %J Nucleic Acids Res %D 2003 %T Tools for comparative protein structure modeling and analysis %A Eswar, N. %A John, B. %A Mirkovic, N. %A Fiser, A. %A Ilyin, V. A. %A Pieper, U. %A Stuart, A. C. %A M. A. Marti-Renom %A Madhusudhan, M. S. %A Yerkovich, B. %A Sali, A. %K Amino Acid *Software *Structural Homology %K Internet Models %K Molecular Protein Folding Proteins/chemistry Reproducibility of Results Sequence Alignment Sequence Homology %K Protein Systems Integration %X The following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution. %B Nucleic Acids Res %V 31 %P 3375-80 %G eng %U http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12824331