@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 {768, title = {Editorial: Critical assessment of massive data analysis (CAMDA) annual conference 2021.}, journal = {Front Genet}, volume = {14}, year = {2023}, month = {2023}, pages = {1154398}, issn = {1664-8021}, doi = {10.3389/fgene.2023.1154398}, author = {{\L}abaj, Pawe{\l} P and Dopazo, Joaquin and Xiao, Wenzhong and Kreil, David P} } @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 {741, title = {Mutational Characterization of Cutaneous Melanoma Supports Divergent Pathways Model for Melanoma Development.}, journal = {Cancers (Basel)}, volume = {13}, year = {2021}, month = {2021 Oct 18}, abstract = {

According to the divergent pathway model, cutaneous melanoma comprises a nevogenic group with a propensity to melanocyte proliferation and another one associated with cumulative solar damage (CSD). While characterized clinically and epidemiologically, the differences in the molecular profiles between the groups have remained primarily uninvestigated. This study has used a custom gene panel and bioinformatics tools to investigate the potential molecular differences in a thoroughly characterized cohort of 119 melanoma patients belonging to nevogenic and CSD groups. We found that the nevogenic melanomas had a restricted set of mutations, with the prominently mutated gene being . The CSD melanomas, in contrast, showed mutations in a diverse group of genes that included , , , and . We thus provide evidence that nevogenic and CSD melanomas constitute different biological entities and highlight the need to explore new targeted therapies.

}, issn = {2072-6694}, doi = {10.3390/cancers13205219}, author = {Mill{\'a}n-Esteban, David and Pe{\~n}a-Chilet, Maria and Garc{\'\i}a-Casado, Zaida and Manrique-Silva, Esperanza and Requena, Celia and Ba{\~n}uls, Jos{\'e} and Lopez-Guerrero, Jose Antonio and Rodr{\'\i}guez-Hern{\'a}ndez, Aranzazu and Traves, V{\'\i}ctor and Dopazo, Joaquin and Vir{\'o}s, Amaya and Kumar, Rajiv and Nagore, Eduardo} } @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 {722, title = {Schuurs{\textendash}Hoeijmakers Syndrome (PACS1 Neurodevelopmental Disorder): Seven Novel Patients and a Review}, journal = {Genes}, volume = {12}, year = {2021}, month = {Jan-05-2021}, pages = {738}, doi = {10.3390/genes12050738}, url = {https://www.mdpi.com/2073-4425/12/5/738https://www.mdpi.com/2073-4425/12/5/738/pdf}, author = {Tenorio-Casta{\~n}o, Jair and Morte, Beatriz and Nevado, Juli{\'a}n and Mart{\'\i}nez-Glez, V{\'\i}ctor and Santos-Simarro, Fernando and Garc{\'\i}a-Mi{\~n}aur, Sixto and Palomares-Bralo, Mar{\'\i}a and Pacio-M{\'\i}guez, Marta and G{\'o}mez, Beatriz and Arias, Pedro and Alcochea, Alba and Carri{\'o}n, Juan and Arias, Patricia and Almoguera, Berta and L{\'o}pez-Grondona, Fermina and Lorda-Sanchez, Isabel and Gal{\'a}n-G{\'o}mez, Enrique and Valenzuela, Irene and M{\'e}ndez Perez, Mar{\'\i}a and Cusc{\'o}, Iv{\'o}n and Barros, Francisco and Pi{\'e}, Juan and Ramos, Sergio and Ramos, Feliciano and Kuechler, Alma and Tizzano, Eduardo and Ayuso, Carmen and Kaiser, Frank and P{\'e}rez-Jurado, Luis and Carracedo, {\'A}ngel and Lapunzina, Pablo} } @article {730, title = {10th Anniversary of the European Association for Predictive, Preventive and Personalised (3P) Medicine~- EPMA World Congress Supplement 2020.}, journal = {EPMA J}, year = {2020}, month = {2020 Aug 19}, pages = {1-133}, abstract = {

In 2019, the EPMA celebrated its 10th anniversary at the 5th World Congress in Pilsen, Czech Republic. The history of the International Professional Network dedicated to Predictive, Preventive and Personalised Medicine~(PPPM / 3PM) is rich in achievements.~Facing the coronavirus COVID-19 pandemic~it is getting evident globally that the predictive approach, targeted prevention and personalisation of medical services is the optimal paradigm in healthcare demonstrating the high potential to save lives and to benefit the society as a whole. The EPMA World Congress Supplement 2020 highlights advances in 3P medicine.

}, issn = {1878-5077}, doi = {10.1007/s13167-020-00206-1}, author = {Golubnitschaja, Olga and Topolcan, Ondrej and Kucera, Radek and Costigliola, Vincenzo} } @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 {689, title = {COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.}, journal = {Sci Data}, volume = {7}, year = {2020}, month = {2020 05 05}, pages = {136}, keywords = {Betacoronavirus, Computational Biology, Coronavirus Infections, COVID-19, Databases, Factual, Host Microbial Interactions, Host-Pathogen Interactions, Humans, International Cooperation, Models, Biological, Pandemics, Pneumonia, Viral, SARS-CoV-2}, issn = {2052-4463}, doi = {10.1038/s41597-020-0477-8}, author = {Ostaszewski, Marek and Mazein, Alexander and Gillespie, Marc E and Kuperstein, Inna and Niarakis, Anna and Hermjakob, Henning and Pico, Alexander R and Willighagen, Egon L and Evelo, Chris T and Hasenauer, Jan and Schreiber, Falk and Dr{\"a}ger, Andreas and Demir, Emek and Wolkenhauer, Olaf and Furlong, Laura I and Barillot, Emmanuel and Dopazo, Joaquin and Orta-Resendiz, Aurelio and Messina, Francesco and Valencia, Alfonso and Funahashi, Akira and Kitano, Hiroaki and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @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 {653, title = {Pazopanib for treatment of typical solitary fibrous tumours: a multicentre, single-arm, phase 2 trial.}, journal = {Lancet Oncol}, volume = {21}, year = {2020}, month = {2020 03}, pages = {456-466}, abstract = {

BACKGROUND: Solitary fibrous tumour is an ultra-rare sarcoma, which encompasses different clinicopathological subgroups. The dedifferentiated subgroup shows an aggressive course with resistance to pazopanib, whereas in the malignant subgroup, pazopanib shows higher activity than in previous studies with chemotherapy. We designed a trial to test pazopanib activity in two different cohorts of solitary fibrous tumour: the malignant-dedifferentiated cohort, which was previously published, and the typical cohort, which is presented here.

