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1. A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.

Author

Lee S, Hahn G, Hecker J, Lutz SM, Mullin K, Hide W, Bertram L, DeMeo DL, Tanzi RE, Lange C, and Prokopenko D

Subjects
Principal Component Analysis, Gene Frequency, Computer Simulation, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Genome, Genomics
Abstract

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Published
2023
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2. Gateways to the FANTOM5 promoter level mammalian expression atlas.

Author

Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S, Mungall CJ, Arner E, Baillie JK, Bertin N, Bono H, de Hoon M, Diehl AD, Dimont E, Freeman TC, Fujieda K, Hide W, Kaliyaperumal R, Katayama T, Lassmann T, Meehan TF, Nishikata K, Ono H, Rehli M, Sandelin A, Schultes EA, 't Hoen PA, Tatum Z, Thompson M, Toyoda T, Wright DW, Daub CO, Itoh M, Carninci P, Hayashizaki Y, Forrest AR, and Kawaji H

Subjects
Animals, Computational Biology methods, Databases, Genetic, Datasets as Topic, Gene Expression Profiling, Humans, Mice, Transcriptome, User-Computer Interface, Genomics methods, Promoter Regions, Genetic, Software, Transcription Initiation, Genetic
Abstract

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.

Published
2015
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3. Research capacity. Enabling the genomic revolution in Africa.

Author

Rotimi C, Abayomi A, Abimiku A, Adabayeri VM, Adebamowo C, Adebiyi E, Ademola AD, Adeyemo A, Adu D, Affolabi D, Agongo G, Ajayi S, Akarolo-Anthony S, Akinyemi R, Akpalu A, Alberts M, Alonso Betancourt O, Alzohairy AM, Ameni G, Amodu O, Anabwani G, Andersen K, Arogundade F, Arulogun O, Asogun D, Bakare R, Balde N, Baniecki ML, Beiswanger C, Benkahla A, Bethke L, Boehnke M, Boima V, Brandful J, Brooks AI, Brosius FC, Brown C, Bucheton B, Burke DT, Burnett BG, Carrington-Lawrence S, Carstens N, Chisi J, Christoffels A, Cooper R, Cordell H, Crowther N, Croxton T, de Vries J, Derr L, Donkor P, Doumbia S, Duncanson A, Ekem I, El Sayed A, Engel ME, Enyaru JC, Everett D, Fadlelmola FM, Fakunle E, Fischbeck KH, Fischer A, Folarin O, Gamieldien J, Garry RF, Gaseitsiwe S, Gbadegesin R, Ghansah A, Giovanni M, Goesbeck P, Gomez-Olive FX, Grant DS, Grewal R, Guyer M, Hanchard NA, Happi CT, Hazelhurst S, Hennig BJ, Hertz- C, Fowler, Hide W, Hilderbrandt F, Hugo-Hamman C, Ibrahim ME, James R, Jaufeerally-Fakim Y, Jenkins C, Jentsch U, Jiang PP, Joloba M, Jongeneel V, Joubert F, Kader M, Kahn K, Kaleebu P, Kapiga SH, Kassim SK, Kasvosve I, Kayondo J, Keavney B, Kekitiinwa A, Khan SH, Kimmel P, King MC, Kleta R, Koffi M, Kopp J, Kretzler M, Kumuthini J, Kyobe S, Kyobutungi C, Lackland DT, Lacourciere KA, Landouré G, Lawlor R, Lehner T, Lesosky M, Levitt N, Littler K, Lombard Z, Loring JF, Lyantagaye S, Macleod A, Madden EB, Mahomva CR, Makani J, Mamven M, Marape M, Mardon G, Marshall P, Martin DP, Masiga D, Mason R, Mate-Kole M, Matovu E, Mayige M, Mayosi BM, Mbanya JC, McCurdy SA, McCarthy MI, McIlleron H, Mc'Ligeyo SO, Merle C, Mocumbi AO, Mondo C, Moran JV, Motala A, Moxey-Mims M, Mpoloka WS, Msefula CL, Mthiyane T, Mulder N, Mulugeta Gh, Mumba D, Musuku J, Nagdee M, Nash O, Ndiaye D, Nguyen AQ, Nicol M, Nkomazana O, Norris S, Nsangi B, Nyarko A, Nyirenda M, Obe E, Obiakor R, Oduro A, Ofori-Acquah SF, Ogah O, Ogendo S, Ohene-Frempong K, Ojo A, Olanrewaju T, Oli J, Osafo C, Ouwe Missi Oukem-Boyer O, Ovbiagele B, Owen A, Owolabi MO, Owolabi L, Owusu-Dabo E, Pare G, Parekh R, Patterton HG, Penno MB, Peterson J, Pieper R, Plange-Rhule J, Pollak M, Puzak J, Ramesar RS, Ramsay M, Rasooly R, Reddy S, Sabeti PC, Sagoe K, Salako T, Samassékou O, Sandhu MS, Sankoh O, Sarfo FS, Sarr M, Shaboodien G, Sidibe I, Simo G, Simuunza M, Smeeth L, Sobngwi E, Soodyall H, Sorgho H, Sow Bah O, Srinivasan S, Stein DJ, Susser ES, Swanepoel C, Tangwa G, Tareila A, Tastan Bishop O, Tayo B, Tiffin N, Tinto H, Tobin E, Tollman SM, Traoré M, Treadwell MJ, Troyer J, Tsimako-Johnstone M, Tukei V, Ulasi I, Ulenga N, van Rooyen B, Wachinou AP, Waddy SP, Wade A, Wayengera M, Whitworth J, Wideroff L, Winkler CA, Winnicki S, Wonkam A, Yewondwos M, sen T, Yozwiak N, and Zar H

Subjects
Africa, England, Genetics, Medical trends, Health, Humans, National Institutes of Health (U.S.), United States, Disease genetics, Genome-Wide Association Study trends, Genomics trends
Published
2014
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4. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls.

Author

Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, and Salit M

Subjects
Genotype, Humans, Internet, Software, Genetic Techniques, Genome, Human genetics, Genomics methods, INDEL Mutation genetics, Polymorphism, Single Nucleotide genetics, Sequence Analysis, DNA methods
Abstract

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

Published
2014
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5. A prioritization analysis of disease association by data-mining of functional annotation of human genes.

