@article {817, title = {Metatranscriptomic analysis reveals gut microbiome bacterial genes in pyruvate and amino acid metabolism associated with hyperuricemia and gout in humans.}, journal = {Sci Rep}, volume = {15}, year = {2025}, month = {2025 Mar 22}, pages = {9981}, abstract = {

Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout. We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of four KO in a larger sample by qRT-PCR. A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange > 23, p-adj <= 3.56 {\texttimes} 10), the pentose pathway (Log2foldchange > 24, p-adj < 1.10 {\texttimes} 10) and purine metabolism (Log2foldchange > 22, p-adj < 1.25 {\texttimes} 10). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p-adj < 1.72 {\texttimes} 10) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p-adj < 3.31 {\texttimes} 10) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients{\textasciiacute} microbiome was validated. There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.

}, keywords = {Adult, Aged, Amino Acids, Bacteria, Case-Control Studies, Feces, Female, Gastrointestinal Microbiome, Gene Expression Profiling, Genes, Bacterial, Gout, Humans, Hyperuricemia, Male, Middle Aged, Pyruvic Acid, Transcriptome}, issn = {2045-2322}, doi = {10.1038/s41598-025-93899-1}, author = {Mart{\'\i}nez-Nava, Gabriela Ang{\'e}lica and Altamirano-Molina, Efren and V{\'a}zquez-Mellado, Janitzia and Casimiro-Soriguer, Carlos S and Dopazo, Joaquin and Lozada-P{\'e}rez, Carlos and Herrera-L{\'o}pez, Br{\'\i}gida and Mart{\'\i}nez-G{\'o}mez, Laura Edith and Mart{\'\i}nez-Armenta, Carlos and Guido-G{\'o}mora, Dafne Lissete and Valle-Guti{\'e}rrez, Sarah{\'\i} and Suarez-Ahedo, Carlos and Camacho-Rea, Mar{\'\i}a Del Carmen and Mart{\'\i}nez-Garc{\'\i}a, Mireya and Guti{\'e}rrez-Esparza, Guadalupe and Amezcua-Guerra, Luis M and Zamudio-Cuevas, Yessica and Mart{\'\i}nez-Flores, Karina and Fern{\'a}ndez-Torres, Javier and Burguete-Garc{\'\i}a, Ana I and Orbe-Orihuela, Yaneth Citlalli and Lagunas-Mart{\'\i}nez, Alfredo and M{\'e}ndez-Salazar, Eder Orlando and Francisco-Balderas, Adriana and Palacios-Gonz{\'a}lez, Berenice and Pineda, Carlos and L{\'o}pez-Reyes, Alberto} } @article {815, title = {Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.}, journal = {Mol Psychiatry}, year = {2024}, month = {2024 May 28}, abstract = {

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

}, issn = {1476-5578}, doi = {10.1038/s41380-024-02609-2}, author = {P{\'e}rez-Guti{\'e}rrez, Ana M and Carmona, Rosario and Loucera, Carlos and Cervilla, Jorge A and Guti{\'e}rrez, Blanca and Molina, Esther and L{\'o}pez-L{\'o}pez, Daniel and Perez-Florido, Javier and Zarza-Rebollo, Juan Antonio and L{\'o}pez-Isac, Elena and Dopazo, Joaquin and Martinez-Gonzalez, Luis Javier and Rivera, Margarita} } @article {770, title = {A Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact.}, journal = {Pharmaceutics}, volume = {15}, year = {2023}, month = {2023 Apr 19}, abstract = {

The implementation of pharmacogenetics (PGx) is a main milestones of precision medicine nowadays in order to achieve safer and more effective therapies. Nevertheless, the implementation of PGx diagnostics is extremely slow and unequal worldwide, in part due to a lack of ethnic PGx information. We analysed genetic data from 3006 Spanish individuals obtained by different high-throughput (HT) techniques. Allele frequencies were determined in our population for the main 21 actionable PGx genes associated with therapeutical changes. We found that 98\% of the Spanish population harbours at least one allele associated with a therapeutical change and, thus, there would be a need for a therapeutical change in a mean of 3.31 of the 64 associated drugs. We also identified 326 putative deleterious variants that were not previously related with PGx in 18 out of the 21 main PGx genes evaluated and a total of 7122 putative deleterious variants for the 1045 PGx genes described. Additionally, we performed a comparison of the main HT diagnostic techniques, revealing that after whole genome sequencing, genotyping with the PGx HT array is the most suitable solution for PGx diagnostics. Finally, all this information was integrated in the Collaborative Spanish Variant Server to be available to and updated by the scientific community.

