@article {805, title = {Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.}, journal = {Front Immunol}, volume = {14}, year = {2024}, month = {2023}, pages = {1282859}, abstract = {

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.

}, keywords = {Computer Simulation, COVID-19, drug repositioning, Humans, SARS-CoV-2, Systems biology}, issn = {1664-3224}, doi = {10.3389/fimmu.2023.1282859}, author = {Niarakis, Anna and Ostaszewski, Marek and Mazein, Alexander and Kuperstein, Inna and Kutmon, Martina and Gillespie, Marc E and Funahashi, Akira and Acencio, Marcio Luis and Hemedan, Ahmed and Aichem, Michael and Klein, Karsten and Czauderna, Tobias and Burtscher, Felicia and Yamada, Takahiro G and Hiki, Yusuke and Hiroi, Noriko F and Hu, Finterly and Pham, Nhung and Ehrhart, Friederike and Willighagen, Egon L and Valdeolivas, Alberto and Dugourd, Aur{\'e}lien and Messina, Francesco and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Soliman, Sylvain and Aghamiri, Sara Sadat and Puniya, Bhanwar Lal and Naldi, Aur{\'e}lien and Helikar, Tom{\'a}{\v s} and Singh, Vidisha and Fern{\'a}ndez, Marco Fari{\~n}as and Bermudez, Viviam and Tsirvouli, Eirini and Montagud, Arnau and No{\"e}l, Vincent and Ponce-de-Leon, Miguel and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Luna, Augustin and Pi{\~n}ero, Janet and Furlong, Laura I and Balaur, Irina and Rougny, Adrien and Jarosz, Yohan and Overall, Rupert W and Phair, Robert and Perfetto, Livia and Matthews, Lisa and Rex, Devasahayam Arokia Balaya and Orlic-Milacic, Marija and Gomez, Luis Cristobal Monraz and De Meulder, Bertrand and Ravel, Jean Marie and Jassal, Bijay and Satagopam, Venkata and Wu, Guanming and Golebiewski, Martin and Gawron, Piotr and Calzone, Laurence and Beckmann, Jacques S and Evelo, Chris T and D{\textquoteright}Eustachio, Peter and Schreiber, Falk and Saez-Rodriguez, Julio and Dopazo, Joaquin and Kuiper, Martin and Valencia, Alfonso and Wolkenhauer, Olaf and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {767, title = {Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging.}, journal = {Commun Biol}, volume = {6}, year = {2023}, month = {2023 Mar 08}, pages = {250}, abstract = {

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.

}, issn = {2399-3642}, doi = {10.1038/s42003-023-04625-4}, author = {Sola-Garc{\'\i}a, Alejandro and C{\'a}liz-Molina, Mar{\'\i}a {\'A}ngeles and Espadas, Isabel and Petr, Michael and Panadero-Mor{\'o}n, Concepci{\'o}n and Gonz{\'a}lez-Mor{\'a}n, Daniel and Mart{\'\i}n-V{\'a}zquez, Mar{\'\i}a Eugenia and Narbona-P{\'e}rez, {\'A}lvaro Jes{\'u}s and L{\'o}pez-Noriega, Livia and Mart{\'\i}nez-Corrales, Guillermo and L{\'o}pez-Fern{\'a}ndez-Sobrino, Ra{\'u}l and Carmona-Marin, Lina M and Mart{\'\i}nez-Force, Enrique and Yanes, Oscar and Vinaixa, Maria and L{\'o}pez-L{\'o}pez, Daniel and Reyes, Jos{\'e} Carlos and Dopazo, Joaquin and Mart{\'\i}n, Franz and Gauthier, Benoit R and Scheibye-Knudsen, Morten and Capilla-Gonz{\'a}lez, Vivian and Mart{\'\i}n-Montalvo, Alejandro} } @article {760, title = {Novel genes and sex differences in COVID-19 severity.}, journal = {Hum Mol Genet}, year = {2022}, month = {2022 Jun 16}, abstract = {

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.