Author

Taniya T, Tanaka S, Yamaguchi-Kabata Y, Hanaoka H, Yamasaki C, Maekawa H, Barrero RA, Lenhard B, Datta MW, Shimoyama M, Bumgarner R, Chakraborty R, Hopkinson I, Jia L, Hide W, Auffray C, Minoshima S, Imanishi T, and Gojobori T

Subjects
Arthritis, Rheumatoid genetics, Cost-Benefit Analysis, Cytokine TWEAK, Data Mining, Genetic Association Studies economics, Humans, Male, Oncostatin M genetics, Prostatic Neoplasms genetics, Tumor Necrosis Factors genetics, Genetic Association Studies methods, Genetic Predisposition to Disease genetics, Genomics methods
Abstract

Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis., (Copyright © 2011. Published by Elsevier Inc.)

Published
2012
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6. The LIFEdb database in 2006.

Author

Mehrle A, Rosenfelder H, Schupp I, del Val C, Arlt D, Hahne F, Bechtel S, Simpson J, Hofmann O, Hide W, Glatting KH, Huber W, Pepperkok R, Poustka A, and Wiemann S

Subjects
Cell Proliferation, Computational Biology, DNA, Complementary chemistry, Genes, Internet, Proteins metabolism, Recombinant Fusion Proteins metabolism, Systems Integration, User-Computer Interface, Databases, Genetic, Gene Expression, Genomics, Proteins analysis, Proteins genetics
Abstract

LIFEdb (http://www.LIFEdb.de) integrates data from large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. New features of LIFEdb include (i) an updated user interface with enhanced query capabilities, (ii) a configurable output table and the option to download search results in XML, (iii) the integration of data from cell-based screening assays addressing the influence of protein-overexpression on cell proliferation and (iv) the display of the relative expression ('Electronic Northern') of the genes under investigation using curated gene expression ontology information. LIFEdb enables researchers to systematically select and characterize genes and proteins of interest, and presents data and information via its user-friendly web-based interface.

Published
2006
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7. Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

Author

Tiffin N, Kelso JF, Powell AR, Pan H, Bajic VB, and Hide WA

Subjects
Anatomy, Databases, Genetic, Gene Expression Profiling, Humans, Systems Integration, Vocabulary, Controlled, Computational Biology methods, Genetic Predisposition to Disease, Genomics methods, PubMed
Abstract

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Published
2005
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8. Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer’s disease-associated genes: DTNB and DLG2

Author

Prokopenko, Dmitry, Lee, Sanghun, Hecker, Julian, Mullin, Kristina, Morgan, Sarah, Katsumata, Yuriko, Weiner, Michael W, Fardo, David W, Laird, Nan, Bertram, Lars, Hide, Winston, Lange, Christoph, and Tanzi, Rudolph E

Subjects
Aging, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biotechnology, Acquired Cognitive Impairment, Human Genome, Neurodegenerative, Alzheimer's Disease, Dementia, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Neurological, Good Health and Well Being, Alzheimer Disease, Dithionitrobenzoic Acid, Dystrophin-Associated Proteins, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Guanylate Kinases, Humans, Neuropeptides, Polymorphism, Single Nucleotide, Tumor Suppressor Proteins, Whole Genome Sequencing, Alzheimer’s Disease Neuroimaging Initiative, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Alzheimer's disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency

Published
2022

9. Enabling the genomic revolution in Africa

Author

Rotimi, Charles, Abayomi, Akin, Abimiku, Alash'le, Adabayeri, Victoria May, Adebamowo, Clement, Adebiyi, Ezekiel, Ademola, Adebowale D, Adeyemo, Adebowale, Adu, Dwomoa, Affolabi, Dissou, Agongo, Godfred, Ajayi, Samuel, Akarolo-Anthony, Sally, Akinyemi, Rufus, Akpalu, Albert, Alberts, Marianne, Alonso Betancourt, Orlando, Alzohairy, Ahmed Mansour, Ameni, Gobena, Amodu, Olukemi, Anabwani, Gabriel, Andersen, Kristian, Arogundade, Fatiu, Arulogun, Oyedunni, Asogun, Danny, Bakare, Rasheed, Balde, Naby, Baniecki, Mary Lynn, Beiswanger, Christine, Benkahla, Alia, Bethke, Lara, Boehnke, Micheal, Boima, Vincent, Brandful, James, Brooks, Andrew I, Brosius, Frank C, Brown, Chester, Bucheton, Bruno, Burke, David T, Burnett, Barrington G, Carrington-Lawrence, Stacy, Carstens, Nadia, Chisi, John, Christoffels, Alan, Cooper, Richard, Cordell, Heather, Crowther, Nigel, Croxton, Talishiea, de Vries, Jantina, Derr, Leslie, Donkor, Peter, Doumbia, Seydou, Duncanson, Audrey, Ekem, Ivy, El Sayed, Ahmed, Engel, Mark E, Enyaru, John CK, Everett, Dean, Fadlelmola, Faisal M, Fakunle, Eyitayo, Fischbeck, Kenneth H, Fischer, Anne, Folarin, Onikepe, Gamieldien, Junaid, Garry, Robert F, Gaseitsiwe, Simani, Gbadegesin, Rasheed, Ghansah, Anita, Giovanni, Maria, Goesbeck, Parham, Gomez-Olive, F Xavier, Grant, Donald S, Grewal, Ravnit, Guyer, Mark, Hanchard, Neil A, Happi, Christian T, Hazelhurst, Scott, Hennig, Branwen J, Hertz-, Christiane, Fowler, Hide, Winston, Hilderbrandt, Friedhelm, Hugo-Hamman, Christopher, Ibrahim, Muntaser E, James, Regina, Jaufeerally-Fakim, Yasmina, Jenkins, Carolyn, Jentsch, Ute, Jiang, Pan-Pan, Joloba, Moses, Jongeneel, Victor, Joubert, Fourie, Kader, Mukthar, Kahn, Kathleen, Kaleebu, Pontiano, Kapiga, Saidi H, Kassim, Samar Kamal, Kasvosve, Ishmael, Kayondo, Jonathan, and Keavney, Bernard

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Clinical Sciences, Medical Microbiology, Human Genome, Cancer, Infectious Diseases, HIV/AIDS, Clinical Research, Biotechnology, Rare Diseases, 2.6 Resources and infrastructure (aetiology), Aetiology, Infection, Good Health and Well Being, Africa, Disease, England, Genetics, Medical, Genome-Wide Association Study, Genomics, Health, Humans, National Institutes of Health (U.S.), United States, H3Africa Consortium, General Science & Technology
Abstract

