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2020
Autores
Castel S.E.; Aguet F.; Aguet F.; Aguet F.; Mohammadi P.; Mohammadi P.; Anand S.; Anand S.; Ardlie K.G.; Ardlie K.G.; Gabriel S.; Getz G.A.; Graubert A.; Graubert A.; Hadley K.; Hadley K.; Handsaker R.E.; Handsaker R.E.; Huang K.H.; Kashin S.; Kashin S.; Li X.; MacArthur D.G.; Meier S.R.; Meier S.R.; Nedzel J.L.; Nedzel J.L.; Nguyen D.T.; Segrè A.V.; Todres E.; Todres E.; Balliu B.; Barbeira A.N.; Battle A.; Bonazzola R.; Brown A.; Brown C.D.; Castel S.E.; Conrad D.F.; Cotter D.J.; Cox N.; Das S.; De Goede O.M.; Dermitzakis E.T.; Einson J.; Engelhardt B.E.; Eskin E.; Eulalio T.Y.; Ferraro N.M.; Flynn E.D.; Fresard L.; Gamazon E.R.; Garrido-Martín D.; Gay N.R.; Gloudemans M.J.; Guigó R.; Hame A.R.; He Y.; Hoffman P.J.; Hormozdiari F.; Hou L.; Huang K.H.; Im H.K.; Jo B.; Kasela S.; Kellis M.; Kim-Hellmuth S.; Kwong A.; Lappalainen T.; Li X.; Li X.; Liang Y.; Mangul S.; Montgomery S.B.; Muñoz-Aguirre M.; Nachun D.C.; Nguyen D.T.; Nobel A.B.; Oliva M.; Park Y.S.; Park Y.; Parsana P.; Rao A.S.; Reverter F.; Rouhana J.M.; Sabatti C.; Saha A.; Segrè A.V.; Skol A.D.; Stephens M.; Stranger B.E.; Strober B.J.; Teran N.A.; Viñuela A.; Wang G.; Wen X.; Wright F.; Wucher V.; Zou Y.; Ferreira P.G.;
Publicação
Genome Biology
Abstract
Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.
2018
Autores
Rocha, M; Ferreira, PG;
Publicação
Bioinformatics Algorithms: Design and Implementation in Python
Abstract
Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications.
2021
Autores
Schüle, R; Timmann, D; Erasmus, CE; Reichbauer, J; Wayand, M; Baets, J; Balicza, P; Chinnery, P; Dürr, A; Haack, T; Hengel, H; Horvath, R; Houlden, H; Kamsteeg, EJ; Kamsteeg, C; Lohmann, K; Macaya, A; Marcé Grau, A; Maver, A; Molnar, J; Münchau, A; Peterlin, B; Riess, O; Schöls, L; Schüle, R; Stevanin, G; Synofzik, M; Timmerman, V; van de Warrenburg, B; van Os, N; Vandrovcova, J; Wayand, M; Wilke, C; van de Warrenburg, B; Schöls, L; Wilke, C; Bevot, A; Zuchner, S; Beltran, S; Laurie, S; Matalonga, L; Graessner, H; Synofzik, M; Graessner, H; Zurek, B; Ellwanger, K; Ossowski, S; Demidov, G; Sturm, M; Schulze Hentrich, JM; Heutink, P; Brunner, H; Scheffer, H; Hoogerbrugge, N; Hoischen, A; ’t Hoen, PAC; Vissers, LELM; Gilissen, C; Steyaert, W; Sablauskas, K; de Voer, RM; Janssen, E; de