}, issn = {1460-2083}, doi = {10.1093/hmg/ddac132}, author = {Cruz, Raquel and Almeida, Silvia Diz-de and Heredia, Miguel L{\'o}pez and Quintela, In{\'e}s and Ceballos, Francisco C and Pita, Guillermo and Lorenzo-Salazar, Jos{\'e} M and Gonz{\'a}lez-Montelongo, Rafaela and Gago-Dom{\'\i}nguez, Manuela and Porras, Marta Sevilla and Casta{\~n}o, Jair Antonio Tenorio and Nevado, Juli{\'a}n and Aguado, Jose Mar{\'\i}a and Aguilar, Carlos and Aguilera-Albesa, Sergio and Almadana, Virginia and Almoguera, Berta and Alvarez, Nuria and Andreu-Bernabeu, {\'A}lvaro and Arana-Arri, Eunate and Arango, Celso and Arranz, Mar{\'\i}a J and Artiga, Maria-Jesus and Baptista-Rosas, Ra{\'u}l C and Barreda-S{\'a}nchez, Mar{\'\i}a and Belhassen-Garcia, Moncef and Bezerra, Joao F and Bezerra, Marcos A C and Boix-Palop, Luc{\'\i}a and Bri{\'o}n, Maria and Brugada, Ram{\'o}n and Bustos, Matilde and Calder{\'o}n, Enrique J and Carbonell, Cristina and Castano, Luis and Castelao, Jose E and Conde-Vicente, Rosa and Cordero-Lorenzana, M Lourdes and Cortes-Sanchez, Jose L and Corton, Marta and Darnaude, M Teresa and De Martino-Rodr{\'\i}guez, Alba and Campo-P{\'e}rez, Victor and Bustamante, Aranzazu Diaz and Dom{\'\i}nguez-Garrido, Elena and Luchessi, Andr{\'e} D and Eir{\'o}s, Roc{\'\i}o and Sanabria, Gladys Mercedes Estigarribia and Fari{\~n}as, Mar{\'\i}a Carmen and Fern{\'a}ndez-Robelo, Ux{\'\i}a and Fern{\'a}ndez-Rodr{\'\i}guez, Amanda and Fern{\'a}ndez-Villa, Tania and Gil-Fournier, Bel{\'e}n and G{\'o}mez-Arrue, Javier and {\'A}lvarez, Beatriz Gonz{\'a}lez and Quir{\'o}s, Fernan Gonzalez Bernaldo and Gonz{\'a}lez-Pe{\~n}as, Javier and Guti{\'e}rrez-Bautista, Juan F and Herrero, Mar{\'\i}a Jos{\'e} and Herrero-Gonzalez, Antonio and Jimenez-Sousa, Mar{\'\i}a A and Lattig, Mar{\'\i}a Claudia and Borja, Anabel Liger and Lopez-Rodriguez, Rosario and Mancebo, Esther and Mart{\'\i}n-L{\'o}pez, Caridad and Mart{\'\i}n, Vicente and Martinez-Nieto, Oscar and Martinez-Lopez, Iciar and Martinez-Resendez, Michel F and Martinez-Perez, {\'A}ngel and Mazzeu, Juliana A and Mac{\'\i}as, Eleuterio Merayo and Minguez, Pablo and Cuerda, Victor Moreno and Silbiger, Vivian N and Oliveira, Silviene F and Ortega-Paino, Eva and Parellada, Mara and Paz-Artal, Estela and Santos, Ney P C and P{\'e}rez-Matute, Patricia and Perez, Patricia and P{\'e}rez-Tom{\'a}s, M Elena and Perucho, Teresa and Pinsach-Abuin, Mel Lina and Pompa-Mera, Ericka N and Porras-Hurtado, Gloria L and Pujol, Aurora and Le{\'o}n, Soraya Ramiro and Resino, Salvador and Fernandes, Marianne R and Rodr{\'\i}guez-Ruiz, Emilio and Rodriguez-Artalejo, Fernando and Rodriguez-Garcia, Jos{\'e} A and Ruiz-Cabello, Francisco and Ruiz-Hornillos, Javier and Ryan, Pablo and Soria, Jos{\'e} Manuel and Souto, Juan Carlos and Tamayo, Eduardo and Tamayo-Velasco, Alvaro and Taracido-Fernandez, Juan Carlos and Teper, Alejandro and Torres-Tobar, Lilian and Urioste, Miguel and Valencia-Ramos, Juan and Y{\'a}{\~n}ez, Zuleima and Zarate, Ruth and Nakanishi, Tomoko and Pigazzini, Sara and Degenhardt, Frauke and Butler-Laporte, Guillaume and Maya-Miles, Douglas and Bujanda, Luis and Bouysran, Youssef and Palom, Adriana and Ellinghaus, David and Mart{\'\i}nez-Bueno, Manuel and Rolker, Selina and Amitrano, Sara and Roade, Luisa and Fava, Francesca and Spinner, Christoph D and Prati, Daniele and Bernardo, David and Garc{\'\i}a, Federico and Darcis, Gilles and Fern{\'a}ndez-Cadenas, Israel and Holter, Jan Cato and Banales, Jesus M and Frithiof, Robert and Duga, Stefano and Asselta, Rosanna and Pereira, Alexandre C and Romero-G{\'o}mez, Manuel and Nafr{\'\i}a-Jim{\'e}nez, Beatriz and Hov, Johannes R and Migeotte, Isabelle and Renieri, Alessandra and Planas, Anna M and Ludwig, Kerstin U and Buti, Maria and Rahmouni, Souad and Alarc{\'o}n-Riquelme, Marta E and Schulte, Eva C and Franke, Andre and Karlsen, Tom H and Valenti, Luca and Zeberg, Hugo and Richards, Brent and Ganna, Andrea and Boada, Merc{\`e} and Rojas, Itziar and Ruiz, Agust{\'\i}n and S{\'a}nchez, Pascual and Real, Luis Miguel and Guill{\'e}n-Navarro, Encarna and Ayuso, Carmen and Gonz{\'a}lez-Neira, Anna and Riancho, Jos{\'e} A and Rojas-Martinez, Augusto and Flores, Carlos and Lapunzina, Pablo and Carracedo, {\'A}ngel} } @article {736, title = {COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.}, journal = {Mol Syst Biol}, volume = {17}, year = {2021}, month = {2021 10}, pages = {e10387}, abstract = {

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.