H3Africa is developing capacity for health-related genomics research in Africa

Published
2014

10. H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa

Author

Mulder, Nicola J, Adebiyi, Ezekiel, Alami, Raouf, Benkahla, Alia, Brandful, James, Doumbia, Seydou, Everett, Dean, Fadlelmola, Faisal M, Gaboun, Fatima, Gaseitsiwe, Simani, Ghazal, Hassan, Hazelhurst, Scott, Hide, Winston, Ibrahimi, Azeddine, Jaufeerally Fakim, Yasmina, Jongeneel, C Victor, Joubert, Fourie, Kassim, Samar, Kayondo, Jonathan, Kumuthini, Judit, Lyantagaye, Sylvester, Makani, Julie, Mansour Alzohairy, Ahmed, Masiga, Daniel, Moussa, Ahmed, Nash, Oyekanmi, Ouwe Missi Oukem-Boyer, Odile, Owusu-Dabo, Ellis, Panji, Sumir, Patterton, Hugh, Radouani, Fouzia, Sadki, Khalid, Department of Integrative Biomedical Sciences (IBMS), and Faculty of Health Sciences

Subjects
Computer Systems, Genetics, Medical, Africa, Computational Biology, Genetic Variation, Humans, Genomics, Health Promotion, African Continental Ancestry Group
Abstract

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.

Published
2016
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11. A Genome-Wide Association Search for Type 2 Diabetes Genes in African Americans