METHODS: In this single-arm, phase 2 trial, adult patients (aged >=18 years) diagnosed with confirmed metastatic or unresectable typical solitary fibrous tumour of any location, who had progressed in the previous 6 months (by Choi criteria or Response Evaluation Criteria in Solid Tumors [RECIST]) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 were enrolled at 11 tertiary hospitals in Italy, France, and Spain. Patients received pazopanib 800 mg once daily, taken orally, until progression, unacceptable toxicity, withdrawal of consent, non-compliance, or a delay in pazopanib administration of longer than 3 weeks. The primary endpoint was proportion of patients achieving an overall response measured by Choi criteria in patients who received at least 1 month of treatment with at least one radiological assessment. All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered in ClinicalTrials.gov, NCT02066285, and with the European Clinical Trials Database, EudraCT 2013-005456-15.

FINDINGS: From June 26, 2014, to Dec 13, 2018, of 40 patients who were assessed, 34 patients were enrolled and 31 patients were included in the response analysis. Median follow-up was 18 months (IQR 14-34), and 18 (58\%) of 31 patients had a partial response, 12 (39\%) had stable disease, and one (3\%) showed progressive disease according to Choi criteria and central review. The proportion of overall response based on Choi criteria was 58\% (95\% CI 34-69). There were no deaths caused by toxicity, and the most frequent adverse events were diarrhoea (18 [53\%] of 34 patients), fatigue (17 [50\%]), and hypertension (17 [50\%]).

INTERPRETATION: To our knowledge, this is the first prospective trial of pazopanib for advanced typical solitary fibrous tumour. The manageable toxicity and activity shown by pazopanib in this cohort suggest that this drug could be considered as first-line treatment for advanced typical solitary fibrous tumour.

FUNDING: Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.

}, keywords = {Aged, Female, Follow-Up Studies, Humans, Indazoles, Male, Middle Aged, Neoplasm Metastasis, Prognosis, Prospective Studies, Protein Kinase Inhibitors, Pyrimidines, Response Evaluation Criteria in Solid Tumors, Solitary Fibrous Tumors, Sulfonamides, Survival Rate}, issn = {1474-5488}, doi = {10.1016/S1470-2045(19)30826-5}, author = {Martin-Broto, Javier and Cruz, Josefina and Penel, Nicolas and Le Cesne, Axel and Hindi, Nadia and Luna, Pablo and Moura, David S and Bernabeu, Daniel and de Alava, Enrique and Lopez-Guerrero, Jose Antonio and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria and Gutierrez, Antonio and Collini, Paola and Karanian, Marie and Redondo, Andres and Lopez-Pousa, Antonio and Grignani, Giovanni and Diaz-Martin, Juan and Marcilla, David and Fernandez-Serra, Antonio and Gonzalez-Aguilera, Cristina and Casali, Paolo G and Blay, Jean-Yves and Stacchiotti, Silvia} } @article {704, title = {Transparency and reproducibility in artificial intelligence.}, journal = {Nature}, volume = {586}, year = {2020}, month = {2020 10}, pages = {E14-E16}, keywords = {Algorithms, Artificial Intelligence, Reproducibility of Results}, issn = {1476-4687}, doi = {10.1038/s41586-020-2766-y}, author = {Haibe-Kains, Benjamin and Adam, George Alexandru and Hosny, Ahmed and Khodakarami, Farnoosh and Waldron, Levi and Wang, Bo and McIntosh, Chris and Goldenberg, Anna and Kundaje, Anshul and Greene, Casey S and Broderick, Tamara and Hoffman, Michael M and Leek, Jeffrey T and Korthauer, Keegan and Huber, Wolfgang and Brazma, Alvis and Pineau, Joelle and Tibshirani, Robert and Hastie, Trevor and Ioannidis, John P A and Quackenbush, John and Aerts, Hugo J W L} } @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 {387, title = {HGVA: the Human Genome Variation Archive.}, journal = {Nucleic Acids Res}, volume = {45}, year = {2017}, month = {2017 07 03}, pages = {W189-W194}, abstract = {

High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK{\textquoteright}s 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.

}, keywords = {Genetic Variation, Genome, Human, Humans, Internet, Software, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkx445}, url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx445}, author = {Lopez, Javier and Coll, Jacobo and Haimel, Matthias and Kandasamy, Swaathi and T{\'a}rraga, Joaqu{\'\i}n and Furio-Tari, Pedro and Bari, Wasim and Bleda, Marta and Rueda, Antonio and Gr{\"a}f, Stefan and Rendon, Augusto and Dopazo, Joaquin and Medina, Ignacio} } @article {451, title = {Stress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis.}, journal = {Mol Metab}, volume = {5}, year = {2016}, month = {2016 Jan}, pages = {19-33}, abstract = {

BACKGROUND: Stress-associated conditions such as psychoemotional reactivity and depression have been paradoxically linked to either weight gain or weight loss. This bi-directional effect of stress is not understood at the functional level. Here we tested the hypothesis that pre-stress level of adaptive thermogenesis and brown adipose tissue (BAT) functions explain the vulnerability or resilience to stress-induced obesity.

