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2020
Autores
Moreno, M; Sousa, A; Melé, M; Oliveira, R; G Ferreira, P;
Publicação
Proceedings
Abstract
2020
Autores
Jiang, L; Wang, M; Lin, S; Jian, R; Li, X; Chan, J; Dong, G; Fang, H; Robinson, AE; Snyder, MP; Aguet, F; Anand, S; Ardlie, KG; Gabriel, S; Getz, G; Graubert, A; Hadley, K; Handsaker, RE; Huang, KH; Kashin, S; MacArthur, DG; Meier, SR; Nedzel, JL; Nguyen, DY; Segrè, AV; Todres, E; Balliu, B; Barbeira, AN; Battle, A; Bonazzola, R; Brown, A; Brown, CD; Castel, SE; Conrad, D; Cotter, DJ; Cox, N; Das, S; de Goede, OM; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Eulalio, TY; Ferraro, NM; Flynn, E; Fresard, L; Gamazon, ER; Garrido-Martín, D; Gay, NR; Guigó, R; Hamel, AR; He, Y; Hoffman, PJ; Hormozdiari, F; Hou, L; Im, HK; Jo, B; Kasela, S; Kellis, M; Kim-Hellmuth, S; Kwong, A; Lappalainen, T; Li, X; Liang, Y; Mangul, S; Mohammadi, P; Montgomery, SB; Muñoz-Aguirre, M; Nachun, DC; Nobel, AB; Oliva, M; Park, Y; Park, Y; Parsana, P; Reverter, F; Rouhana, JM; Sabatti, C; Saha, A; Skol, AD; Stephens, M; Stranger, BE; Strober, BJ; Teran, NA; Viñuela, A; Wang, G; Wen, X; Wright, F; Wucher, V; Zou, Y; Ferreira, PG; Li, G; Melé, M; Yeger-Lotem, E; Barcus, ME; Bradbury, D; Krubit, T; McLean, JA; Qi, L; Robinson, K; Roche, NV; Smith, AM; Sobin, L; Tabor, DE; Undale, A; Bridge, J; Brigham, LE; Foster, BA; Gillard, BM; Hasz, R; Hunter, M; Johns, C; Johnson, M; Karasik, E; Kopen, G; Leinweber, WF; McDonald, A; Moser, MT; Myer, K; Ramsey, KD; Roe, B; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Jewell, SD; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Branton, PA; Barker, LK; Gardiner, HM; Mosavel, M; Siminoff, LA; Flicek, P; Haeussler, M; Juettemann, T; Kent, WJ; Lee, CM; Powell, CC; Rosenbloom, KR; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Abell, NS; Akey, J; Chen, L; Demanelis, K; Doherty, JA; Feinberg, AP; Hansen, KD; Hickey, PF; Jasmine, F; Kaul, R; Kibriya, MG; Li, JB; Li, Q; Linder, SE; Pierce, BL; Rizzardi, LF; Smith, KS; Stamatoyannopoulos, J; Tang, H; Carithers, LJ; Guan, P; Koester, SE; Little, AR; Moore, HM; Nierras, CR; Rao, AK; Vaught, JB; Volpi, S;
Publicação
Cell
Abstract
Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.