Boer, E; Steehouwer, M; Yaldiz, B; Kleefstra, T; Brookes, AJ; Veal, C; Gibson, S; Wadsley, M; Mehtarizadeh, M; Riaz, U; Warren, G; Dizjikan, FY; Shorter, T; Töpf, A; Straub, V; Bettolo, CM; Specht, S; Clayton Smith, J; Banka, S; Alexander, E; Jackson, A; Faivre, L; Thauvin, C; Vitobello, A; Denommé Pichon, AS; Duffourd, Y; Tisserant, E; Bruel, AL; Peyron, C; Pélissier, A; Beltran, S; Gut, IG; Laurie, S; Piscia, D; Matalonga, L; Papakonstantinou, A; Bullich, G; Corvo, A; Garcia, C; Fernandez Callejo, M; Hernández, C; Picó, D; Paramonov, I; Lochmüller, H; Gumus, G; Bros Facer, V; Rath, A; Hanauer, M; Olry, A; Lagorce, D; Havrylenko, S; Izem, K; Rigour, F; Durr, A; Davoine, CS; Guillot Noel, L; Heinzmann, A; Coarelli, G; Bonne, G; Evangelista, T; Allamand, V; Nelson, I; Yaou, RB; Metay, C; Eymard, B; Cohen, E; Atalaia, A; Stojkovic, T; Macek, M; Turnovec, M; Thomasová, D; Kremliková, RP; Franková, V; Havlovicová, M; Kremlik, V; Parkinson, H; Keane, T; Spalding, D; Senf, A; Robinson, P; Danis, D; Robert, G; Costa, A; Patch, C; Hanna, M; Houlden, H; Reilly, M; Vandrovcova, J; Muntoni, F; Zaharieva, I; Sarkozy, A; de Jonghe, P; Nigro, V; Banfi, S; Torella, A; Musacchia, F; Piluso, G; Ferlini, A; Selvatici, R; Rossi, R; Neri, M; Aretz, S; Spier, I; Sommer, AK; Peters, S; Oliveira, C; Pelaez, JG; Matos, AR; José, CS; Ferreira, M; Gullo, I; Fernandes, S; Garrido, L; Ferreira, P; Carneiro, F; Swertz, MA; Johansson, L; van der Velde, JK; van der Vries, G; Neerincx, PB; Roelofs Prins, D; Köhler, S; Metcalfe, A; Verloes, A; Drunat, S; Rooryck, C; Trimouille, A; Castello, R; Morleo, M; Pinelli, M; Varavallo, A; De la Paz, MP; Sánchez, EB; Martín, EL; Delgado, BM; de la Rosa, FJAG; Ciolfi, A; Dallapiccola, B; Pizzi, S; Radio, FC; Tartaglia, M; Renieri, A; Benetti, E; Balicza, P; Molnar, MJ; Maver, A; Peterlin, B; Münchau, A; Lohmann, K; Herzog, R; Pauly, M; Macaya, A; Marcé Grau, A; Osorio, AN; de Benito, DN; Lochmüller, H; Thompson, R; Polavarapu, K; Beeson, D; Cossins, J; Cruz, PMR; Hackman, P; Johari, M; Savarese, M; Udd, B; Horvath, R; Capella, G; Valle, L; Holinski Feder, E; Laner, A; Steinke Lange, V; Schröck, E; Rump, A;
Publicação
European Journal of Human Genetics
Abstract
In the original publication of the article, consortium author lists were missing in the article. © 2021, The Author(s).