METHODS: We used wt and triple β1,β2,β3-Adrenergic Receptors knockout (β-less) mice exposed to a model of chronic subordination stress (CSS) at either room temperature (22~{\textdegree}C) or murine thermoneutrality (30~{\textdegree}C). A combined behavioral, physiological, molecular, and immunohistochemical analysis was conducted to determine stress-induced modulation of energy balance and BAT structure and function. Immortalized brown adipocytes were used for in~vitro assays.

RESULTS: Departing from our initial observation that βARs are dispensable for cold-induced BAT browning, we demonstrated that under physiological conditions promoting low adaptive thermogenesis and BAT activity (e.g. thermoneutrality or genetic deletion of the βARs), exposure to CSS acted as a stimulus for BAT activation and thermogenesis, resulting in resistance to diet-induced obesity despite the presence of hyperphagia. Conversely, in wt mice acclimatized to room temperature, and therefore characterized by sustained BAT function, exposure to CSS increased vulnerability to obesity. Exposure to CSS enhanced the sympathetic innervation of BAT in wt acclimatized to thermoneutrality and in β-less mice. Despite increased sympathetic innervation suggesting adrenergic-mediated browning, norepinephrine did not promote browning in βARs knockout brown adipocytes, which led us to identify an alternative sympathetic/brown adipocytes purinergic pathway in the BAT. This pathway is downregulated under conditions of low adaptive thermogenesis requirements, is induced by stress, and elicits activation of UCP1 in wt and β-less brown adipocytes. Importantly, this purinergic pathway is conserved in human BAT.

CONCLUSION: Our findings demonstrate that thermogenesis and BAT function are determinant of the resilience or vulnerability to stress-induced obesity. Our data support a model in which adrenergic and purinergic pathways exert complementary/synergistic functions in BAT, thus suggesting an alternative to βARs agonists for the activation of human BAT.

}, issn = {2212-8778}, doi = {10.1016/j.molmet.2015.10.005}, author = {Razzoli, Maria and Frontini, Andrea and Gurney, Allison and Mondini, Eleonora and Cubuk, Cankut and Katz, Liora S and Cero, Cheryl and Bolan, Patrick J and Dopazo, Joaquin and Vidal-Puig, Antonio and Cinti, Saverio and Bartolomucci, Alessandro} } @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 {513, title = {Diversification of the expanded teleost-specific toll-like receptor family in Atlantic cod, Gadus morhua.}, journal = {BMC Evol Biol}, volume = {12}, year = {2012}, month = {2012 Dec 29}, pages = {256}, abstract = {

BACKGROUND: Toll-like receptors (Tlrs) are major molecular pattern recognition receptors of the innate immune system. Atlantic cod (Gadus morhua) is the first vertebrate known to have lost most of the mammalian Tlr orthologues, particularly all bacterial recognising and other cell surface Tlrs. On the other hand, its genome encodes a unique repertoire of teleost-specific Tlrs. The aim of this study was to investigate if these duplicate Tlrs have been retained through adaptive evolution to compensate for the lack of other cell surface Tlrs in the cod genome.

RESULTS: In this study, one tlr21, 12 tlr22 and two tlr23 genes representing the teleost-specific Tlr family have been cloned and characterised in cod. Phylogenetic analysis grouped all tlr22 genes under a single clade, indicating that the multiple cod paralogues have arisen through lineage-specific duplications. All tlrs examined were transcribed in immune-related tissues as well as in stomach, gut and gonads of adult cod and were differentially expressed during early development. These tlrs were also differentially regulated following immune challenge by immersion with Vibrio anguillarum, indicating their role in the immune response. An increase in water temperature from 4 to 12{\textdegree}C was associated with a 5.5-fold down-regulation of tlr22d transcript levels in spleen. Maximum likelihood analysis with different evolution models revealed that tlr22 genes are under positive selection. A total of 24 codons were found to be positively selected, of which 19 are in the ligand binding region of ectodomain.

CONCLUSION: Positive selection pressure coupled with experimental evidence of differential expression strongly support the hypothesis that teleost-specific tlr paralogues in cod are undergoing neofunctionalisation and can recognise bacterial pathogen-associated molecular patterns to compensate for the lack of other cell surface Tlrs.

}, keywords = {Amino Acid Sequence, Animals, Binding Sites, Evolution, Molecular, Fish Diseases, Fish Proteins, Gadus morhua, Gene Expression Profiling, Genetic Variation, Gills, Head Kidney, Host-Pathogen Interactions, Models, Molecular, Molecular Sequence Data, Multigene Family, Phylogeny, Protein Structure, Tertiary, Reverse Transcriptase Polymerase Chain Reaction, Selection, Genetic, Sequence Analysis, DNA, Sequence Homology, Amino Acid, Temperature, Toll-Like Receptors, Vibrio}, issn = {1471-2148}, doi = {10.1186/1471-2148-12-256}, author = {Sundaram, Arvind Y M and Kiron, Viswanath and Dopazo, Joaquin and Fernandes, Jorge M O} } @article {514, title = {Extensive translatome remodeling during ER stress response in mammalian cells.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e35915}, abstract = {

In this work we have described the translatome of two mammalian cell lines, NIH3T3 and Jurkat, by scoring the relative polysome association of \~{}10,000 mRNA under normal and ER stress conditions. We have found that translation efficiencies of mRNA correlated poorly with transcript abundance, although a general tendency was observed so that the highest translation efficiencies were found in abundant mRNA. Despite the differences found between mouse (NIH3T3) and human (Jurkat) cells, both cell types share a common translatome composed by \~{}800-900 mRNA that encode proteins involved in basic cellular functions. Upon stress, an extensive remodeling in translatomes was observed so that translation of \~{}50\% of mRNA was inhibited in both cell types, this effect being more dramatic for those mRNA that accounted for most of the cell translation. Interestingly, we found two subsets comprising 1000-1500 mRNA whose translation resisted or was induced by stress. Translation arrest resistant class includes many mRNA encoding aminoacyl tRNA synthetases, ATPases and enzymes involved in DNA replication and stress response such as BiP. This class of mRNA is characterized by high translation rates in both control and stress conditions. Translation inducible class includes mRNA whose translation was relieved after stress, showing a high enrichment in early response transcription factors of bZIP and zinc finger C2H2 classes. Unlike yeast, a general coordination between changes in translation and transcription upon stress (potentiation) was not observed in mammalian cells. Among the different features of mRNA analyzed, we found a relevant association of translation efficiency with the presence of upstream ATG in the 5{\textquoteright}UTR and with the length of coding sequence of mRNA, and a looser association with other parameters such as the length and the G+C content of 5{\textquoteright}UTR. A model for translatome remodeling during the acute phase of stress response in mammalian cells is proposed.