}, issn = {1999-4923}, doi = {10.3390/pharmaceutics15041286}, author = {N{\'u}{\~n}ez-Torres, Roc{\'\i}o and Pita, Guillermo and Pe{\~n}a-Chilet, Maria and L{\'o}pez-L{\'o}pez, Daniel and Zamora, Jorge and Rold{\'a}n, Gema and Herr{\'a}ez, Bel{\'e}n and Alvarez, Nuria and Alonso, Mar{\'\i}a Rosario and Dopazo, Joaquin and Gonz{\'a}lez-Neira, Anna} } @article {760, title = {Novel genes and sex differences in COVID-19 severity.}, journal = {Hum Mol Genet}, year = {2022}, month = {2022 Jun 16}, abstract = {

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

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

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

}, keywords = {Antiviral Agents, Computational Biology, Computer Graphics, COVID-19, Cytokines, Data Mining, Databases, Factual, Gene Expression Regulation, Host Microbial Interactions, Humans, Immunity, Cellular, Immunity, Humoral, Immunity, Innate, Lymphocytes, Metabolic Networks and Pathways, Myeloid Cells, Protein Interaction Mapping, SARS-CoV-2, Signal Transduction, Software, Transcription Factors, Viral Proteins}, issn = {1744-4292}, doi = {10.15252/msb.202110387}, author = {Ostaszewski, Marek and Niarakis, Anna and Mazein, Alexander and Kuperstein, Inna and Phair, Robert and Orta-Resendiz, Aurelio and Singh, Vidisha and Aghamiri, Sara Sadat and Acencio, Marcio Luis and Glaab, Enrico and Ruepp, Andreas and Fobo, Gisela and Montrone, Corinna and Brauner, Barbara and Frishman, Goar and Monraz G{\'o}mez, Luis Crist{\'o}bal and Somers, Julia and Hoch, Matti and Kumar Gupta, Shailendra and Scheel, Julia and Borlinghaus, Hanna and Czauderna, Tobias and Schreiber, Falk and Montagud, Arnau and Ponce de Leon, Miguel and Funahashi, Akira and Hiki, Yusuke and Hiroi, Noriko and Yamada, Takahiro G and Dr{\"a}ger, Andreas and Renz, Alina and Naveez, Muhammad and Bocskei, Zsolt and Messina, Francesco and B{\"o}rnigen, Daniela and Fergusson, Liam and Conti, Marta and Rameil, Marius and Nakonecnij, Vanessa and Vanhoefer, Jakob and Schmiester, Leonard and Wang, Muying and Ackerman, Emily E and Shoemaker, Jason E and Zucker, Jeremy and Oxford, Kristie and Teuton, Jeremy and Kocakaya, Ebru and Summak, G{\"o}k{\c c}e Ya{\u g}mur and Hanspers, Kristina and Kutmon, Martina and Coort, Susan and Eijssen, Lars and Ehrhart, Friederike and Rex, Devasahayam Arokia Balaya and Slenter, Denise and Martens, Marvin and Pham, Nhung and Haw, Robin and Jassal, Bijay and Matthews, Lisa and Orlic-Milacic, Marija and Senff Ribeiro, Andrea and Rothfels, Karen and Shamovsky, Veronica and Stephan, Ralf and Sevilla, Cristoffer and Varusai, Thawfeek and Ravel, Jean-Marie and Fraser, Rupsha and Ortseifen, Vera and Marchesi, Silvia and Gawron, Piotr and Smula, Ewa and Heirendt, Laurent and Satagopam, Venkata and Wu, Guanming and Riutta, Anders and Golebiewski, Martin and Owen, Stuart and Goble, Carole and Hu, Xiaoming and Overall, Rupert W and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Vega, Carlos and Grou{\`e}s, Valentin and Vazquez, Miguel and Porras, Pablo and Licata, Luana and Iannuccelli, Marta and Sacco, Francesca and Nesterova, Anastasia and Yuryev, Anton and de Waard, Anita and Turei, Denes and Luna, Augustin