Author

Palmer, Nicholette D, McDonough, Caitrin W, Hicks, Pamela J, Roh, Bong H, Wing, Maria R, An, S Sandy, Hester, Jessica M, Cooke, Jessica N, Bostrom, Meredith A, Rudock, Megan E, Talbert, Matthew E, Lewis, Joshua P, DIAGRAM Consortium, MAGIC Investigators, Ferrara, Assiamira, Lu, Lingyi, Ziegler, Julie T, Sale, Michele M, Divers, Jasmin, Shriner, Daniel, Adeyemo, Adebowale, Rotimi, Charles N, Ng, Maggie CY, Langefeld, Carl D, Freedman, Barry I, Bowden, Donald W, Voight, Benjamin F, Scott, Laura J, Steinthorsdottir, Valgerdur, Morris, Andrew P, Dina, Christian, Welch, Ryan P, Zeggini, Eleftheria, Huth, Cornelia, Aulchenko, Yurii S, Thorleifsson, Gudmar, McCulloch, Laura J, Ferreira, Teresa, Grallert, Harald, Amin, Najaf, Wu, Guanming, Willer, Cristen J, Raychaudhuri, Soumya, McCarroll, Steve A, Langenberg, Claudia, Hofmann, Oliver M, Dupuis, Josée, Qi, Lu, Segrè, Ayellet V, Van Hoek, Mandy, Navarro, Pau, Ardlie, Kristin, Balkau, Beverley, Benediktsson, Rafn, Bennett, Amanda J, Blagieva, Roza, Boerwinkle, Eric, Bonnycastle, Lori L, Boström, Kristina Bengtsson, Bravenboer, Bert, Bumpstead, Suzannah, Burtt, Noël P, Charpentier, Guillaume, Chines, Peter S, Cornelis, Marilyn, Couper, David J, Crawford, Gabe, Doney, Alex SF, Elliott, Katherine S, Elliott, Amanda L, Erdos, Michael R, Fox, Caroline S, Franklin, Christopher S, Ganser, Martha, Gieger, Christian, Grarup, Niels, Green, Todd, Griffin, Simon, Groves, Christopher J, Guiducci, Candace, Hadjadj, Samy, Hassanali, Neelam, Herder, Christian, Isomaa, Bo, Jackson, Anne U, Johnson, Paul RV, Jørgensen, Torben, Kao, Wen HL, Klopp, Norman, Kong, Augustine, Kraft, Peter, Kuusisto, Johanna, Lauritzen, Torsten, Li, Man, Lieverse, Aloysius, Lindgren, Cecilia M, Lyssenko, Valeriya, Marre, Michel, Meitinger, Thomas, Midthjell, Kristian, Morken, Mario A, Narisu, Narisu, Nilsson, Peter, Owen, Katharine R, Payne, Felicity, Perry, John RB, Petersen, Ann-Kristin, Platou, Carl, Proença, Christine, Prokopenko, Inga, Rathmann, Wolfgang, Rayner, N William, Robertson, Neil R, Rocheleau, Ghislain, Roden, Michael, Sampson, Michael J, Saxena, Richa, Shields, Beverley M, Shrader, Peter, Sigurdsson, Gunnar, Sparsø, Thomas, Strassburger, Klaus, Stringham, Heather M, Sun, Qi, Swift, Amy J, Thorand, Barbara, Tichet, Jean, Tuomi, Tiinamaija, Van Dam, Rob M, Van Haeften, Timon W, Van Herpt, Thijs, Van Vliet-Ostaptchouk, Jana V, Walters, G Bragi, Weedon, Michael N, Wijmenga, Cisca, Witteman, Jacqueline, Bergman, Richard N, Cauchi, Stephane, Collins, Francis S, Gloyn, Anna L, Gyllensten, Ulf, Hansen, Torben, Hide, Winston A, Hitman, Graham A, Hofman, Albert, Hunter, David J, Hveem, Kristian, Laakso, Markku, Mohlke, Karen L, Morris, Andrew D, Palmer, Colin NA, Pramstaller, Peter P, Rudan, Igor, Sijbrands, Eric, Stein, Lincoln D, Tuomilehto, Jaakko, Uitterlinden, Andre, Walker, Mark, Wareham, Nicholas J, Watanabe, Richard M, Abecasis, Goncalo R, Boehm, Bernhard O, Campbell, Harry, Daly, Mark J, Hattersley, Andrew T, Hu, Frank B, Meigs, James B, Pankow, James S, Pedersen, Oluf, Wichmann, H-Erich, Barroso, Inês, Florez, Jose C, Frayling, Timothy M, Groop, Leif, Sladek, Rob, Thorsteinsdottir, Unnur, Wilson, James F, Illig, Thomas, Froguel, Philippe, Van Duijn, Cornelia M, Stefansson, Kari, Altshuler, David, Boehnke, Michael, McCarthy, Mark I, Soranzo, Nicole, Wheeler, Eleanor, Glazer, Nicole L, Bouatia-Naji, Nabila, Mägi, Reedik, Randall, Joshua, Johnson, Toby, Elliott, Paul, Rybin, Denis, Henneman, Peter, Dehghan, Abbas, Hottenga, Jouke Jan, Song, Kijoung, Goel, Anuj, Egan, Josephine M, Lajunen, Taina, Doney, Alex, Kanoni, Stavroula, Cavalcanti-Proença, Christine, Kumari, Meena, Timpson, Nicholas J, Zabena, Carina, Ingelsson, Erik, An, Ping, O'Connell, Jeffrey, Luan, Jian'an, Elliott, Amanda, McCarroll, Steven A, Roccasecca, Rosa Maria, Pattou, François, Sethupathy, Praveen, Ariyurek, Yavuz, Barter, Philip, Beilby, John P, Ben-Shlomo, Yoav, Bergmann, Sven, Bochud, Murielle, Bonnefond, Amélie, Borch-Johnsen, Knut, Böttcher, Yvonne, Brunner, Eric, Bumpstead, Suzannah J, Chen, Yii-Der Ida, Chines, Peter, Clarke, Robert, Coin, Lachlan JM, Cooper, Matthew N, Crisponi, Laura, Day, Ian NM, De Geus, Eco JC, Delplanque, Jerome, Fedson, Annette C, Fischer-Rosinsky, Antje, Forouhi, Nita G, Frants, Rune, Franzosi, Maria Grazia, Galan, Pilar, Goodarzi, Mark O, Graessler, Jürgen, Grundy, Scott, Gwilliam, Rhian, Hallmans, Göran, Hammond, Naomi, Han, Xijing, Hartikainen, Anna-Liisa, Hayward, Caroline, Heath, Simon C, Hercberg, Serge, Hicks, Andrew A, Hillman, David R, Hingorani, Aroon D, Hui, Jennie, Hung, Joe, Jula, Antti, Kaakinen, Marika, Kaprio, Jaakko, Kesaniemi, Y Antero, Kivimaki, Mika, Knight, Beatrice, Koskinen, Seppo, Kovacs, Peter, Kyvik, Kirsten Ohm, Lathrop, G Mark, Lawlor, Debbie A, Le Bacquer, Olivier, Lecoeur, Cécile, Li, Yun, Mahley, Robert, Mangino, Massimo, Manning, Alisa K, Martínez-Larrad, María Teresa, McAteer, Jarred B, McPherson, Ruth, Meisinger, Christa, Melzer, David, Meyre, David, Mitchell, Braxton D, Mukherjee, Sutapa, Naitza, Silvia, Neville, Matthew J, Oostra, Ben A, Orrù, Marco, Pakyz, Ruth, Paolisso, Giuseppe, Pattaro, Cristian, Pearson, Daniel, Peden, John F, Pedersen, Nancy L, Perola, Markus, Pfeiffer, Andreas FH, Pichler, Irene, Polasek, Ozren, Posthuma, Danielle, Potter, Simon C, Pouta, Anneli, Province, Michael A, Psaty, Bruce M, Rayner, Nigel W, Rice, Kenneth, Ripatti, Samuli, Rivadeneira, Fernando, Rolandsson, Olov, Sandbaek, Annelli, Sandhu, Manjinder, Sanna, Serena, Sayer, Avan Aihie, Scheet, Paul, Seedorf, Udo, Sharp, Stephen J, Shields, Beverley, Sijbrands, Eric JG, Silveira, Angela, Simpson, Laila, Singleton, Andrew, Smith, Nicholas L, Sovio, Ulla, Swift, Amy, Syddall, Holly, Syvänen, Ann-Christine, Tanaka, Toshiko, Tönjes, Anke, Uitterlinden, André G, Van Dijk, Ko Willems, Varma, Dhiraj, Visvikis-Siest, Sophie, Vitart, Veronique, Vogelzangs, Nicole, Waeber, Gérard, Wagner, Peter J, Walley, Andrew, Ward, Kim L, Watkins, Hugh, Wild, Sarah H, Willemsen, Gonneke, Witteman, Jaqueline CM, Yarnell, John WG, Zelenika, Diana, Zethelius, Björn, Zhai, Guangju, Zhao, Jing Hua, Zillikens, M Carola, Borecki, Ingrid B, Loos, Ruth JF, Meneton, Pierre, Magnusson, Patrik KE, Nathan, David M, Williams, Gordon H, Silander, Kaisa, Salomaa, Veikko, Smith, George Davey, Bornstein, Stefan R, Schwarz, Peter, Spranger, Joachim, Karpe, Fredrik, Shuldiner, Alan R, Cooper, Cyrus, Dedoussis, George V, Serrano-Ríos, Manuel, Lind, Lars, Palmer, Lyle J, Franks, Paul W, Ebrahim, Shah, Marmot, Michael, Kao, WH Linda, Pramstaller, Peter Paul, Wright, Alan F, Stumvoll, Michael, Hamsten, Anders, Buchanan, Thomas A, Valle, Timo T, Rotter, Jerome I, Siscovick, David S, Penninx, Brenda WJH, Boomsma, Dorret I, Deloukas, Panos, Spector, Timothy D, Ferrucci, Luigi, Cao, Antonio, Scuteri, Angelo, Schlessinger, David, Uda, Manuela, Ruokonen, Aimo, Jarvelin, Marjo-Riitta, Waterworth, Dawn M, Vollenweider, Peter, Peltonen, Leena, Mooser, Vincent, Sladek, Robert, Center for Liver, Digestive and Metabolic Diseases (CLDM), Palmer, Nd, Mcdonough, Cw, Hicks, Pj, Roh, Bh, Wing, Mr, An, S, Hester, Jm, Cooke, Jn, Bostrom, Ma, Rudock, Me, Talbert, Me, Lewis, Jp, Diagram, Consortium, Magic, Investigator, Ferrara, A, Lu, L, Ziegler, Jt, Sale, Mm, Divers, J, Shriner, D, Adeyemo, A, Rotimi, Cn, Ng, Mc, Langefeld, Cd, Freedman, Bi, Bowden, Dw, Voight, Bf, Scott, Lj, Steinthorsdottir, V, Morris, Ap, Dina, C, Welch, Rp, Zeggini, E, Huth, C, Aulchenko, Y, Thorleifsson, G, Mcculloch, Lj, Ferreira, T, Grallert, H, Amin, N, Wu, G, Willer, Cj, Raychaudhuri, S, Mccarroll, Sa, Langenberg, C, Hofmann, Om, Dupuis, J, Qi, L, Segrè, Av, van Hoek, M, Navarro, P, Ardlie, K, Balkau, B, Benediktsson, R, Bennett, Aj, Blagieva, R, Boerwinkle, E, Bonnycastle, Ll, Boström, Kb, Bravenboer, B, Bumpstead, S, Burtt, Np, Charpentier, G, Chines, P, Cornelis, M, Couper, Dj, Crawford, G, Doney, A, Elliott, K, Elliott, Al, Erdos, Mr, Fox, C, Franklin, C, Ganser, M, Gieger, C, Grarup, N, Green, T, Griffin, S, Groves, Cj, Guiducci, C, Hadjadj, S, Hassanali, N, Herder, C, Isomaa, B, Jackson, Au, Johnson, Pr, Jørgensen, T, Kao, Wh, Klopp, N, Kong, A, Kraft, P, Kuusisto, J, Lauritzen, T, Li, M, Lieverse, A, Lindgren, Cm, Lyssenko, V, Marre, M, Meitinger, T, Midthjell, K, Morken, Ma, Narisu, N, Nilsson, P, Owen, Kr, Payne, F, Perry, Jr, Petersen, Ak, Platou, C, Proença, C, Prokopenko, I, Rathmann, W, Rayner, Nw, Robertson, Nr, Rocheleau, G, Roden, M, Sampson, Mj, Saxena, R, Shields, Bm, Shrader, P, Sigurdsson, G, Sparsø, T, Strassburger, K, Stringham, Hm, Sun, Q, Swift, Aj, Thorand, B, Tichet, J, Tuomi, T, van Dam, Rm, van Haeften, Tw, van Herpt, T, van Vliet Ostaptchouk, Jv, Walters, Gb, Weedon, Mn, Wijmenga, C, Witteman, J, Bergman, Rn, Cauchi, S, Collins, F, Gloyn, Al, Gyllensten, U, Hansen, T, Hide, Wa, Hitman, Ga, Hofman, A, Hunter, Dj, Hveem, K, Laakso, M, Mohlke, Kl, Morris, Ad, Palmer, Cn, Pramstaller, Pp, Rudan, I, Sijbrands, E, Stein, Ld, Tuomilehto, J, Uitterlinden, A, Walker, M, Wareham, Nj, Watanabe, Rm, Abecasis, Gr, Boehm, Bo, Campbell, H, Daly, Mj, Hattersley, At, Hu, Fb, Meigs, Jb, Pankow, J, Pedersen, O, Wichmann, He, Barroso, I, Florez, Jc, Frayling, Tm, Groop, L, Sladek, R, Thorsteinsdottir, U, Wilson, Jf, Illig, T, Froguel, P, van Duijn, Cm, Stefansson, K, Altshuler, D, Boehnke, M, Mccarthy, Mi, Soranzo, N, Wheeler, E, Glazer, Nl, Bouatia Naji, N, Mägi, R, Randall, J, Johnson, T, Elliott, P, Rybin, D, Henneman, P, Dehghan, A, Hottenga, Jj, Song, K, Goel, A, Egan, Jm, Lajunen, T, Kanoni, S, Cavalcanti Proença, C, Kumari, M, Timpson, Nj, Zabena, C, Ingelsson, E, An, P, O'Connell, J, Luan, J, Elliott, A, Roccasecca, Rm, Pattou, F, Sethupathy, P, Ariyurek, Y, Barter, P, Beilby, Jp, Ben Shlomo, Y, Bergmann, S, Bochud, M, Bonnefond, A, Borch Johnsen, K, Böttcher, Y, Brunner, E, Bumpstead, Sj, Chen, Yd, Clarke, R, Coin, Lj, Cooper, Mn, Crisponi, L, Day, In, de Geus, Ej, Delplanque, J, Fedson, Ac, Fischer Rosinsky, A, Forouhi, Ng, Frants, R, Franzosi, Mg, Galan, P, Goodarzi, Mo, Graessler, J, Grundy, S, Gwilliam, R, Hallmans, G, Hammond, N, Han, X, Hartikainen, Al, Hayward, C, Heath, Sc, Hercberg, S, Hicks, Aa, Hillman, Dr, Hingorani, Ad, Hui, J, Hung, J, Jula, A, Kaakinen, M, Kaprio, J, Kesaniemi, Ya, Kivimaki, M, Knight, B, Koskinen, S, Kovacs, P, Kyvik, Ko, Lathrop, Gm, Lawlor, Da, Le Bacquer, O, Lecoeur, C, Li, Y, Mahley, R, Mangino, M, Manning, Ak, Martínez Larrad, Mt, Mcateer, Jb, Mcpherson, R, Meisinger, C, Melzer, D, Meyre, D, Mitchell, Bd, Mukherjee, S, Naitza, S, Neville, Mj, Oostra, Ba, Orrù, M, Pakyz, R, Paolisso, Giuseppe, Pattaro, C, Pearson, D, Peden, Jf, Pedersen, Nl, Perola, M, Pfeiffer, Af, Pichler, I, Polasek, O, Posthuma, D, Potter, Sc, Pouta, A, Province, Ma, Psaty, Bm, Rice, K, Ripatti, S, Rivadeneira, F, Rolandsson, O, Sandbaek, A, Sandhu, M, Sanna, S, Sayer, Aa, Scheet, P, Seedorf, U, Sharp, Sj, Shields, B, Sijbrands, Ej, Silveira, A, Simpson, L, Singleton, A, Smith, Nl, Sovio, U, Swift, A, Syddall, H, Syvänen, Ac, Tanaka, T, Tönjes, A, Uitterlinden, Ag, van Dijk, Kw, Varma, D, Visvikis Siest, S, Vitart, V, Vogelzangs, N, Waeber, G, Wagner, Pj, Walley, A, Ward, Kl, Watkins, H, Wild, Sh, Willemsen, G, Witteman, Jc, Yarnell, Jw, Zelenika, D, Zethelius, B, Zhai, G, Zhao, Jh, Zillikens, Mc, Borecki, Ib, Loos, Rj, Meneton, P, Magnusson, Pk, Nathan, Dm, Williams, Gh, Silander, K, Salomaa, V, Smith, Gd, Bornstein, Sr, Schwarz, P, Spranger, J, Karpe, F, Shuldiner, Ar, Cooper, C, Dedoussis, Gv, Serrano Ríos, M, Lind, L, Palmer, Lj, Franks, Pw, Ebrahim, S, Marmot, M, Wright, Af, Stumvoll, M, Hamsten, A, Buchanan, Ta, Valle, Tt, Rotter, Ji, Siscovick, D, Penninx, Bw, Boomsma, Di, Deloukas, P, Spector, Td, Ferrucci, L, Cao, A, Scuteri, A, Schlessinger, D, Uda, M, Ruokonen, A, Jarvelin, Mr, Waterworth, Dm, Vollenweider, P, Peltonen, L, Mooser, V, Sladek, R., Medical Research Council (MRC), Human genetics, Psychiatry, NCA - Attention & Cognition, EMGO - Lifestyle, overweight and diabetes, Biological Psychology, Functional Genomics, Neuroscience Campus Amsterdam - Attention & Cognition, EMGO+ - Lifestyle, Overweight and Diabetes, Langenberg, Claudia [0000-0002-5017-7344], Griffin, Simon [0000-0002-2157-4797], Wareham, Nicholas [0000-0003-1422-2993], Soranzo, Nicole [0000-0003-1095-3852], Wheeler, Eleanor [0000-0002-8616-6444], Luan, Jian'an [0000-0003-3137-6337], Forouhi, Nita [0000-0002-5041-248X], Sharp, Stephen [0000-0003-2375-1440], Sovio, Ulla [0000-0002-0799-1105], Apollo - University of Cambridge Repository, DIAGRAM Consortium, MAGIC Investigators, Johnson, T., Bergman, S., Bochud, M., Waeber, G., and Vollenweider, P.