}, keywords = {Animals, Endoplasmic Reticulum Stress, Humans, Jurkat Cells, Mice, NIH 3T3 Cells, Oligonucleotide Array Sequence Analysis, Protein Biosynthesis, RNA, Messenger, Transcription, Genetic}, issn = {1932-6203}, doi = {10.1371/journal.pone.0035915}, author = {Ventoso, Iv{\'a}n and Kochetov, Alex and Montaner, David and Dopazo, Joaquin and Santoyo, Javier} } @article {521, title = {The protease MT1-MMP drives a combinatorial proteolytic program in activated endothelial cells.}, journal = {FASEB J}, volume = {26}, year = {2012}, month = {2012 Nov}, pages = {4481-94}, abstract = {

The mechanism by which proteolytic events translate into biological responses is not well understood. To explore the link of pericellular proteolysis to events relevant to capillary sprouting within the inflammatory context, we aimed at the identification of the collection of substrates of the protease MT1-MMP in endothelial tip cells induced by inflammatory stimuli. We applied quantitative proteomics to endothelial cells (ECs) derived from wild-type and MT1-MMP-null mice to identify the substrate repertoire of this protease in TNF-α-activated ECs. Bioinformatics analysis revealed a combinatorial MT1-MMP proteolytic program, in which combined rather than single substrate processing would determine biological decisions by activated ECs, including chemotaxis, cell motility and adhesion, and vasculature development. MT1-MMP-deficient ECs inefficiently processed several of these substrates (TSP1, CYR61, NID1, and SEM3C), validating the model. This novel concept of MT1-MMP-driven combinatorial proteolysis in angiogenesis might be extendable to proteolytic actions in other cellular contexts.

}, keywords = {Animals, Blotting, Western, Combinatorial Chemistry Techniques, Computational Biology, Endothelial Cells, Gene Expression Regulation, Enzymologic, Inflammation, Matrix Metalloproteinase 14, Mice, Protein Array Analysis, Reverse Transcriptase Polymerase Chain Reaction, RNA Interference, RNA, Small Interfering, Transcriptome, Tumor Necrosis Factor-alpha}, issn = {1530-6860}, doi = {10.1096/fj.12-205906}, author = {Koziol, Agnieszka and Gonzalo, Pilar and Mota, Alba and Poll{\'a}n, Angela and Lorenzo, Cristina and Colom{\'e}, Nuria and Montaner, David and Dopazo, Joaquin and Arribas, Joaqu{\'\i}n and Canals, Francesc and Arroyo, Alicia G} } @article {879, title = {Fortunella margarita Transcriptional Reprogramming Triggered by Xanthomonas citri subsp. citri.}, journal = {BMC plant biology}, volume = {11}, year = {2011}, month = {2011}, pages = {159}, abstract = {ABSTRACT:}, issn = {1471-2229}, author = {Khalaf, Abeer A and Gmitter, Frederick G and Ana Conesa and Dopazo, Joaquin and Moore, Gloria A} } @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 {581, title = {Functional signatures identified in B-cell non-Hodgkin lymphoma profiles.}, journal = {Leuk Lymphoma}, volume = {50}, year = {2009}, month = {2009 Oct}, pages = {1699-708}, abstract = {

Gene-expression profiling in B-cell lymphomas has provided crucial data on specific lymphoma types, which can contribute to the identification of essential lymphoma survival genes and pathways. In this study, the gene-expression profiling data of all major B-cell lymphoma types were analyzed by unsupervised clustering. The transcriptome classification so obtained, was explored using gene set enrichment analysis generating a heatmap for B-cell lymphoma that identifies common lymphoma survival mechanisms and potential therapeutic targets, recognizing sets of coregulated genes and functional pathways expressed in different lymphoma types. Some of the most relevant signatures (stroma, cell cycle, B-cell receptor (BCR)) are shared by multiple lymphoma types or subclasses. A specific attention was paid to the analysis of BCR and coregulated pathways, defining molecular heterogeneity within multiple B-cell lymphoma types.

}, keywords = {Adult, Cluster Analysis, Gene Expression Profiling, Gene Expression Regulation, Leukemic, Genetic Heterogeneity, Humans, Lymphoma, B-Cell, Neoplasm Proteins, Oligonucleotide Array Sequence Analysis, RNA, Messenger, RNA, Neoplasm, Transcription, Genetic}, issn = {1029-2403}, doi = {10.1080/10428190903189035}, author = {Aggarwal, Mohit and S{\'a}nchez-Beato, Margarita and G{\'o}mez-L{\'o}pez, Gonzalo and Al-Shahrour, F{\'a}tima and Mart{\'\i}nez, Nerea and Rodr{\'\i}guez, Antonia and Ruiz-Ballesteros, Elena and Camacho, Francisca I and P{\'e}rez-Rosado, Alberto and de la Cueva, Paloma and Artiga, Mar{\'\i}a J and Pisano, David G and Kimby, Eva and Dopazo, Joaquin and Villuendas, Raquel and Piris, Miguel A} } @article {18948282, title = {MODBASE, a database of annotated comparative protein structure models and associated resources}, journal = {Nucleic Acids Res}, volume = {37}, number = {Database issue}, year = {2009}, note = {Pieper, Ursula Eswar, Narayanan Webb, Ben M Eramian, David Kelly, Libusha Barkan, David T Carter, Hannah Mankoo, Parminder Karchin, Rachel Marti-Renom, Marc A Davis, Fred P Sali, Andrej GM08284/GM/NIGMS NIH HHS/United States P01 GM71790/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States U01 GM61390/GM/NIGMS NIH HHS/United States U54 GM074929/GM/NIGMS NIH HHS/United States U54 GM074945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, Non-P.H.S. England Nucleic acids research Nucleic Acids Res. 2009 Jan;37(Database issue):D347-54. Epub 2008 Oct 23.}, pages = {D347-54}, abstract = {MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).}, keywords = {*Databases, Molecular Mutation Polymorphism, Protein Genomics Humans Ligands *Models, Protein User-Computer Interface, Single Nucleotide Protein Folding Protein Interaction Domains and Motifs *Protein Structure, Tertiary Proteins/genetics *Structural Homology}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18948282}, author = {Pieper, U. and Eswar, N. and Webb, B. M. and Eramian, D. and Kelly, L. and Barkan, D. T. and Carter, H. and Mankoo, P. and Karchin, R. and M. A. Marti-Renom and Davis, F. P. and Sali, A.} } @article {722, title = {Statistical methods for analysis of high-throughput RNA interference screens}, journal = {Nature Methods}, volume = {6}, year = {2009}, note = {