and Babur, Ozgun and Soliman, Sylvain and Valdeolivas, Alberto and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Helikar, Tom{\'a}{\v s} and Puniya, Bhanwar Lal and Modos, Dezso and Treveil, Agatha and Olbei, Marton and De Meulder, Bertrand and Ballereau, Stephane and Dugourd, Aur{\'e}lien and Naldi, Aur{\'e}lien and No{\"e}l, Vincent and Calzone, Laurence and Sander, Chris and Demir, Emek and Korcsmaros, Tamas and Freeman, Tom C and Aug{\'e}, Franck and Beckmann, Jacques S and Hasenauer, Jan and Wolkenhauer, Olaf and Wilighagen, Egon L and Pico, Alexander R and Evelo, Chris T and Gillespie, Marc E and Stein, Lincoln D and Hermjakob, Henning and D{\textquoteright}Eustachio, Peter and Saez-Rodriguez, Julio and Dopazo, Joaquin and Valencia, Alfonso and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {714, title = {The NCI Genomic Data Commons}, journal = {Nature Genetics}, year = {2021}, month = {Oct-02-2022}, issn = {1061-4036}, doi = {10.1038/s41588-021-00791-5}, url = {http://www.nature.com/articles/s41588-021-00791-5}, author = {Heath, Allison P. and Ferretti, Vincent and Agrawal, Stuti and An, Maksim and Angelakos, James C. and Arya, Renuka and Bajari, Rosita and Baqar, Bilal and Barnowski, Justin H. B. and Burt, Jeffrey and Catton, Ann and Chan, Brandon F. and Chu, Fay and Cullion, Kim and Davidsen, Tanja and Do, Phuong-My and Dompierre, Christian and Ferguson, Martin L. and Fitzsimons, Michael S. and Ford, Michael and Fukuma, Miyuki and Gaheen, Sharon and Ganji, Gajanan L. and Garcia, Tzintzuni I. and George, Sameera S. and Gerhard, Daniela S. and Gerthoffert, Francois and Gomez, Fauzi and Han, Kang and Hernandez, Kyle M. and Issac, Biju and Jackson, Richard and Jensen, Mark A. and Joshi, Sid and Kadam, Ajinkya and Khurana, Aishmit and Kim, Kyle M. J. and Kraft, Victoria E. and Li, Shenglai and Lichtenberg, Tara M. and Lodato, Janice and Lolla, Laxmi and Martinov, Plamen and Mazzone, Jeffrey A. and Miller, Daniel P. and Miller, Ian and Miller, Joshua S. and Miyauchi, Koji and Murphy, Mark W. and Nullet, Thomas and Ogwara, Rowland O. and Ortu{\~n}o, Francisco M. and Pedrosa, Jes{\'u}s and Pham, Phuong L. and Popov, Maxim Y. and Porter, James J. and Powell, Raymond and Rademacher, Karl and Reid, Colin P. and Rich, Samantha and Rogel, Bessie and Sahni, Himanso and Savage, Jeremiah H. and Schmitt, Kyle A. and Simmons, Trevar J. and Sislow, Joseph and Spring, Jonathan and Stein, Lincoln and Sullivan, Sean and Tang, Yajing and Thiagarajan, Mathangi and Troyer, Heather D. and Wang, Chang and Wang, Zhining and West, Bedford L. and Wilmer, Alex and Wilson, Shane and Wu, Kaman and Wysocki, William P. and Xiang, Linda and Yamada, Joseph T. and Yang, Liming and Yu, Christine and Yung, Christina K. and Zenklusen, Jean Claude and Zhang, Junjun and Zhang, Zhenyu and Zhao, Yuanheng and Zubair, Ariz and Staudt, Louis M. and Grossman, Robert L.} } @article {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 {719, title = {Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.}, journal = {Mol Med}, volume = {27}, year = {2021}, month = {2021 05 24}, pages = {50}, abstract = {

OBJECTIVE: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.

