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2017
Autores
Tavares, PC; Henriques, PR; Gomes, EF;
Publicação
PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION (CSEDU), VOL 1
Abstract
Motivate students is one of the biggest challenges that teachers have to face, in general and in particular in programming courses. In this article two techniques, aimed at supporting the teaching of programming, are discussed: program animation, and automatic evaluation of programs. Based on the combination of these techniques and their currently available tools, we will describe two possible approaches to increase motivation and improve the success. The conclusions of a first experiment conducted in the classroom will be presented. PEP, a Web-based tool that implements one of the approaches proposed, will be introduced.
2017
Autores
Silva, PRC; Dias, SM; Brandão, WC; Song, MA; Zárate, LE;
Publicação
Proceedings of the 19th International Conference on Enterprise Information Systems
Abstract
2017
Autores
Yang F.; Wang J.; Pierce B.L.; Chen L.S.; Aguet F.; Ardlie K.G.; Cummings B.B.; Gelfand E.T.; Getz G.; Hadley K.; Handsaker R.E.; Huang K.H.; Kashin S.; Karczewski K.J.; Lek M.; Li X.; MacArthur D.G.; Nedzel J.L.; Nguyen D.T.; Noble M.S.; Segrè A.V.; Trowbridge C.A.; Tukiainen T.; Abell N.S.; Balliu B.; Barshir R.; Basha O.; Battle A.; Bogu G.K.; Brown A.; Brown C.D.; Castel S.E.; Chiang C.; Conrad D.F.; Cox N.J.; Damani F.N.; Davis J.R.; Delaneau O.; Dermitzakis E.T.; Engelhardt B.E.; Eskin E.; Ferreira P.G.; Frésard L.; Gamazon E.R.; Garrido-Martín D.; Gewirtz A.D.H.; Gliner G.; Gloudemans M.J.; Guigo R.; Hall I.M.; Han B.; He Y.; Hormozdiari F.; Howald C.; Im H.K.; Jo B.; Kang E.Y.; Kim Y.; Kim-Hellmuth S.; Lappalainen T.; Li G.; Li X.; Liu B.; Mangul S.; McCarthy M.I.; McDowell I.C.; Mohammadi P.; Monlong J.; Montgomery S.B.; Muñoz-Aguirre M.; Ndungu A.W.; Nicolae D.L.; Nobel A.B.; Oliva M.; Ongen H.; Palowitch J.J.; Panousis N.; Papasaikas P.; Park Y.S.; Parsana P.; Payne A.J.; Peterson C.B.; Quan J.; Reverter F.; Sabatti C.; Saha A.; Sammeth M.; Scott A.J.; Shabalin A.A.; Sodaei R.; Stephens M.; Stranger B.E.; Strober B.J.; Sul J.H.; Tsang E.K.; Urbut S.; van de Bunt M.; Wang G.; Wen X.; Wright F.A.;
Publicação
Genome Research
Abstract
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.
2017
Autores
Saha A.; Kim Y.; Gewirtz A.D.H.; Jo B.; Gao C.; McDowell I.C.; Engelhardt B.E.; Battle A.; Aguet F.; Ardlie K.G.; Cummings B.B.; Gelfand E.T.; Getz G.; Hadley K.; Handsaker R.E.; Huang K.H.; Kashin S.; Karczewski K.J.; Lek M.; Li X.; MacArthur D.G.; Nedzel J.L.; Nguyen D.T.; Noble M.S.; Segrè A.V.; Trowbridge C.A.; Tukiainen T.; Abell N.S.; Balliu B.; Barshir R.; Basha O.; Battle A.; Bogu G.K.; Brown A.; Brown C.D.; Castel S.E.; Chen L.S.; Chiang C.; Conrad D.F.; Cox N.J.; Damani F.N.; Davis J.R.; Delaneau O.; Dermitzakis E.T.; Engelhardt B.E.; Eskin E.; Ferreira P.G.; Frésard L.; Gamazon E.R.; Garrido-Martín D.; Gliner G.; Gloudemans M.J.; Guigo R.; Hall I.M.; Han B.; He Y.; Hormozdiari F.; Howald C.; Im H.K.; Kang E.Y.; Kim-Hellmuth S.; Lappalainen T.; Li G.; Li X.; Liu B.; Mangul S.; McCarthy M.I.; Mohammadi P.; Monlong J.; Montgomery S.B.; Muñoz-Aguirre M.; Ndungu A.W.; Nicolae D.L.; Nobel A.B.; Oliva M.; Ongen H.; Palowitch J.J.; Panousis N.; Papasaikas P.; Park Y.S.; Parsana P.; Payne A.J.; Peterson C.B.; Quan J.; Reverter F.; Sabatti C.; Sammeth M.; Scott A.J.; Shabalin A.A.; Sodaei R.; Stephens M.; Stranger B.E.; Strober B.J.; Sul J.H.; Tsang E.K.; Urbut S.; van de Bunt M.; Wang G.; Wen X.; Wright F.A.;
Publicação
Genome Research
Abstract
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
2017
Autores