10.1038/nmeth.1351

}, month = {2009/08//print}, pages = {569 - 575}, publisher = {Nature Publishing Group}, keywords = {gene silencing, regulation, siRNA}, isbn = {1548-7091}, url = {http://dx.doi.org/10.1038/nmeth.1351}, author = {Birmingham, Amanda and Selfors, Laura M and Forster, Thorsten and Wrobel, David and Kennedy, Caleb J and Shanks, Emma and Santoyo-L{\'o}pez, Javier and Dunican, Dara J and Long, Aideen and Kelleher, Dermot and Smith, Queta and Beijersbergen, Roderick L and Ghazal, Peter and Shamu, Caroline E} } @article {18652888, title = {Direct functional assessment of the composite phenotype through multivariate projection strategies}, journal = {Genomics}, volume = {92}, number = {6}, year = {2008}, note = {

Conesa, Ana Bro, Rasmus Garcia-Garcia, Francisco Prats, Jose Manuel Gotz, Stefan Kjeldahl, Karin Montaner, David Dopazo, Joaquin Evaluation Studies Research Support, Non-U.S. Gov{\textquoteright}t United States Genomics Genomics. 2008 Dec;92(6):373-83. Epub 2008 Sep 13.

}, pages = {373-83}, abstract = {

We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.

}, keywords = {Breast Neoplasms/genetics Computational Biology/*methods Databases, Genetic Female Gene Expression Profiling/*statistics \& numerical data Humans Mathematical Computing Multivariate Analysis Phenotype}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18652888}, author = {A. Conesa and Bro, R. and Garcia-Garcia, F. and Prats, J. M. and Gotz, S. and Kjeldahl, K. and Montaner, D. and Dopazo, J.} } @article {590, title = {Direct functional assessment of the composite phenotype through multivariate projection strategies.}, journal = {Genomics}, volume = {92}, year = {2008}, month = {2008 Dec}, pages = {373-83}, abstract = {

We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.

}, keywords = {Breast Neoplasms, Computational Biology, Databases, Genetic, Female, Gene Expression Profiling, Humans, Mathematical Computing, Multivariate Analysis, Phenotype}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2008.05.015}, author = {Conesa, Ana and Bro, Rasmus and Garcia-Garcia, Francisco and Prats, Jos{\'e} Manuel and G{\"o}tz, Stefan and Kjeldahl, Karin and Montaner, David and Dopazo, Joaquin} } @article {595, title = {Interoperability with Moby 1.0--it{\textquoteright}s better than sharing your toothbrush!}, journal = {Brief Bioinform}, volume = {9}, year = {2008}, month = {2008 May}, pages = {220-31}, abstract = {

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

}, keywords = {Computational Biology, Database Management Systems, Databases, Factual, Information Storage and Retrieval, Internet, Programming Languages, Systems Integration}, issn = {1477-4054}, doi = {10.1093/bib/bbn003}, author = {Wilkinson, Mark D and Senger, Martin and Kawas, Edward and Bruskiewich, Richard and Gouzy, Jerome and Noirot, Celine and Bardou, Philippe and Ng, Ambrose and Haase, Dirk and Saiz, Enrique de Andres and Wang, Dennis and Gibbons, Frank and Gordon, Paul M K and Sensen, Christoph W and Carrasco, Jose Manuel Rodriguez and Fern{\'a}ndez, Jos{\'e} M and Shen, Lixin and Links, Matthew and Ng, Michael and Opushneva, Nina and Neerincx, Pieter B T and Leunissen, Jack A M and Ernst, Rebecca and Twigger, Simon and Usadel, Bjorn and Good, Benjamin and Wong, Yan and Stein, Lincoln and Crosby, William and Karlsson, Johan and Royo, Romina and P{\'a}rraga, Iv{\'a}n and Ram{\'\i}rez, Sergio and Gelpi, Josep Lluis and Trelles, Oswaldo and Pisano, David G and Jimenez, Natalia and Kerhornou, Arnaud and Rosset, Roman and Zamacola, Leire and T{\'a}rraga, Joaqu{\'\i}n and Huerta-Cepas, Jaime and Carazo, Jose Mar{\'\i}a and Dopazo, Joaquin and Guig{\'o}, Roderic and Navarro, Arcadi and Orozco, Modesto and Valencia, Alfonso and Claros, M Gonzalo and P{\'e}rez, Antonio J and Aldana, Jose and Rojano, M Mar and Fernandez-Santa Cruz, Raul and Navas, Ismael and Schiltz, Gary and Farmer, Andrew and Gessler, Damian and Schoof, Heiko and Groscurth, Andreas} } @article {18238804, title = {Interoperability with Moby 1.0{\textendash}it{\textquoteright}s better than sharing your toothbrush!}, journal = {Brief Bioinform}, volume = {9}, number = {3}, year = {2008}, note = {