}, keywords = {Antiviral Agents, Computational Biology, Computer Graphics, COVID-19, Cytokines, Data Mining, Databases, Factual, Gene Expression Regulation, Host Microbial Interactions, Humans, Immunity, Cellular, Immunity, Humoral, Immunity, Innate, Lymphocytes, Metabolic Networks and Pathways, Myeloid Cells, Protein Interaction Mapping, SARS-CoV-2, Signal Transduction, Software, Transcription Factors, Viral Proteins}, issn = {1744-4292}, doi = {10.15252/msb.202110387}, author = {Ostaszewski, Marek and Niarakis, Anna and Mazein, Alexander and Kuperstein, Inna and Phair, Robert and Orta-Resendiz, Aurelio and Singh, Vidisha and Aghamiri, Sara Sadat and Acencio, Marcio Luis and Glaab, Enrico and Ruepp, Andreas and Fobo, Gisela and Montrone, Corinna and Brauner, Barbara and Frishman, Goar and Monraz G{\'o}mez, Luis Crist{\'o}bal and Somers, Julia and Hoch, Matti and Kumar Gupta, Shailendra and Scheel, Julia and Borlinghaus, Hanna and Czauderna, Tobias and Schreiber, Falk and Montagud, Arnau and Ponce de Leon, Miguel and Funahashi, Akira and Hiki, Yusuke and Hiroi, Noriko and Yamada, Takahiro G and Dr{\"a}ger, Andreas and Renz, Alina and Naveez, Muhammad and Bocskei, Zsolt and Messina, Francesco and B{\"o}rnigen, Daniela and Fergusson, Liam and Conti, Marta and Rameil, Marius and Nakonecnij, Vanessa and Vanhoefer, Jakob and Schmiester, Leonard and Wang, Muying and Ackerman, Emily E and Shoemaker, Jason E and Zucker, Jeremy and Oxford, Kristie and Teuton, Jeremy and Kocakaya, Ebru and Summak, G{\"o}k{\c c}e Ya{\u g}mur and Hanspers, Kristina and Kutmon, Martina and Coort, Susan and Eijssen, Lars and Ehrhart, Friederike and Rex, Devasahayam Arokia Balaya and Slenter, Denise and Martens, Marvin and Pham, Nhung and Haw, Robin and Jassal, Bijay and Matthews, Lisa and Orlic-Milacic, Marija and Senff Ribeiro, Andrea and Rothfels, Karen and Shamovsky, Veronica and Stephan, Ralf and Sevilla, Cristoffer and Varusai, Thawfeek and Ravel, Jean-Marie and Fraser, Rupsha and Ortseifen, Vera and Marchesi, Silvia and Gawron, Piotr and Smula, Ewa and Heirendt, Laurent and Satagopam, Venkata and Wu, Guanming and Riutta, Anders and Golebiewski, Martin and Owen, Stuart and Goble, Carole and Hu, Xiaoming and Overall, Rupert W and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Vega, Carlos and Grou{\`e}s, Valentin and Vazquez, Miguel and Porras, Pablo and Licata, Luana and Iannuccelli, Marta and Sacco, Francesca and Nesterova, Anastasia and Yuryev, Anton and de Waard, Anita and Turei, Denes and Luna, Augustin and Babur, Ozgun and Soliman, Sylvain and Valdeolivas, Alberto and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Helikar, Tom{\'a}{\v s} and Puniya, Bhanwar Lal and Modos, Dezso and Treveil, Agatha and Olbei, Marton and De Meulder, Bertrand and Ballereau, Stephane and Dugourd, Aur{\'e}lien and Naldi, Aur{\'e}lien and No{\"e}l, Vincent and Calzone, Laurence and Sander, Chris and Demir, Emek and Korcsmaros, Tamas and Freeman, Tom C and Aug{\'e}, Franck and Beckmann, Jacques S and Hasenauer, Jan and Wolkenhauer, Olaf and Wilighagen, Egon L and Pico, Alexander R and Evelo, Chris T and Gillespie, Marc E and Stein, Lincoln D and Hermjakob, Henning and D{\textquoteright}Eustachio, Peter and Saez-Rodriguez, Julio and Dopazo, Joaquin and Valencia, Alfonso and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {714, title = {The NCI Genomic Data Commons}, journal = {Nature Genetics}, year = {2021}, month = {Oct-02-2022}, issn = {1061-4036}, doi = {10.1038/s41588-021-00791-5}, url = {http://www.nature.com/articles/s41588-021-00791-5}, author = {Heath, Allison P. and Ferretti, Vincent and Agrawal, Stuti and An, Maksim and Angelakos, James C. and Arya, Renuka and Bajari, Rosita and Baqar, Bilal and Barnowski, Justin H. B. and Burt, Jeffrey and Catton, Ann and Chan, Brandon F. and Chu, Fay and Cullion, Kim and Davidsen, Tanja and Do, Phuong-My and Dompierre, Christian and Ferguson, Martin L. and Fitzsimons, Michael S. and Ford, Michael and Fukuma, Miyuki and Gaheen, Sharon and Ganji, Gajanan L. and Garcia, Tzintzuni I. and George, Sameera S. and Gerhard, Daniela S. and Gerthoffert, Francois and Gomez, Fauzi and Han, Kang and Hernandez, Kyle M. and Issac, Biju and Jackson, Richard and Jensen, Mark A. and Joshi, Sid and Kadam, Ajinkya and Khurana, Aishmit and Kim, Kyle M. J. and Kraft, Victoria E. and Li, Shenglai and Lichtenberg, Tara M. and Lodato, Janice and Lolla, Laxmi and Martinov, Plamen and Mazzone, Jeffrey A. and Miller, Daniel P. and Miller, Ian and Miller, Joshua S. and Miyauchi, Koji and Murphy, Mark W. and Nullet, Thomas and Ogwara, Rowland O. and Ortu{\~n}o, Francisco M. and Pedrosa, Jes{\'u}s and Pham, Phuong L. and Popov, Maxim Y. and Porter, James J. and Powell, Raymond and Rademacher, Karl and Reid, Colin P. and Rich, Samantha and Rogel, Bessie and Sahni, Himanso and Savage, Jeremiah H. and Schmitt, Kyle A. and Simmons, Trevar J. and Sislow, Joseph and Spring, Jonathan and Stein, Lincoln and Sullivan, Sean and Tang, Yajing and Thiagarajan, Mathangi and Troyer, Heather D. and Wang, Chang and Wang, Zhining and West, Bedford L. and Wilmer, Alex and Wilson, Shane and Wu, Kaman and Wysocki, William P. and Xiang, Linda and Yamada, Joseph T. and Yang, Liming and Yu, Christine and Yung, Christina K. and Zenklusen, Jean Claude and Zhang, Junjun and Zhang, Zhenyu and Zhao, Yuanheng and Zubair, Ariz and Staudt, Louis M. and Grossman, Robert L.} } @article {734, title = {Orchestrating and sharing large multimodal data for transparent and reproducible research.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 10 04}, pages = {5797}, abstract = {