METHODS: Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.

RESULTS: We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.

CONCLUSION: Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.

}, keywords = {Biodiversity, Computational Biology, Dysbiosis, Gastrointestinal Microbiome, Gout, Humans, Metagenome, metagenomics, Protein Interaction Mapping, Protein Interaction Maps, Uric Acid}, issn = {1528-3658}, doi = {10.1186/s10020-021-00311-5}, author = {M{\'e}ndez-Salazar, Eder Orlando and V{\'a}zquez-Mellado, Janitzia and Casimiro-Soriguer, Carlos S and Dopazo, Joaquin and Cubuk, Cankut and Zamudio-Cuevas, Yessica and Francisco-Balderas, Adriana and Mart{\'\i}nez-Flores, Karina and Fern{\'a}ndez-Torres, Javier and Lozada-P{\'e}rez, Carlos and Pineda, Carlos and S{\'a}nchez-Gonz{\'a}lez, Austreberto and Silveira, Luis H and Burguete-Garc{\'\i}a, Ana I and Orbe-Orihuela, Citlalli and Lagunas-Mart{\'\i}nez, Alfredo and Vazquez-Gomez, Alonso and L{\'o}pez-Reyes, Alberto and Palacios-Gonz{\'a}lez, Berenice and Mart{\'\i}nez-Nava, Gabriela Ang{\'e}lica} } @article {715, title = {Uniform genomic data analysis in the NCI Genomic Data CommonsAbstract}, journal = {Nature Communications}, volume = {12}, year = {2021}, month = {Jan-12-2021}, doi = {10.1038/s41467-021-21254-9}, url = {http://www.nature.com/articles/s41467-021-21254-9}, author = {Zhang, Zhenyu and Hernandez, Kyle and Savage, Jeremiah and Li, Shenglai and Miller, Dan and Agrawal, Stuti and Ortuno, Francisco and Staudt, Louis M. and Heath, Allison and Grossman, Robert 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 {437, title = {Identification of the Photoreceptor Transcriptional Co-Repressor SAMD11 as Novel Cause of Autosomal Recessive Retinitis Pigmentosa.}, journal = {Sci Rep}, volume = {6}, year = {2016}, month = {2016 10 13}, pages = {35370}, abstract = {

Retinitis pigmentosa (RP), the most frequent form of inherited retinal dystrophy is characterized by progressive photoreceptor degeneration. Many genes have been implicated in RP development, but several others remain to be identified. Using a combination of homozygosity mapping, whole-exome and targeted next-generation sequencing, we found a novel homozygous nonsense mutation in SAMD11 in five individuals diagnosed with adult-onset RP from two unrelated consanguineous Spanish families. SAMD11 is ortholog to the mouse major retinal SAM domain (mr-s) protein that is implicated in CRX-mediated transcriptional regulation in the retina. Accordingly, protein-protein network analysis revealed a significant interaction of SAMD11 with CRX. Immunoblotting analysis confirmed strong expression of SAMD11 in human retina. Immunolocalization studies revealed SAMD11 was detected in the three nuclear layers of the human retina and interestingly differential expression between cone and rod photoreceptors was observed. Our study strongly implicates SAMD11 as novel cause of RP playing an important role in the pathogenesis of human degeneration of photoreceptors.

}, keywords = {Aged, Animals, Co-Repressor Proteins, Codon, Nonsense, Cohort Studies, Comparative Genomic Hybridization, Consanguinity, DNA Mutational Analysis, Exome, Eye Proteins, Female, Gene Expression Regulation, Genes, Recessive, Homeodomain Proteins, Homozygote, Humans, Male, Mice, Middle Aged, Polymorphism, Single Nucleotide, Protein Interaction Mapping, Retina, Retinal Dystrophies, Retinal Rod Photoreceptor Cells, Retinitis pigmentosa, Spain, Trans-Activators, Transcription Factors}, issn = {2045-2322}, doi = {10.1038/srep35370}, author = {Corton, M and Avila-Fern{\'a}ndez, A and Campello, L and S{\'a}nchez, M and Benavides, B and L{\'o}pez-Molina, M I and Fern{\'a}ndez-S{\'a}nchez, L and S{\'a}nchez-Alcudia, R and da Silva, L R J and Reyes, N and Mart{\'\i}n-Garrido, E and Zurita, O and Fern{\'a}ndez-San Jos{\'e}, P and P{\'e}rez-Carro, R and Garc{\'\i}a-Garc{\'\i}a, F and Dopazo, J and Garc{\'\i}a-Sandoval, B and Cuenca, N and Ayuso, C} } @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 {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 {575, title = {Mutation spectrum of EYS in Spanish patients with autosomal recessive retinitis pigmentosa.}, journal = {Hum Mutat}, volume = {31}, year = {2010}, month = {2010 Nov}, pages = {E1772-800}, abstract = {