BioMoby Consortium Wilkinson, Mark D Senger, Martin Kawas, Edward Bruskiewich, Richard Gouzy, Jerome Noirot, Celine Bardou, Philippe Ng, Ambrose Haase, Dirk Saiz, Enrique de Andres Wang, Dennis Gibbons, Frank Gordon, Paul M K Sensen, Christoph W Carrasco, Jose Manuel Rodriguez Fernandez, Jose M Shen, Lixin Links, Matthew Ng, Michael Opushneva, Nina Neerincx, Pieter B T Leunissen, Jack A M Ernst, Rebecca Twigger, Simon Usadel, Bjorn Good, Benjamin Wong, Yan Stein, Lincoln Crosby, William Karlsson, Johan Royo, Romina Parraga, Ivan Ramirez, Sergio Gelpi, Josep Lluis Trelles, Oswaldo Pisano, David G Jimenez, Natalia Kerhornou, Arnaud Rosset, Roman Zamacola, Leire Tarraga, Joaquin Huerta-Cepas, Jaime Carazo, Jose Maria Dopazo, Joaquin Guigo, Roderic Navarro, Arcadi Orozco, Modesto Valencia, Alfonso Claros, M Gonzalo Perez, Antonio J Aldana, Jose Rojano, M Mar Fernandez-Santa Cruz, Raul Navas, Ismael Schiltz, Gary Farmer, Andrew Gessler, Damian Schoof, Heiko Groscurth, Andreas Research Support, Non-U.S. Gov{\textquoteright}t Review England Briefings in bioinformatics Brief Bioinform. 2008 May;9(3):220-31. Epub 2008 Jan 31.

}, pages = {220-31}, abstract = {

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

}, keywords = {Computational Biology/*methods *Database Management Systems *Databases, Factual Information Storage and Retrieval/*methods *Internet *Programming Languages Systems Integration}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18238804}, author = {Wilkinson, M. D. and Senger, M. and Kawas, E. and Bruskiewich, R. and Gouzy, J. and Noirot, C. and Bardou, P. and Ng, A. and Haase, D. and Saiz Ede, A. and Wang, D. and Gibbons, F. and Gordon, P. M. and Sensen, C. W. and Carrasco, J. M. and Fernandez, J. M. and Shen, L. and Links, M. and Ng, M. and Opushneva, N. and Neerincx, P. B. and Leunissen, J. A. and Ernst, R. and Twigger, S. and Usadel, B. and Good, B. and Wong, Y. and Stein, L. and Crosby, W. and Karlsson, J. and Royo, R. and Parraga, I. and Ramirez, S. and Gelpi, J. L. and Trelles, O. and Pisano, D. G. and Jimenez, N. and Kerhornou, A. and Rosset, R. and Zamacola, L. and Tarraga, J. and Huerta-Cepas, J. and Carazo, J. M. and Dopazo, J. and R. Guigo and Navarro, A. and Orozco, M. and Valencia, A. and Claros, M. G. and Perez, A. J. and Aldana, J. and Rojano, M. M. and Fernandez-Santa Cruz, R. and Navas, I. and Schiltz, G. and Farmer, A. and Gessler, D. and Schoof, H. and Groscurth, A.} } @article {599, title = {SNP and haplotype mapping for genetic analysis in the rat.}, journal = {Nat Genet}, volume = {40}, year = {2008}, month = {2008 May}, pages = {560-6}, abstract = {

The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81\% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.

}, keywords = {Animals, Chromosome Mapping, Databases, Genetic, Genome, Haplotypes, Linkage Disequilibrium, Phylogeny, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Rats, Rats, Inbred Strains, Recombination, Genetic}, issn = {1546-1718}, doi = {10.1038/ng.124}, author = {Saar, Kathrin and Beck, Alfred and Bihoreau, Marie-Th{\'e}r{\`e}se and Birney, Ewan and Brocklebank, Denise and Chen, Yuan and Cuppen, Edwin and Demonchy, Stephanie and Dopazo, Joaquin and Flicek, Paul and Foglio, Mario and Fujiyama, Asao and Gut, Ivo G and Gauguier, Dominique and Guig{\'o}, Roderic and Guryev, Victor and Heinig, Matthias and Hummel, Oliver and Jahn, Niels and Klages, Sven and Kren, Vladimir and Kube, Michael and Kuhl, Heiner and Kuramoto, Takashi and Kuroki, Yoko and Lechner, Doris and Lee, Young-Ae and Lopez-Bigas, Nuria and Lathrop, G Mark and Mashimo, Tomoji and Medina, Ignacio and Mott, Richard and Patone, Giannino and Perrier-Cornet, Jeanne-Antide and Platzer, Matthias and Pravenec, Michal and Reinhardt, Richard and Sakaki, Yoshiyuki and Schilhabel, Markus and Schulz, Herbert and Serikawa, Tadao and Shikhagaie, Medya and Tatsumoto, Shouji and Taudien, Stefan and Toyoda, Atsushi and Voigt, Birger and Zelenika, Diana and Zimdahl, Heike and Hubner, Norbert} } @article {18443594, title = {SNP and haplotype mapping for genetic analysis in the rat}, journal = {Nat Genet}, volume = {40}, number = {5}, year = {2008}, note = {

STAR Consortium Saar, Kathrin Beck, Alfred Bihoreau, Marie-Therese Birney, Ewan Brocklebank, Denise Chen, Yuan Cuppen, Edwin Demonchy, Stephanie Dopazo, Joaquin Flicek, Paul Foglio, Mario Fujiyama, Asao Gut, Ivo G Gauguier, Dominique Guigo, Roderic Guryev, Victor Heinig, Matthias Hummel, Oliver Jahn, Niels Klages, Sven Kren, Vladimir Kube, Michael Kuhl, Heiner Kuramoto, Takashi Kuroki, Yoko Lechner, Doris Lee, Young-Ae Lopez-Bigas, Nuria Lathrop, G Mark Mashimo, Tomoji Medina, Ignacio Mott, Richard Patone, Giannino Perrier-Cornet, Jeanne-Antide Platzer, Matthias Pravenec, Michal Reinhardt, Richard Sakaki, Yoshiyuki Schilhabel, Markus Schulz, Herbert Serikawa, Tadao Shikhagaie, Medya Tatsumoto, Shouji Taudien, Stefan Toyoda, Atsushi Voigt, Birger Zelenika, Diana Zimdahl, Heike Hubner, Norbert 057733/Z/99/A/Wellcome Trust/United Kingdom 066780/Z/01/Z/Wellcome Trust/United Kingdom Research Support, Non-U.S. Gov{\textquoteright}t Technical Report United States Nature genetics Nat Genet. 2008 May;40(5):560-6.