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.

}, issn = {2041-1723}, doi = {10.1038/s41467-021-25974-w}, author = {Mammoliti, Anthony and Smirnov, Petr and Nakano, Minoru and Safikhani, Zhaleh and Eeles, Christopher and Seo, Heewon and Nair, Sisira Kadambat and Mer, Arvind S and Smith, Ian and Ho, Chantal and Beri, Gangesh and Kusko, Rebecca and Lin, Eva and Yu, Yihong and Martin, Scott and Hafner, Marc and Haibe-Kains, Benjamin} } @article {742, title = {Reporting guidelines for human microbiome research: the STORMS checklist.}, journal = {Nat Med}, volume = {27}, year = {2021}, month = {2021 11}, pages = {1885-1892}, abstract = {

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 {\textquoteright}Strengthening The Organization and Reporting of Microbiome Studies{\textquoteright} (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.

}, keywords = {Computational Biology, Dysbiosis, Humans, Microbiota, Observational Studies as Topic, Research Design, Translational Science, Biomedical}, issn = {1546-170X}, doi = {10.1038/s41591-021-01552-x}, author = {Mirzayi, Chloe and Renson, Audrey and Zohra, Fatima and Elsafoury, Shaimaa and Geistlinger, Ludwig and Kasselman, Lora J and Eckenrode, Kelly and van de Wijgert, Janneke and Loughman, Amy and Marques, Francine Z and MacIntyre, David A and Arumugam, Manimozhiyan and Azhar, Rimsha and Beghini, Francesco and Bergstrom, Kirk and Bhatt, Ami and Bisanz, Jordan E and Braun, Jonathan and Bravo, Hector Corrada and Buck, Gregory A and Bushman, Frederic and Casero, David and Clarke, Gerard and Collado, Maria Carmen and Cotter, Paul D and Cryan, John F and Demmer, Ryan T and Devkota, Suzanne and Elinav, Eran and Escobar, Juan S and Fettweis, Jennifer and Finn, Robert D and Fodor, Anthony A and Forslund, Sofia and Franke, Andre and Furlanello, Cesare and Gilbert, Jack and Grice, Elizabeth and Haibe-Kains, Benjamin and Handley, Scott and Herd, Pamela and Holmes, Susan and Jacobs, Jonathan P and Karstens, Lisa and Knight, Rob and Knights, Dan and Koren, Omry and Kwon, Douglas S and Langille, Morgan and Lindsay, Brianna and McGovern, Dermot and McHardy, Alice C and McWeeney, Shannon and Mueller, Noel T and Nezi, Luigi and Olm, Matthew and Palm, Noah and Pasolli, Edoardo and Raes, Jeroen and Redinbo, Matthew R and R{\"u}hlemann, Malte and Balfour Sartor, R and Schloss, Patrick D and Schriml, Lynn and Segal, Eran and Shardell, Michelle and Sharpton, Thomas and Smirnova, Ekaterina and Sokol, Harry and Sonnenburg, Justin L and Srinivasan, Sujatha and Thingholm, Louise B and Turnbaugh, Peter J and Upadhyay, Vaibhav and Walls, Ramona L and Wilmes, Paul and Yamada, Takuji and Zeller, Georg and Zhang, Mingyu and Zhao, Ni and Zhao, Liping and Bao, Wenjun and Culhane, Aedin and Devanarayan, Viswanath and Dopazo, Joaquin and Fan, Xiaohui and Fischer, Matthias and Jones, Wendell and Kusko, Rebecca and Mason, Christopher E and Mercer, Tim R and Sansone, Susanna-Assunta and Scherer, Andreas and Shi, Leming and Thakkar, Shraddha and Tong, Weida and Wolfinger, Russ and Hunter, Christopher and Segata, Nicola and Huttenhower, Curtis and Dowd, Jennifer B and Jones, Heidi E and Waldron, Levi} } @article {696, title = {Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.}, journal = {Cell Syst}, volume = {11}, year = {2020}, month = {2020 08 26}, pages = {186-195.e9}, abstract = {