Subjects
Netherlands Twin Register (NTR), Male, Adult, African Americans/genetics, Aged, Case-Control Studies, Cohort Studies, Diabetes Mellitus, Type 2/ethnology, Diabetes Mellitus, Type 2/genetics, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Meta-Analysis as Topic, Middle Aged, Polymorphism, Single Nucleotide, Validation Studies as Topic, Medicin och hälsovetenskap, Linkage disequilibrium, Genetic Screens, endocrine system diseases, lcsh:Medicine, Genome-wide association study, Medical and Health Sciences, 0302 clinical medicine, Endocrinology, Genome Sequencing, lcsh:Science, Medicine(all), Genetics, African Americans, 0303 health sciences, education.field_of_study, INSULIN-RESISTANCE, Multidisciplinary, Agricultural and Biological Sciences(all), LARGE-SCALE ASSOCIATION, STAGE RENAL-DISEASE, COMMON VARIANTS, Genomics, Medicine, Research Article, SUSCEPTIBILITY LOCI, General Science & Technology, Population, Single-nucleotide polymorphism, Biology, DIAGRAM Consortium, 03 medical and health sciences, MAGIC Investigators, SDG 3 - Good Health and Well-being, Genetic linkage, MD Multidisciplinary, Genome-Wide Association Studies, SNP, ddc:610, education, Genotyping, 030304 developmental biology, Diabetic Endocrinology, LINKAGE ANALYSIS, Biochemistry, Genetics and Molecular Biology(all), lcsh:R, TCF7L2 GENE, Case-control study, Computational Biology, nutritional and metabolic diseases, Human Genetics, Diabetes Mellitus Type 2, Stage renal-disease, large-scale association, Susceptibility loci, Insulin-resistance, Fasting glucose, Tissue factor, Homeodomain protein, Linkage analysis, Common variants, TCF7L2 gene, Black or African American, Diabetes Mellitus, Type 2, TISSUE FACTOR, Genetics of Disease, HOMEODOMAIN PROTEIN, Genetic Polymorphism, lcsh:Q, Genome Expression Analysis, 030217 neurology & neurosurgery, Population Genetics, FASTING GLUCOSE
Abstract