}, pages = {560-6}, abstract = {

The laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81\% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.

}, keywords = {Animals Chromosome Mapping *Databases, Genetic, Genetic Genome *Haplotypes Linkage Disequilibrium Phylogeny *Polymorphism, Inbred Strains/*genetics Recombination, Single Nucleotide *Quantitative Trait Loci Rats/*genetics Rats}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18443594}, author = {K. Saar and A. Beck and M. T. Bihoreau and E. Birney and D. Brocklebank and Y. Chen and E. Cuppen and S. Demonchy and Dopazo, J. and P. Flicek and M. Foglio and A. Fujiyama and I. G. Gut and D. Gauguier and R. Guigo and V. Guryev and M. Heinig and O. Hummel and N. Jahn and S. Klages and V. Kren and M. Kube and H. Kuhl and Kuramoto, T. and Kuroki, Y. and Lechner, D. and Lee, Y. A. and Lopez-Bigas, N. and Lathrop, G. M. and Mashimo, T. and Medina, Ignacio and Mott, R. and Patone, G. and Perrier-Cornet, J. A. and Platzer, M. and Pravenec, M. and Reinhardt, R. and Sakaki, Y. and Schilhabel, M. and Schulz, H. and Serikawa, T. and Shikhagaie, M. and Tatsumoto, S. and Taudien, S. and Toyoda, A. and Voigt, B. and Zelenika, D. and Zimdahl, H. and Hubner, N.} } @article {16381869, title = {MODBASE: a database of annotated comparative protein structure models and associated resources}, journal = {Nucleic Acids Res}, volume = {34}, number = {Database issue}, year = {2006}, note = {Pieper, Ursula Eswar, Narayanan Davis, Fred P Braberg, Hannes Madhusudhan, M S Rossi, Andrea Marti-Renom, Marc Karchin, Rachel Webb, Ben M Eramian, David Shen, Min-Yi Kelly, Libusha Melo, Francisco Sali, Andrej GM 08284/GM/NIGMS NIH HHS/United States 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 U54 GM074945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2006 Jan 1;34(Database issue):D291-5.}, pages = {D291-5}, abstract = {MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).}, keywords = {Binding Sites *Databases, Molecular Polymorphism, Protein Humans Internet Ligands *Models, Protein Systems Integration User-Computer Interface, Single Nucleotide Protein Structure, Tertiary Proteins/*chemistry/genetics/metabolism Software *Structural Homology}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=16381869}, author = {Pieper, U. and Eswar, N. and Davis, F. P. and Braberg, H. and Madhusudhan, M. S. and Rossi, A. and M. A. Marti-Renom and Karchin, R. and Webb, B. M. and Eramian, D. and Shen, M. Y. and Kelly, L. and Melo, F. and Sali, A.} } @article {15744302, title = {An anaerobic mitochondrion that produces hydrogen}, journal = {Nature}, volume = {434}, number = {7029}, year = {2005}, note = {Boxma, Brigitte de Graaf, Rob M van der Staay, Georg W M van Alen, Theo A Ricard, Guenola Gabaldon, Toni van Hoek, Angela H A M Moon-van der Staay, Seung Yeo Koopman, Werner J H van Hellemond, Jaap J Tielens, Aloysius G M Friedrich, Thorsten Veenhuis, Marten Huynen, Martijn A Hackstein, Johannes H P Research Support, Non-U.S. Gov{\textquoteright}t England Nature Nature. 2005 Mar 3;434(7029):74-9.}, pages = {74-9}, abstract = {Hydrogenosomes are organelles that produce ATP and hydrogen, and are found in various unrelated eukaryotes, such as anaerobic flagellates, chytridiomycete fungi and ciliates. Although all of these organelles generate hydrogen, the hydrogenosomes from these organisms are structurally and metabolically quite different, just like mitochondria where large differences also exist. These differences have led to a continuing debate about the evolutionary origin of hydrogenosomes. Here we show that the hydrogenosomes of the anaerobic ciliate Nyctotherus ovalis, which thrives in the hindgut of cockroaches, have retained a rudimentary genome encoding components of a mitochondrial electron transport chain. Phylogenetic analyses reveal that those proteins cluster with their homologues from aerobic ciliates. In addition, several nucleus-encoded components of the mitochondrial proteome, such as pyruvate dehydrogenase and complex II, were identified. The N. ovalis hydrogenosome is sensitive to inhibitors of mitochondrial complex I and produces succinate as a major metabolic end product{\textendash}biochemical traits typical of anaerobic mitochondria. The production of hydrogen, together with the presence of a genome encoding respiratory chain components, and biochemical features characteristic of anaerobic mitochondria, identify the N. ovalis organelle as a missing link between mitochondria and hydrogenosomes.}, keywords = {*Anaerobiosis Animals Ciliophora/*cytology/genetics/*metabolism/ultrastructure Cockroaches/parasitology DNA, Mitochondrial/genetics Electron Transport Electron Transport Complex I/antagonists \& inhibitors/metabolism Genome Glucose/metabolism Hydrogen/*metabolism Mitochondria/enzymology/genetics/*metabolism/ultrastructure Molecular Sequence Data Open Reading Fra}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15744302}, author = {Boxma, B. and de Graaf, R. M. and van der Staay, G. W. and van Alen, T. A. and Ricard, G. and Gabald{\'o}n, T. and van Hoek, A. H. and Moon-van der Staay, S. Y. and Koopman, W. J. and van Hellemond, J. J. and Tielens, A. G. and Friedrich, T. and Veenhuis, M. and M. A. Huynen and Hackstein, J. H.