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{\textquoteright}s transparent peer review process is included in the Supplemental Information.

}, keywords = {Crowdsourcing, Female, Genomics, Humans, Machine Learning, Male, Neoplasms, Phosphoproteins, Proteins, Proteomics, Transcriptome}, issn = {2405-4720}, doi = {10.1016/j.cels.2020.06.013}, author = {Yang, Mi and Petralia, Francesca and Li, Zhi and Li, Hongyang and Ma, Weiping and Song, Xiaoyu and Kim, Sunkyu and Lee, Heewon and Yu, Han and Lee, Bora and Bae, Seohui and Heo, Eunji and Kaczmarczyk, Jan and St{\k e}pniak, Piotr and Warcho{\l}, Micha{\l} and Yu, Thomas and Calinawan, Anna P and Boutros, Paul C and Payne, Samuel H and Reva, Boris and Boja, Emily and Rodriguez, Henry and Stolovitzky, Gustavo and Guan, Yuanfang and Kang, Jaewoo and Wang, Pei and Feny{\"o}, David and Saez-Rodriguez, Julio} } @article {729, title = {The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research.}, journal = {F1000Res}, volume = {9}, year = {2020}, month = {2020}, chapter = {1229}, abstract = {

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{\textquoteright}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.

}, keywords = {Computational Biology, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans}, issn = {2046-1402}, doi = {10.12688/f1000research.24887.1}, author = {Salgado, David and Armean, Irina M and Baudis, Michael and Beltran, Sergi and Capella-Gut{\'\i}errez, Salvador and Carvalho-Silva, Denise and Dominguez Del Angel, Victoria and Dopazo, Joaquin and Furlong, Laura I and Gao, Bo and Garcia, Leyla and Gerloff, Dietlind and Gut, Ivo and Gyenesei, Attila and Habermann, Nina and Hancock, John M and Hanauer, Marc and Hovig, Eivind and Johansson, Lennart F and Keane, Thomas and Korbel, Jan and Lauer, Katharina B and Laurie, Steve and Lesko{\v s}ek, Brane and Lloyd, David and Marqu{\'e}s-Bonet, Tom{\'a}s and Mei, Hailiang and Monostory, Katalin and Pi{\~n}ero, Janet and Poterlowicz, Krzysztof and Rath, Ana and Samarakoon, Pubudu and Sanz, Ferran and Saunders, Gary and Sie, Daoud and Swertz, Morris A and Tsukanov, Kirill and Valencia, Alfonso and Vidak, Marko and Yenyxe Gonz{\'a}lez, Cristina and Ylstra, Bauke and B{\'e}roud, Christophe} } @article {612, title = {Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 06 17}, pages = {2674}, abstract = {

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{\textquoteright}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.