2018
Autores
Barbeira, AN; Dickinson, SP; Bonazzola, R; Zheng, J; Wheeler, HE; Torres, JM; Torstenson, ES; Shah, KP; Garcia, T; Edwards, TL; Stahl, EA; Huckins, LM; Aguet, F; Ardlie, KG; Cummings, BB; Gelfand, ET; Getz, G; Hadley, K; Handsaker, RE; Huang, KH; Kashin, S; Karczewski, KJ; Lek, M; Li, X; MacArthur, DG; Nedzel, JL; Nguyen, DT; Noble, MS; Segrè, AV; Trowbridge, CA; Tukiainen, T; Abell, NS; Balliu, B; Barshir, R; Basha, O; Battle, A; Bogu, GK; Brown, A; Brown, CD; Castel, SE; Chen, LS; Chiang, C; Conrad, DF; Damani, FN; Davis, JR; Delaneau, O; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Ferreira, PG; Frésard, L; Gamazon, ER; Garrido Martín, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Guigo, R; Hall, IM; Han, B; He, Y; Hormozdiari, F; Howald, C; Jo, B; Kang, EY; Kim, Y; Kim Hellmuth, S; Lappalainen, T; Li, G; Li, X; Liu, B; Mangul, S; McCarthy, MI; McDowell, IC; Mohammadi, P; Monlong, J; Montgomery, SB; Muñoz Aguirre, M; Ndungu, AW; Nobel, AB; Oliva, M; Ongen, H; Palowitch, JJ; Panousis, N; Papasaikas, P; Park, Y; Parsana, P; Payne, AJ; Peterson, CB; Quan, J; Reverter, F; Sabatti, C; Saha, A; Sammeth, M; Scott, AJ; Shabalin, AA; Sodaei, R; Stephens, M; Stranger, BE; Strober, BJ; Sul, JH; Tsang, EK; Urbut, S; Van De Bunt, M; Wang, G; Wen, X; Wright, FA; Xi, HS; Yeger Lotem, E; Zappala, Z; Zaugg, JB; Zhou, YH; Akey, JM; Bates, D; Chan, J; Claussnitzer, M; Demanelis, K; Diegel, M; Doherty, JA; Feinberg, AP; Fernando, MS; Halow, J; Hansen, KD; Haugen, E; Hickey, PF; Hou, L; Jasmine, F; Jian, R; Jiang, L; Johnson, A; Kaul, R; Kellis, M; Kibriya, MG; Lee, K; Li, JB; Li, Q; Lin, J; Lin, S; Linder, S; Linke, C; Liu, Y; Maurano, MT; Molinie, B; Nelson, J; Neri, FJ; Park, Y; Pierce, BL; Rinaldi, NJ; Rizzardi, LF; Sandstrom, R; Skol, A; Smith, KS; Snyder, MP; Stamatoyannopoulos, J; Tang, H; Wang, L; Wang, M; Van Wittenberghe, N; Wu, F; Zhang, R; Nierras, CR; Branton, PA; Carithers, LJ; Guan, P; Moore, HM; Rao, A; Vaught, JB; Gould, SE; Lockart, NC; Martin, C; Struewing, JP; Volpi, S; Addington, AM; Koester, SE; Little, AR; Brigham, LE; Hasz, R; Hunter, M; Johns, C; Johnson, M; Kopen, G; Leinweber, WF; Lonsdale, JT; McDonald, A; Mestichelli, B; Myer, K; Roe, B; Salvatore, M; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Bridge, J; Foster, BA; Gillard, BM; Karasik, E; Kumar, R; Miklos, M; Moser, MT; Jewell, SD; Montroy, RG; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Undale, AH; Smith, AM; Tabor, DE; Roche, NV; McLean, JA; Vatanian, N; Robinson, KL; Sobin, L; Barcus, ME; Valentino, KM; Qi, L; Hunter, S; Hariharan, P; Singh, S; Um, KS; Matose, T; Tomaszewski, MM; Barker, LK; Mosavel, M; Siminoff, LA; Traino, HM; Flicek, P; Juettemann, T; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Craft, B; Goldman, M; Haeussler, M; Kent, WJ; Lee, CM; Paten, B; Rosenbloom, KR; Vivian, J; Zhu, J; Nicolae, DL; Cox, NJ; Im, HK;
Publicação
Nature Communications
Abstract
Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes. © 2018 The Author(s).
2022
Autores
Moreno, M; Vilaca, R; Ferreira, PG;
Publicação
BMC BIOINFORMATICS
Abstract
Background: Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. Methods: In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. Results: This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https:// github. com/martaccmoreno/gexp-ml-dask. Conclusion: By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.
2025
Autores
Ferreira, M; José, CS; Almeida, F; Maqueda, J; Monteiro, R; Ferreira, P; Oliveira, C;
Publicação
MEDICINE
Abstract
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