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

Published
2012

12. Comparison of Illumina and 454 Deep Sequencing in Participants Failing Raltegravir-Based Antiretroviral Therapy.

Author

Li, Jonathan Z., Chapman, Brad, Charlebois, Patrick, Hofmann, Oliver, Weiner, Brian, Porter, Alyssa J., Samuel, Reshmi, Vardhanabhuti, Saran, Zheng, Lu, Eron, Joseph, Taiwo, Babafemi, Zody, Michael C., Henn, Matthew R., Kuritzkes, Daniel R., Hide, Winston, Wilson, Cara C., Berzins, Baiba I., Acosta, Edward P., Bastow, Barbara, and Kim, Peter S.

Subjects
RALTEGRAVIR, ANTIRETROVIRAL agents, DRUG resistance in microorganisms, HIV, STATISTICAL correlation, NUCLEOTIDE sequence, COMPUTATIONAL biology
Abstract

Background: The impact of raltegravir-resistant HIV-1 minority variants (MVs) on raltegravir treatment failure is unknown. Illumina sequencing offers greater throughput than 454, but sequence analysis tools for viral sequencing are needed. We evaluated Illumina and 454 for the detection of HIV-1 raltegravir-resistant MVs. Methods: A5262 was a single-arm study of raltegravir and darunavir/ritonavir in treatment-naïve patients. Pre-treatment plasma was obtained from 5 participants with raltegravir resistance at the time of virologic failure. A control library was created by pooling integrase clones at predefined proportions. Multiplexed sequencing was performed with Illumina and 454 platforms at comparable costs. Illumina sequence analysis was performed with the novel snp-assess tool and 454 sequencing was analyzed with V-Phaser. Results: Illumina sequencing resulted in significantly higher sequence coverage and a 0.095% limit of detection. Illumina accurately detected all MVs in the control library at ≥0.5% and 7/10 MVs expected at 0.1%. 454 sequencing failed to detect any MVs at 0.1% with 5 false positive calls. For MVs detected in the patient samples by both 454 and Illumina, the correlation in the detected variant frequencies was high (R2 = 0.92, P<0.001). Illumina sequencing detected 2.4-fold greater nucleotide MVs and 2.9-fold greater amino acid MVs compared to 454. The only raltegravir-resistant MV detected was an E138K mutation in one participant by Illumina sequencing, but not by 454. Conclusions: In participants of A5262 with raltegravir resistance at virologic failure, baseline raltegravir-resistant MVs were rarely detected. At comparable costs to 454 sequencing, Illumina demonstrated greater depth of coverage, increased sensitivity for detecting HIV MVs, and fewer false positive variant calls. [ABSTRACT FROM AUTHOR]

Published
2014
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13. Integrating Murine Gene Expression Studies to Understand Obstructive Lung Disease Due to Chronic Inhaled Endotoxin