}, keywords = {ADAM17 Protein, Antineoplastic Combined Chemotherapy Protocols, Benchmarking, Biomarkers, Tumor, Cell Line, Tumor, Computational Biology, Datasets as Topic, Drug Antagonism, Drug Resistance, Neoplasm, Drug Synergism, Genomics, Humans, Molecular Targeted Therapy, mutation, Neoplasms, pharmacogenetics, Phosphatidylinositol 3-Kinases, Phosphoinositide-3 Kinase Inhibitors, Treatment Outcome}, issn = {2041-1723}, doi = {10.1038/s41467-019-09799-2}, author = {Menden, Michael P and Wang, Dennis and Mason, Mike J and Szalai, Bence and Bulusu, Krishna C and Guan, Yuanfang and Yu, Thomas and Kang, Jaewoo and Jeon, Minji and Wolfinger, Russ and Nguyen, Tin and Zaslavskiy, Mikhail and Jang, In Sock and Ghazoui, Zara and Ahsen, Mehmet Eren and Vogel, Robert and Neto, Elias Chaibub and Norman, Thea and Tang, Eric K Y and Garnett, Mathew J and Veroli, Giovanni Y Di and Fawell, Stephen and Stolovitzky, Gustavo and Guinney, Justin and Dry, Jonathan R and Saez-Rodriguez, Julio} } @article {428, title = {A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection}, journal = {Nature Communications}, volume = {9}, year = {2018}, month = {Jan-12-2018}, doi = {10.1038/s41467-018-06735-8}, url = {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}, author = {Fourati, Slim and Talla, Aarthi and Mahmoudian, Mehrad and Burkhart, Joshua G. and Kl{\'e}n, Riku and Henao, Ricardo and Yu, Thomas and Ayd{\i}n, Zafer and Yeung, Ka Yee and Ahsen, Mehmet Eren and Almugbel, Reem and Jahandideh, Samad and Liang, Xiao and Nordling, Torbj{\"o}rn E. M. and Shiga, Motoki and Stanescu, Ana and Vogel, Robert and Pandey, Gaurav and Chiu, Christopher and McClain, Micah T. and Woods, Christopher W. and Ginsburg, Geoffrey S. and Elo, Laura L. and Tsalik, Ephraim L. and Mangravite, Lara M. and Sieberts, Solveig K.} } @article {1132, title = {Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.}, journal = {Nature methods}, year = {2015}, month = {2015 May 18}, abstract = {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/.}, keywords = {cancer, NGS, variant calling}, issn = {1548-7105}, doi = {10.1038/nmeth.3407}, url = {http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html}, author = {Ewing, Adam D and Houlahan, Kathleen E and Hu, Yin and Ellrott, Kyle and Caloian, Cristian and Yamaguchi, Takafumi N and Bare, J Christopher and P{\textquoteright}ng, Christine and Waggott, Daryl and Sabelnykova, Veronica Y and Kellen, Michael R and Norman, Thea C and Haussler, David and Friend, Stephen H and Stolovitzky, Gustavo and Margolin, Adam A and Stuart, Joshua M and Boutros, Paul C}, editor = {ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants and Liu Xi and Ninad Dewal and Yu Fan and Wenyi Wang and David Wheeler and Andreas Wilm and Grace Hui Ting and Chenhao Li and Denis Bertrand and Niranjan Nagarajan and Qing-Rong Chen and Chih-Hao Hsu and Ying Hu and Chunhua Yan and Warren Kibbe and Daoud Meerzaman and Kristian Cibulskis and Mara Rosenberg and Louis Bergelson and Adam Kiezun and Amie Radenbaugh and Anne-Sophie Sertier and Anthony Ferrari and Laurie Tonton and Kunal Bhutani and Nancy F Hansen and Difei Wang and Lei Song and Zhongwu Lai and Liao, Yang and Shi, Wei and Carbonell-Caballero, Jos{\'e} and Joaqu{\'\i}n Dopazo and Cheryl C K Lau and Justin Guinney} } @article {1155, title = {Prediction of human population responses to toxic compounds by a collaborative competition.}, journal = {Nature biotechnology}, year = {2015}, month = {2015 Aug 10}, abstract = {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{\textquoteright}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.}, issn = {1546-1696}, doi = {10.1038/nbt.3299}, url = {http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html}, author = {Eduati, Federica and Mangravite, Lara M and Wang, Tao and Tang, Hao and Bare, J Christopher and Huang, Ruili and Norman, Thea and Kellen, Mike and Menden, Michael P and Yang, Jichen and Zhan, Xiaowei and Zhong, Rui and Xiao, Guanghua and Xia, Menghang and Abdo, Nour and Kosyk, Oksana} } @article {1087, title = {Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.}, journal = {Nature communications}, volume = {5}, year = {2014}, month = {2014}, pages = {5125}, abstract = {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 {\textquoteright}dashboard{\textquoteright} 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.}, keywords = {RNA-seq}, issn = {2041-1723}, doi = {10.1038/ncomms6125}, url = {http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html}, author = {Munro, Sarah A and Lund, Steven P and Pine, P Scott and Binder, Hans and Clevert, Djork-Arn{\'e} and Ana Conesa and Dopazo, Joaquin and Fasold, Mario and Hochreiter, Sepp and Hong, Huixiao and Jafari, Nadereh and Kreil, David P and Labaj, Pawe{\l} P and Li, Sheng and Liao, Yang and Lin, Simon M and Meehan, Joseph and Mason, Christopher E and Santoyo-L{\'o}pez, Javier and Setterquist, Robert A and Shi, Leming and Shi, Wei and Smyth, Gordon K and Stralis-Pavese, Nancy and Su, Zhenqiang and Tong, Weida and Wang, Charles and Wang, Jian and Xu, Joshua and Ye, Zhan and Yang, Yong and Yu, Ying and Salit, Marc} } @article {535, title = {Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells.}, journal = {Hum Mol Genet}, volume = {20}, year = {2011}, month = {2011 Dec 15}, pages = {4932-46}, abstract = {

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.