Retinitis pigmentosa (RP) is a heterogeneous group of inherited retinal dystrophies characterised ultimately by the loss of photoreceptor cells. We have recently identified a new gene(EYS) encoding an ortholog of Drosophila space maker (spam) as a commonly mutated gene in autosomal recessive RP. In the present study, we report the identification of 73 sequence variations in EYS, of which 28 are novel. Of these, 42.9\% (12/28) are very likely pathogenic, 17.9\% (5/28)are possibly pathogenic, whereas 39.3\% (11/28) are SNPs. In addition, we have detected 3 pathogenic changes previously reported in other populations. We are also presenting the characterisation of EYS homologues in different species, and a detailed analysis of the EYS domains, with the identification of an interesting novel feature: a putative coiled-coil domain.Majority of the mutations in the arRP patients have been found within the domain structures of EYS. The minimum observed prevalence of distinct EYS mutations in our group of patients is of 15.9\% (15/94), confirming a major involvement of EYS in the pathogenesis of arRP in the Spanish population. Along with the detection of three recurrent mutations in Caucasian population, our hypothesis of EYS being the first prevalent gene in arRP has been reinforced in the present study.

}, keywords = {Amino Acid Sequence, Animals, Case-Control Studies, DNA Mutational Analysis, Drosophila Proteins, Evolution, Molecular, Eye Proteins, Female, Genes, Recessive, Genetic Variation, Humans, Male, Molecular Sequence Data, mutation, Pedigree, Polymorphism, Single Nucleotide, Protein Structure, Tertiary, Retinitis pigmentosa, Spain, Structural Homology, Protein}, issn = {1098-1004}, doi = {10.1002/humu.21334}, author = {Barrag{\'a}n, Isabel and Borrego, Salud and Pieras, Juan Ignacio and Gonz{\'a}lez-del Pozo, Mar{\'\i}a and Santoyo, Javier and Ayuso, Carmen and Baiget, Montserrat and Mill{\'a}n, Jos{\'e} M and Mena, Marcela and Abd El-Aziz, Mai M and Audo, Isabelle and Zeitz, Christina and Littink, Karin W and Dopazo, Joaquin and Bhattacharya, Shomi S and Anti{\v n}olo, Guillermo} } @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 {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 {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 {15830128, title = {Development of a citrus genome-wide EST collection and cDNA microarray as resources for genomic studies}, journal = {Plant Mol Biol}, volume = {57}, number = {3}, year = {2005}, note = {Forment, J Gadea, J Huerta, L Abizanda, L Agusti, J Alamar, S Alos, E Andres, F Arribas, R Beltran, J P Berbel, A Blazquez, M A Brumos, J Canas, L A Cercos, M Colmenero-Flores, J M Conesa, A Estables, B Gandia, M Garcia-Martinez, J L Gimeno, J Gisbert, A Gomez, G Gonzalez-Candelas, L Granell, A Guerri, J Lafuente, M T Madueno, F Marcos, J F Marques, M C Martinez, F Martinez-Godoy, M A Miralles, S Moreno, P Navarro, L Pallas, V Perez-Amador, M A Perez-Valle, J Pons, C Rodrigo, I Rodriguez, P L Royo, C Serrano, R Soler, G Tadeo, F Talon, M Terol, J Trenor, M Vaello, L Vicente, O Vidal, Ch Zacarias, L Conejero, V Comparative Study Research Support, U.S. Gov{\textquoteright}t, Non-P.H.S. Netherlands Plant molecular biology Plant Mol Biol. 2005 Feb;57(3):375-91.}, pages = {375-91}, abstract = {A functional genomics project has been initiated to approach the molecular characterization of the main biological and agronomical traits of citrus. As a key part of this project, a citrus EST collection has been generated from 25 cDNA libraries covering different tissues, developmental stages and stress conditions. The collection includes a total of 22,635 high-quality ESTs, grouped in 11,836 putative unigenes, which represent at least one third of the estimated number of genes in the citrus genome. Functional annotation of unigenes which