Author

Lai, Peggy S., Hofmann, Oliver, Baron, Rebecca M., Cernadas, Manuela, Meng, Quanxin Ryan, Bresler, Herbert S., Brass, David M., Yang, Ivana V., Schwartz, David A., Christiani, David C., and Hide, Winston

Subjects
GENE expression, OBSTRUCTIVE lung diseases, ENDOTOXINS, ENVIRONMENTAL exposure, ASTHMA, PATHOLOGICAL physiology, LABORATORY mice
Abstract

Rationale: Endotoxin is a near ubiquitous environmental exposure that that has been associated with both asthma and chronic obstructive pulmonary disease (COPD). These obstructive lung diseases have a complex pathophysiology, making them difficult to study comprehensively in the context of endotoxin. Genome-wide gene expression studies have been used to identify a molecular snapshot of the response to environmental exposures. Identification of differentially expressed genes shared across all published murine models of chronic inhaled endotoxin will provide insight into the biology underlying endotoxin-associated lung disease. Methods: We identified three published murine models with gene expression profiling after repeated low-dose inhaled endotoxin. All array data from these experiments were re-analyzed, annotated consistently, and tested for shared genes found to be differentially expressed. Additional functional comparison was conducted by testing for significant enrichment of differentially expressed genes in known pathways. The importance of this gene signature in smoking-related lung disease was assessed using hierarchical clustering in an independent experiment where mice were exposed to endotoxin, smoke, and endotoxin plus smoke. Results: A 101-gene signature was detected in three murine models, more than expected by chance. The three model systems exhibit additional similarity beyond shared genes when compared at the pathway level, with increasing enrichment of inflammatory pathways associated with longer duration of endotoxin exposure. Genes and pathways important in both asthma and COPD were shared across all endotoxin models. Mice exposed to endotoxin, smoke, and smoke plus endotoxin were accurately classified with the endotoxin gene signature. Conclusions: Despite the differences in laboratory, duration of exposure, and strain of mouse used in three experimental models of chronic inhaled endotoxin, surprising similarities in gene expression were observed. The endotoxin component of tobacco smoke may play an important role in disease development. [ABSTRACT FROM AUTHOR]

Published
2013
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14. Big data: The future of biocuration.

Author

Howe, Doug, Costanzo, Maria, Fey, Petra, Gojobori, Takashi, Hannick, Linda, Hide, Winston, Hill, David P., Kania, Renate, Schaeffer, Mary, St Pierre, Susan, Twigger, Simon, White, Owen, and Yon Rhee, Seung

Subjects
WEBSITES, GENOMICS, GENOMES, GENETICS, MOLECULAR biology, COMPUTER network resources
Abstract

The article reviews several Web sites including www.genenames.org from HUGO Gene Nomenclature Committee, http://daphnia.cgb.indiana from Dapnia Genomics Consortium and http://iggi.sanbi.ac.za from Glossina Genomics Initiatives.

Published
2008
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15. Population Differences in Transcript-Regulator Expression Quantitative Trait Loci

Author

Bushel, Pierre R., McGovern, Ray, Liu, Liwen, Huda, Ahsan, Lu, Jun, Hofmann, Oliver Marc, Lin, Xihong, and Hide, Winston

Subjects
biology, computational biology, population genetics, genetics, heredity, genomics, population biology, systems biology, computer science, algorithms, statistics, mathematics
Abstract

Gene expression quantitative trait loci (eQTL) are useful for identifying single nucleotide polymorphisms (SNPs) associated with diseases. At times, a genetic variant may be associated with a master regulator involved in the manifestation of a disease. The downstream target genes of the master regulator are typically co-expressed and share biological function. Therefore, it is practical to screen for eQTLs by identifying SNPs associated with the targets of a transcript-regulator (TR). We used a multivariate regression with the gene expression of known targets of TRs and SNPs to identify TReQTLs in European (CEU) and African (YRI) HapMap populations. A nominal p-value of <1 x 10\(^{-6}\)revealed 234 SNPs in CEU and 154 in YRI as TReQTLs. These represent 36 independent (tag) SNPs in CEU and 39 in YRI affecting the downstream targets of 25 and 36 TRs respectively. At a false discovery rate (FDR) = 45%, one cis-acting tag SNP (within 1 kb of a gene) in each population was identified as a TReQTL. In CEU, the SNP (rs16858621) in Pcnxl2 was found to be associated with the genes regulated by CREM whereas in YRI, the SNP (rs16909324) was linked to the targets of miRNA hsa-miR-125a. To infer the pathways thatregulate expression, we ranked TReQTLs by connectivity within the structure of biological process subtrees. One TReQTL SNP (rs3790904) in CEU maps to Lphn2 and is associated (nominal p-value = 8.1 x 10\(^{-7}\)) with the targets of the X-linked breast cancer suppressor Foxp3. The structure of the biological process subtree and a gene interaction network of the TReQTL revealed that tumor necrosis factor, NF-kappaB and variants in G-protein coupled receptors signaling may play a central role as communicators in Foxp3 functional regulation. The potential pleiotropic effect of the Foxp3 TReQTLs was gleaned from integrating mRNA-Seq data and SNP-set enrichment into the analysis.

Published
2012
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16. ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level.

Author

Rocca-Serra, Philippe, Brandizi, Marco, Maguire, Eamonn, Sklyar, Nataliya, Taylor, Chris, Begley, Kimberly, Field, Dawn, Harris, Stephen, Hide, Winston, Hofmann, Oliver, Neumann, Steffen, Sterk, Peter, Tong, Weida, and Sansone, Susanna-Assunta

Subjects
OPEN source software, COMPUTER software, FREEWARE (Computer software), METADATA, GENOMICS
Abstract

Summary: The first open source software suite for experimentalists and curators that (i) assists in the annotation and local management of experimental metadata from high-throughput studies employing one or a combination of omics and other technologies; (ii) empowers users to uptake community-defined checklists and ontologies; and (iii) facilitates submission to international public repositories. [ABSTRACT FROM PUBLISHER]

Published
2010
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17. A case for a Glossina genome project

Author

Aksoy, Serap, Berriman, Matt, Hall, Neil, Hattori, Masahira, Hide, Winston, and Lehane, Michael J.