Aguet, F; Brown, AA; Castel, SE; Davis, JR; He, Y; Jo, B; Mohammadi, P; Park, Y; Parsana, P; Segre, AV; Strober, BJ; Zappala, Z; Cummings, BB; Gelfand, ET; Hadley, K; Huang, KH; Lek, M; Li, X; Nedzel, JL; Nguyen, DY; Noble, MS; Sullivan, TJ; Tukiainen, T; MacArthur, DG; Getz, G; Management, NP; Addington, A; Guan, P; Koester, S; Little, AR; Lockhart, NC; Moore, HM; Rao, A; Struewing, JP; Volpi, S; Collection, B; 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, D; Mash, DC; Davis, DA; Sobin, L; Barcus, ME; Branton, PA; Grp, EMW; Abell, NS; Balliu, B; Delaneau, O; Fresard, L; Gamazon, ER; Garrido Martin, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Han, B; He, AZ; Hormozdiari, F; Li, X; Liu, B; Kang, EY; McDowell, IC; Ongen, H; Palowitch, JJ; Peterson, CB; Quon, G; Ripke, S; Saha, A; Shabalin, AA; Shimko, TC; Sul, JH; Teran, NA; Tsang, EK; Zhang, H; Zhou, YH; Bustamante, CD; Cox, NJ; Guigo, R; Kellis, M; McCarthy, MI; Conrad, DF; Eskin, E; Li, G; Nobel, AB; Sabatti, C; Stranger, BE; Wen, X; Wright, FA; Ardlie, KG; Dermitzakis, ET; Lappalainen, T; Battle, A; Brown, CD; Engelhardt, BE; Montgomery, SB; 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; Segre, 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; Cox, NJ; Damani, FN; Davis, JR; Delaneau, O; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Ferreira, PG; Fresard, L; Gamazon, ER; Garrido Martin, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Guigo, R; Hall, IM; Han, B; He, Y; Hormozdiari, F; Howald, C; Im, HK; 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; Munoz Aguirre, M; Ndungu, AW; Nicolae, DL; 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; De Bunt, MV; Wang, G; Wen, X; Wright, FA; Xi, HS; Yeger Lotem, E; Zappala, Z; Zaugg, JB; Zhou, YH; Akey, JM; Bates, D; Chan, J; Chen, LS; 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; Li, X; Lin, J; Lin, S; Linder, S; Linke, C; Liu, Y; Maurano, MT; Molinie, B; Montgomery, SB; Nelson, J; Neri, FJ; Oliva, M; Park, Y; Pierce, BL; Rinaldi, NJ; Rizzardi, LF; Sandstrom, R; Skol, A; Smith, KS; Snyder, MP; Stamatoyannopoulos, J; Stranger, BE; Tang, H; Tsang, EK; Wang, L; Wang, M; Van Wittenberghe, N; Wu, F; Zhang, R; Fund, NC; Nierras, CR; Nci, N; Branton, PA; Carithers, LJ; Guan, P; Moore, HM; Rao, A; Vaught, JB; Nhgri, N; Gould, SE; Lockart, NC; Martin, C; Struewing, JP; Volpi, S; Nimh, N; Addington, AM; Koester, SE; Nida, N; 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; Study, E; 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;
Publicação
NATURE
Abstract
Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
2017
Autores
Li, X; Kim, Y; Tsang, EK; Davis, JR; Damani, FN; Chiang, C; Hess, GT; Zappala, Z; Strober, BJ; Scott, AJ; Li, A; Ganna, A; Bassik, MC; Merker, JD; 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; Cox, NJ; Damani, FN; Davis, JR; Delaneau, O; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Ferreira, PG; Frésard, L; Gamazon, ER; Garrido-Martín, D; Gewirtz, AD; Gliner, G; Gloudemans, MJ; Guigo, R; Hall, IM; Han, B; He, Y; Hormozdiari, F; Howald, C; Kyung Im, H; Jo, B; Yong Kang, E; 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; Nicolae, DL; 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, Y; Akey, JM; Bates, D; Chan, J; Chen, LS; 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; Billy Li, J; Li, Q; Li, X; Lin, J; Lin, S; Linder, S; Linke, C; Liu, Y; Maurano, MT; Molinie, B; Montgomery, SB; Nelson, J; Neri, FJ; Oliva, M; Park, Y; Pierce, BL; Rinaldi, NJ; Rizzardi, LF; Sandstrom, R; Skol, A; Smith, KS; Snyder, MP; Stamatoyannopoulos, J; Stranger, BE; Tang, H; Tsang, EK; 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; Hall, IM; Battle, A; Montgomery, SB;
Publicação
Nature
Abstract
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1-4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8-11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.
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