have Arabidopsis orthologues (68\% of all unigenes) revealed gene representation in every major functional category, suggesting that a genome-wide EST collection was obtained. A Citrus clementina Hort. ex Tan. cv. Clemenules genomic library, that will contribute to further characterization of relevant genes, has also been constructed. To initiate the analysis of citrus transcriptome, we have developed a cDNA microarray containing 12,672 probes corresponding to 6875 putative unigenes of the collection. Technical characterization of the microarray showed high intra- and inter-array reproducibility, as well as a good range of sensitivity. We have also validated gene expression data achieved with this microarray through an independent technique such as RNA gel blot analysis.}, keywords = {Citrus/*genetics DNA, Complementary/chemistry/genetics *Expressed Sequence Tags Gene Expression Profiling Gene Library *Genome, DNA, Plant Genomics/*methods Molecular Sequence Data Oligonucleotide Array Sequence Analysis/*methods RNA, Plant/genetics/metabolism Reproducibility of Results Sequence Analysis}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15830128}, author = {J. Forment and J. Gadea and Huerta, L. and Abizanda, L. and Agusti, J. and Alamar, S. and Alos, E. and Andres, F. and Arribas, R. and Beltran, J. P. and Berbel, A. and Blazquez, M. A. and Brumos, J. and Canas, L. A. and Cercos, M. and Colmenero-Flores, J. M. and A. Conesa and Estables, B. and Gandia, M. and Garcia-Martinez, J. L. and Gimeno, J. and Gisbert, A. and Gomez, G. and Gonzalez-Candelas, L. and Granell, A. and Guerri, J. and Lafuente, M. T. and Madueno, F. and Marcos, J. F. and Marques, M. C. and Martinez, F. and Martinez-Godoy, M. A. and Miralles, S. and Moreno, P. and Navarro, L. and Pallas, V. and Perez-Amador, M. A. and Perez-Valle, J. and Pons, C. and Rodrigo, I. and Rodriguez, P. L. and Royo, C. and Serrano, R. and Soler, G. and Tadeo, F. and Talon, M. and Terol, J. and Trenor, M. and Vaello, L. and Vicente, O. and Vidal, Ch and Zacarias, L. and Conejero, V.} } @article {11251224, title = {Methods and approaches in the analysis of gene expression data}, journal = {J Immunol Methods}, volume = {250}, number = {1-2}, year = {2001}, note = {

Dopazo, J Zanders, E Dragoni, I Amphlett, G Falciani, F Comparative Study Review Netherlands Journal of immunological methods J Immunol Methods. 2001 Apr;250(1-2):93-112.

}, pages = {93-112}, abstract = {

The application of high-density DNA array technology to monitor gene transcription has been responsible for a real paradigm shift in biology. The majority of research groups now have the ability to measure the expression of a significant proportion of the human genome in a single experiment, resulting in an unprecedented volume of data being made available to the scientific community. As a consequence of this, the storage, analysis and interpretation of this information present a major challenge. In the field of immunology the analysis of gene expression profiles has opened new areas of investigation. The study of cellular responses has revealed that cells respond to an activation signal with waves of co-ordinated gene expression profiles and that the components of these responses are the key to understanding the specific mechanisms which lead to phenotypic differentiation. The discovery of {\textquoteright}cell type specific{\textquoteright} gene expression signatures have also helped the interpretation of the mechanisms leading to disease progression. Here we review the principles behind the most commonly used data analysis methods and discuss the approaches that have been employed in immunological research.

}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11251224}, author = {Dopazo, J. and Zanders, E. and Dragoni, I. and Amphlett, G. and Falciani, F.} }