Subjects
*TSETSE-flies, *GENOMICS, *PATHOGENIC microorganisms, *BACTERIAL artificial chromosomes, *GENOMES
Abstract

Given the medical and agricultural significance of Glossina, knowledge of the genomic aspects of the vector and vector–pathogen interactions are a high priority. In preparation for a full genome sequence initiative, an extensive set of expressed sequence tags (ESTs) has been generated from tissue-specific normalized libraries. In addition, bacterial artificial chromosome (BAC) libraries are being constructed, and information on the genome structure and size from different species has been obtained. An international consortium is now in place to further efforts to lead to a full genome project. [Copyright &y& Elsevier]

Published
2005
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18. Transcriptomic signature of Leishmania infected mice macrophages: a metabolic point of view

Author

Elena Kutumova, Florian Noguier, Kais Ghedira, Oliver Hofmann, ROMAN BRUNO, Dhafer Laouini, Hanene Attia, Ralf Herwig, Winston Hide, Ruslan Sharipov, Christoph Wierling, Fabien Pierrat, Beatrice REGNAULT, Pierre-André Cazenave, Slimane Ben miled, Nicki Tiffin, Imen RABHI, David Piquemal, Fatma Zahra Guerfali, Institut Pasteur de Tunis, Réseau International des Instituts Pasteur (RIIP), Department Lehrach (Vertebrate Genomics Department), Max Planck Institute for Molecular Genetics (MPIMG), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Skuldtech, Génopole, Institut Pasteur [Paris] (IP), Institut Armand Frappier (INRS-IAF), Institut National de la Recherche Scientifique [Québec] (INRS)-Réseau International des Instituts Pasteur (RIIP), This study was supported by funds from a European community grant (Proposal number: 37231), the Canadian Institutes of Health Research (MOP-12933), and a fellowship from the Reseau international des Instituts Pasteur (to SR)., Sysco Consortium: Attia Hanène, Ben Miled Slimane, Benkahla Alia, Bruno Roman, Cazenave Pierre-André, Checkmeneva Elena, Dellagi Koussay, Gabdoulline Razif, Ghedira Kais, Guerfali Fatma Z., Gustin Cindy, Herwig Ralf, Hide Winston, Hofmann Oliver, Hornischer Klaus, Kel Alexander, Kiselev Ilya, Kolpakov Fedor, Kondrakhin Yuriy, Kutumova Elena, Land Sigrid, Laouini Dhafer, Lemaire Julien, Liebich Ines, Manchon Laurent, Matys Volker, Michael Holger, Mkannez Ghada, Noguier Florian, Pierrat Fabien, Renard Patricia, Ryabova Anna, Sandoval Ruy Jauregui, Schacherer Frank, Sghaier Rabiaa Manel, Sharipov Ruslan, Stegmaier Philip, Tiffin Nicki, Tolstykh Nikita, Valeev Tagir, Voss Nico, Wierling Christoph, Yevshin Ivan., The authors are grateful to Dr K. Dellagi who has initiated the Sysco project. We also wish to acknowledge Marcel Desrosiers for assistance with confocal microscopy., Institut Pasteur [Paris], Réseau International des Instituts Pasteur (RIIP)-Institut National de la Recherche Scientifique [Québec] (INRS), Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Skuldtech. Cap Delta - ZAC Euromedecine II. 1682, rue de la Valsière, and Génotypage des Eucaryotes (Plate-Forme)

Subjects
lcsh:Arctic medicine. Tropical medicine, lcsh:RC955-962, Microarrays, [SDV]Life Sciences [q-bio], Gene Expression, Microbiology, 03 medical and health sciences, Mice, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Lipid droplet, Molecular Cell Biology, Animals, Glycolysis, Leishmania major, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Anaerobiosis, Scavenger receptor, Biology, ComputingMilieux_MISCELLANEOUS, Inbred BALB C, 030304 developmental biology, Foam cell, 0303 health sciences, Mice, Inbred BALB C, biology, 030306 microbiology, lcsh:Public aspects of medicine, Gene Expression Profiling, Macrophages, Public Health, Environmental and Occupational Health, Computational Biology, Lipid metabolism, lcsh:RA1-1270, Genomics, biology.organism_classification, Lipid Metabolism, 3. Good health, Cell biology, Host-Pathogen Interaction, Metabolic pathway, Infectious Diseases, Biochemistry, Anaerobic glycolysis, Carbohydrate Metabolism, lipids (amino acids, peptides, and proteins), Metabolic Networks and Pathways, Research Article
Abstract

We analyzed the transcriptional signatures of mouse bone marrow-derived macrophages at different times after infection with promastigotes of the protozoan parasite Leishmania major. Ingenuity Pathway Analysis revealed that the macrophage metabolic pathways including carbohydrate and lipid metabolisms were among the most altered pathways at later time points of infection. Indeed, L. major promastiogtes induced increased mRNA levels of the glucose transporter and almost all of the genes associated with glycolysis and lactate dehydrogenase, suggesting a shift to anaerobic glycolysis. On the other hand, L. major promastigotes enhanced the expression of scavenger receptors involved in the uptake of Low-Density Lipoprotein (LDL), inhibited the expression of genes coding for proteins regulating cholesterol efflux, and induced the synthesis of triacylglycerides. These data suggested that Leishmania infection disturbs cholesterol and triglycerides homeostasis and may lead to cholesterol accumulation and foam cell formation. Using Filipin and Bodipy staining, we showed cholesterol and triglycerides accumulation in infected macrophages. Moreover, Bodipy-positive lipid droplets accumulated in close proximity to parasitophorous vacuoles, suggesting that intracellular L. major may take advantage of these organelles as high-energy substrate sources. While the effect of infection on cholesterol accumulation and lipid droplet formation was independent on parasite development, our data indicate that anaerobic glycolysis is actively induced by L. major during the establishment of infection., Author Summary Leishmania are obligated intracellular pathogens that develop almost exclusively in macrophages. Experimental leishmaniasis in mice is one of the most extensively studied models of intracellular infections both at the level of the parasite and host immune responses. We took advantage of Balb/c mice model to investigate gene expression profile through Affymetrix oligonucleotide arrays. In order to have a general and dynamic picture of the complex biological events that are acting in the context of Leishmania intracellular parasitism, we investigated the mouse macrophage response to initial invasion of L. major over a time course that extended from one to 24 hours post-infection. Our results reveal the alteration of several biological processes and metabolic changes. Indeed, similarly to different other pathogens, Leishmania induces cholesterol accumulation and foam cell formation that have been confirmed by confocal microscopy experiments. Whether Leishmania parasites take advantage of this high-energy source is now under investigation. Our findings provided further understandings in host responses to Leishmania infection.

Published
2012

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