}, keywords = {Acute Disease, Anemia, Hemolytic, Animals, Basic Helix-Loop-Helix Transcription Factors, Cell Differentiation, Cell Line, Cell Lineage, Cluster Analysis, Embryonic Stem Cells, Erythroid Cells, Flow Cytometry, Gene Expression Profiling, Hematopoietic Stem Cells, Humans, Mice, Myeloid Cells, Paracrine Communication, Proto-Oncogene Proteins, Reverse Transcriptase Polymerase Chain Reaction, rho GTP-Binding Proteins, Signal Transduction, Stem Cell Transplantation, T-Cell Acute Lymphocytic Leukemia Protein 1, Transcriptome}, issn = {1460-2083}, doi = {10.1093/hmg/ddr431}, author = {Yung, Sun and Ledran, Maria and Moreno-Gimeno, Inmaculada and Conesa, Ana and Montaner, David and Dopazo, Joaquin and Dimmick, Ian and Slater, Nicholas J and Marenah, Lamin and Real, Pedro J and Paraskevopoulou, Iliana and Bisbal, Viviana and Burks, Deborah and Santibanez-Koref, Mauro and Moreno, Ruben and Mountford, Joanne and Menendez, Pablo and Armstrong, Lyle and Lako, Majlinda} } @article {20164864, title = {Fine-scale evolution: genomic, phenotypic and ecological differentiation in two coexisting Salinibacter ruber strains.}, journal = {The ISME journal}, year = {2010}, month = {2010 Feb 18}, abstract = {

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.

}, author = {Pe{\~n}a, Arantxa and Teeling, Hanno and Huerta-Cepas, Jaime and Santos, Fernando and Yarza, Pablo and Brito-Echeverr{\'\i}a, Jocelyn and Lucio, Marianna and Schmitt-Kopplin, Philippe and Meseguer, Inmaculada and Schenowitz, Chantal and Dossat, Carole and Barbe, Valerie and Joaqu{\'\i}n Dopazo and Rossell{\'o}-Mora, Ramon and Sch{\"u}ler, Margarete and Gl{\"o}ckner, Frank Oliver and Amann, Rudolf and Gabald{\'o}n, Toni and Ant{\'o}n, Josefa} } @article {20676074, title = {The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.}, journal = {Nature biotechnology}, volume = {28}, year = {2010}, month = {2010 Aug}, pages = {827-38}, abstract = {

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.

}, url = {http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html}, author = {Shi, Leming and Campbell, Gregory and Jones, Wendell D and Campagne, Fabien and Wen, Zhining and Walker, Stephen J and Su, Zhenqiang and Chu, Tzu-Ming and Goodsaid, Federico M and Pusztai, Lajos and Shaughnessy, John D and Oberthuer, Andr{\'e} and Thomas, Russell S and Paules, Richard S and Fielden, Mark and Barlogie, Bart and Chen, Weijie and Du, Pan and Fischer, Matthias and Furlanello, Cesare and Gallas, Brandon D and Ge, Xijin and Megherbi, Dalila B and Symmans, W Fraser and Wang, May D and Zhang, John and Bitter, Hans and Brors, Benedikt and Bushel, Pierre R and Bylesjo, Max and Chen, Minjun and Cheng, Jie and Cheng, Jing and Chou, Jeff and Davison, Timothy S and Delorenzi, Mauro and Deng, Youping and Devanarayan, Viswanath and Dix, David J and Dopazo, Joaquin and Dorff, Kevin C and Elloumi, Fathi and Fan, Jianqing and Fan, Shicai and Fan, Xiaohui and Fang, Hong and Gonzaludo, Nina and Hess, Kenneth R and Hong, Huixiao and Huan, Jun and Irizarry, Rafael A and Judson, Richard and Juraeva, Dilafruz and Lababidi, Samir and Lambert, Christophe G and Li, Li and Li, Yanen and Li, Zhen and Lin, Simon M and Liu, Guozhen and Lobenhofer, Edward K and Luo, Jun and Luo, Wen and McCall, Matthew N and Nikolsky, Yuri and Pennello, Gene A and Perkins, Roger G and Philip, Reena and Popovici, Vlad and Price, Nathan D and Qian, Feng and Scherer, Andreas and Shi, Tieliu and Shi, Weiwei and Sung, Jaeyun and Thierry-Mieg, Danielle and Thierry-Mieg, Jean and Thodima, Venkata and Trygg, Johan and Vishnuvajjala, Lakshmi and Wang, Sue Jane and Wu, Jianping and Wu, Yichao and Xie, Qian and Yousef, Waleed A and Zhang, Liang and Zhang, Xuegong and Zhong, Sheng and Zhou, Yiming and Zhu, Sheng and Arasappan, Dhivya and Bao, Wenjun and Lucas, Anne Bergstrom and Berthold, Frank and Brennan, Richard J and Buness, Andreas and Catalano, Jennifer G and Chang, Chang and Chen, Rong and Cheng, Yiyu and Cui, Jian and Czika, Wendy and Demichelis, Francesca and Deng, Xutao and Dosymbekov, Damir and Eils, Roland and Feng, Yang and Fostel, Jennifer and Fulmer-Smentek, Stephanie and Fuscoe, James C and Gatto, Laurent and Ge, Weigong and Goldstein, Darlene R and Guo, Li and Halbert, Donald N and Han, Jing and Harris, Stephen C and Hatzis, Christos and Herman, Damir and Huang, Jianping and Jensen, Roderick V and Jiang, Rui and Johnson, Charles D and Jurman, Giuseppe and Kahlert, Yvonne and Khuder, Sadik A and Kohl, Matthias and Li, Jianying and Li, Li and Li, Menglong and Li, Quan-Zhen and Li, Shao and Li, Zhiguang and Liu, Jie and Liu, Ying and Liu, Zhichao and Meng, Lu and Madera, Manuel and Martinez-Murillo, Francisco and Medina, Ignacio and Meehan, Joseph and Miclaus, Kelci and Moffitt, Richard A and Montaner, David and Mukherjee, Piali and Mulligan, George J and Neville, Padraic and Nikolskaya, Tatiana and Ning, Baitang and Page, Grier P and Parker, Joel and Parry, R Mitchell and Peng, Xuejun and Peterson, Ron L and Phan, John H and Quanz, Brian and Ren, Yi and Riccadonna, Samantha and Roter, Alan H and Samuelson, Frank W and Schumacher, Martin M and Shambaugh, Joseph D and Shi, Qiang and Shippy, Richard and Si, Shengzhu and Smalter, Aaron and Sotiriou, Christos and Soukup, Mat and Staedtler, Frank and Steiner, Guido and Stokes, Todd H and Sun, Qinglan and Tan, Pei-Yi and Tang, Rong and Tezak, Zivana and Thorn, Brett and Tsyganova, Marina and Turpaz, Yaron and Vega, Silvia C and Visintainer, Roberto and von Frese, Juergen and Wang, Charles and Wang, Eric and Wang, Junwei and Wang, Wei and Westermann, Frank and Willey, James C and Woods, Matthew and Wu, Shujian and Xiao, Nianqing and Xu, Joshua and Xu, Lei and Yang, Lun and Zeng, Xiao and Zhang, Jialu and Zhang, Li and Zhang, Min and Zhao, Chen and Puri, Raj K and Scherf, Uwe and Tong, Weida and Wolfinger, Russell D} } @article {15980548, title = {GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data}, journal = {Nucleic Acids Res}, volume = {33}, year = {2005}, note = {