} } @article {16170324, title = {The C-type lectin fold as an evolutionary solution for massive sequence variation}, journal = {Nat Struct Mol Biol}, volume = {12}, number = {10}, year = {2005}, note = {McMahon, Stephen A Miller, Jason L Lawton, Jeffrey A Kerkow, Donald E Hodes, Asher Marti-Renom, Marc A Doulatov, Sergei Narayanan, Eswar Sali, Andrej Miller, Jeff F Ghosh, Partho F31AI061840/AI/NIAID NIH HHS/United States F32AI49695/AI/NIAID NIH HHS/United States T32GM008326/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, P.H.S. United States Nature structural \& molecular biology Nat Struct Mol Biol. 2005 Oct;12(10):886-92. Epub 2005 Sep 18.}, pages = {886-92}, abstract = {Only few instances are known of protein folds that tolerate massive sequence variation for the sake of binding diversity. The most extensively characterized is the immunoglobulin fold. We now add to this the C-type lectin (CLec) fold, as found in the major tropism determinant (Mtd), a retroelement-encoded receptor-binding protein of Bordetella bacteriophage. Variation in Mtd, with its approximately 10(13) possible sequences, enables phage adaptation to Bordetella spp. Mtd is an intertwined, pyramid-shaped trimer, with variable residues organized by its CLec fold into discrete receptor-binding sites. The CLec fold provides a highly static scaffold for combinatorial display of variable residues, probably reflecting a different evolutionary solution for balancing diversity against stability from that in the immunoglobulin fold. Mtd variants are biased toward the receptor pertactin, and there is evidence that the CLec fold is used broadly for sequence variation by related retroelements.}, keywords = {Amino Acid Sequence Bacterial Outer Membrane Proteins/*chemistry Bacteriophages/*metabolism Bordetella/*virology Evolution, Bordetella/*chemistry, C-Type/*chemistry Molecular Sequence Data Protein Conformation Protein Folding Viral Proteins/*chemistry/*genetics Virulence Factors, Molecular Genetic Variation Genome, Viral Lectins}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=16170324}, author = {McMahon, S. A. and Miller, J. L. and Lawton, J. A. and Kerkow, D. E. and Hodes, A. and M. A. Marti-Renom and Doulatov, S. and Narayanan, E. and Sali, A. and Miller, J. F. and Ghosh, P.} } @article {12824315, title = {EVA: Evaluation of protein structure prediction servers}, journal = {Nucleic Acids Res}, volume = {31}, number = {13}, year = {2003}, note = {Koh, Ingrid Y Y Eyrich, Volker A Marti-Renom, Marc A Przybylski, Dariusz Madhusudhan, Mallur S Eswar, Narayanan Grana, Osvaldo Pazos, Florencio Valencia, Alfonso Sali, Andrej Rost, Burkhard 1-P50-GM62413-01/GM/NIGMS NIH HHS/United States 5-P20-LM7276/LM/NLM NIH HHS/United States P50 GM62529/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States R01-GM63029-01/GM/NIGMS NIH HHS/United States Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, Non-P.H.S. Research Support, U.S. Gov{\textquoteright}t, P.H.S. England Nucleic acids research Nucleic Acids Res. 2003 Jul 1;31(13):3311-5.}, pages = {3311-5}, abstract = {EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.}, keywords = {Automation Databases, Protein, Protein Internet *Protein Conformation Protein Folding Protein Structure, Protein Structural Homology, Secondary Proteins/chemistry Reproducibility of Results *Sequence Analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12824315}, author = {Koh, I. Y. and Eyrich, V. A. and M. A. Marti-Renom and Przybylski, D. and Madhusudhan, M. S. and Eswar, N. and Grana, O. and Pazos, F. and Valencia, A. and Sali, A. and Rost, B.} } @article {12471146, title = {Use of single point mutations in domain I of beta 2-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies}, journal = {J Immunol}, volume = {169}, number = {12}, year = {2002}, note = {Iverson, G Michael Reddel, Stephen Victoria, Edward J Cockerill, Keith A Wang, Ying-Xia Marti-Renom, Marc A Sali, Andrej Marquis, David M Krilis, Steven A Linnik, Matthew D GM54762/GM/NIGMS NIH HHS/United States Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, P.H.S. United States Journal of immunology (Baltimore, Md. : 1950) J Immunol. 2002 Dec 15;169(12):7097-103.}, pages = {7097-103}, abstract = {Autoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope.}, keywords = {Amino Acid Substitution/genetics Antibodies, Antibody/genetics Binding, Antiphospholipid/blood/*metabolism Antibodies, Competitive/genetics/immunology Enzyme-Linked Immunosorbent Assay/methods Epitopes/analysis/*immunology/metabolism Glycine/genetics Glycoproteins/biosynthesis/*genetics/*immunology/isolation \& purification/metabolism Humans Models, Molecular Peptide Fragments/genetics/immunology/isolation \& purification/metabolism *Point Mutation Protein Structure, Monoclonal/blood/metabolism Antiphospholipid Syndrome/immunology Arginine/genetics *Binding Sites, Tertiary/genetics Recombinant Proteins/biosynthesis/immunology/isolation \& purification/metabolism Static Electricity beta 2-Glycoprotein I}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12471146}, author = {Iverson, G. M. and Reddel, S. and Victoria, E. J. and Cockerill, K. A. and Wang, Y. X. and M. A. Marti-Renom and Sali, A. and Marquis, D. M. and Krilis, S. A. and Linnik, M. D.} }