Vaquerizas, Juan M Conde, Lucia Yankilevich, Patricio Cabezon, Amaya Minguez, Pablo Diaz-Uriarte, Ramon Al-Shahrour, Fatima Herrero, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W616-20.

}, pages = {W616-20}, abstract = {

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.

}, keywords = {gepas, microarray data analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15980548}, author = {Vaquerizas, J. M. and L. Conde and Yankilevich, P. and Cabezon, A. and Minguez, P. and Diaz-Uriarte, R. and Fatima Al-Shahrour and Herrero, J. and Dopazo, J.} } @article {12576477, title = {Examining the role of glutamic acid 183 in chloroperoxidase catalysis}, journal = {J Biol Chem}, volume = {278}, number = {16}, year = {2003}, note = {Yi, Xianwen Conesa, Ana Punt, Peter J Hager, Lowell P GM 07768/GM/NIGMS NIH HHS/United States Research Support, U.S. Gov{\textquoteright}t, P.H.S. United States The Journal of biological chemistry J Biol Chem. 2003 Apr 18;278(16):13855-9. Epub 2003 Feb 7.}, pages = {13855-9}, abstract = {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.}, keywords = {Aspergillus niger/metabolism Catalase/metabolism Catalysis Chloride Peroxidase/*chemistry/*metabolism Chlorine/metabolism Chromatography, Ion Exchange Circular Dichroism Crystallography, Polyacrylamide Gel Fungi/enzymology Glutamic Acid/*chemistry Histidine/chemistry/metabolism Hydrogen-Ion Concentration Immunoblotting Isoelectric Focusing Mutation Oxidoreductases/metabolism Plasmids/metabolism, X-Ray Electrophoresis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12576477}, author = {Yi, X. and A. Conesa and Punt, P. J. and Hager, L. P.} } @article {12824331, title = {Tools for comparative protein structure modeling and analysis}, journal = {Nucleic Acids Res}, volume = {31}, number = {13}, year = {2003}, note = {Eswar, Narayanan John, Bino Mirkovic, Nebojsa Fiser, Andras Ilyin, Valentin A Pieper, Ursula Stuart, Ashley C Marti-Renom, Marc A Madhusudhan, M S Yerkovich, Bozidar Sali, Andrej P50 GM62529/GM/NIGMS NIH HHS/United States R01 GM 54762/GM/NIGMS NIH HHS/United States R33 CA84699/CA/NCI NIH HHS/United States Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, P.H.S. England Nucleic acids research Nucleic Acids Res. 2003 Jul 1;31(13):3375-80.}, pages = {3375-80}, abstract = {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.}, keywords = {Amino Acid *Software *Structural Homology, Internet Models, Molecular Protein Folding Proteins/chemistry Reproducibility of Results Sequence Alignment Sequence Homology, Protein Systems Integration}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12824331}, author = {Eswar, N. and John, B. and Mirkovic, N. and Fiser, A. and Ilyin, V. A. and Pieper, U. and Stuart, A. C. and M. A. Marti-Renom and Madhusudhan, M. S. and Yerkovich, B. and Sali, A.} }