2020
Autores
Aguet, F; Barbeira, AN; Bonazzola, R; Brown, A; Castel, SE; Jo, B; Kasela, S; Kim Hellmuth, S; Liang, Y; Oliva, M; Flynn, ED; Parsana, P; Fresard, L; Gamazon, ER; Hamel, AR; He, Y; Hormozdiari, F; Mohammadi, P; Muñoz Aguirre, M; Park, Y; Saha, A; Segrè, AV; Strober, BJ; Wen, X; Wucher, V; Ardlie, KG; Battle, A; Brown, CD; Cox, N; Das, S; Dermitzakis, ET; Engelhardt, BE; Garrido Martín, D; Gay, NR; Getz, GA; Guigó, R; Handsaker, RE; Hoffman, PJ; Im, HK; Kashin, S; Kwong, A; Lappalainen, T; Li, X; MacArthur, DG; Montgomery, SB; Rouhana, JM; Stephens, M; Stranger, BE; Todres, E; Viñuela, A; Wang, G; Zou, Y; Anand, S; Gabriel, S; Graubert, A; Hadley, K; Huang, KH; Meier, SR; Nedzel, JL; Nguyen, DT; Balliu, B; Conrad, DF; Cotter, DJ; deGoede, OM; Einson, J; Eskin, E; Eulalio, TY; Ferraro, NM; Gloudemans, MJ; Hou, L; Kellis, M; Li, X; Mangul, S; Nachun, DC; Nobel, AB; Park, Y; Rao, AS; Reverter, F; Sabatti, C; Skol, AD; Teran, NA; Wright, F; Ferreira, PG; Li, G; Melé, M; Yeger Lotem, E; Barcus, ME; Bradbury, D; Krubit, T; McLean, JA; Qi, L; Robinson, K; Roche, NV; Smith, AM; Sobin, L; Tabor, DE; Undale, A; Bridge, J; Brigham, LE; Foster, BA; Gillard, BM; Hasz, R; Hunter, M; Johns, C; Johnson, M; Karasik, E; Kopen, G; Leinweber, WF; McDonald, A; Moser, MT; Myer, K; Ramsey, KD; Roe, B; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Jewell, SD; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Branton, PA; Barker, LK; Gardiner, HM; Mosavel, M; Siminoff, LA; Flicek, P; Haeussler, M; Juettemann, T; Kent, WJ; Lee, CM; Powell, CC; Rosenbloom, KR; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Abell, NS; Akey, J; Chen, L; Demanelis, K; Doherty, JA; Feinberg, AP; Hansen, KD; Hickey, PF; Jasmine, F; Jiang, L; Kaul, R; Kibriya, MG; Li, JB; Li, Q; Lin, S; Linder, SE; Pierce, BL; Rizzardi, LF; Smith, KS; Snyder, M; Stamatoyannopoulos, J; Tang, H; Wang, M; Carithers, LJ; Guan, P; Koester, SE; Little, AR; Moore, HM; Nierras, CR; Rao, AK; Vaught, JB; Volpi, S;
Publicação
Science
Abstract
INTRODUCTION: The human genome contains tens of thousands of rare (minor allele frequency <1%) variants, some of which contribute to disease risk. Using 838 samples with whole-genome and multitissue transcriptome sequencing data in the Genotype-Tissue Expression (GTEx) project version 8, we assessed how rare genetic variants contribute to extreme patterns in gene expression (eOutliers), allelic expression (aseOutliers), and alternative splicing (sOutliers). We integrated these three signals across 49 tissues with genomic annotations to prioritize high-impact rare variants (RVs) that associate with human traits. RATIONALE: Outlier gene expression aids in identifying functional RVs. Transcriptome sequencing provides diverse measurements beyond gene expression, including allele-specific expression and alternative splicing, which can provide additional insight into RV functional effects. RESULTS: After identifying multitissue eOutliers, aseOutliers, and sOutliers, we found that outlier individuals of each type were significantly more likely to carry an RV near the corresponding gene. Among eOutliers, we observed strong enrichment of rare structural variants. sOutliers were particularly enriched for RVs that disrupted or created a splicing consensus sequence. aseOutliers provided the strongest enrichment signal when evaluated from just a single tissue. We developed Watershed, a probabilistic model for personal genome interpretation that improves over standard genomic annotation–based methods for scoring RVs by integrating these three transcriptomic signals from the same individual and replicates in an independent cohort. To assess whether outlier RVs identified in GTEx associate with traits, we evaluated these variants for association with diverse traits in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. We found that transcriptome-assisted prioritization identified RVs with larger trait effect sizes and were better predictors of effect size than genomic annotation alone. CONCLUSION: With >800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics.
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.
2020
Autores
Leite, A; Silva, ME; Rocha, AP;
Publicação
2020 11TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO): COMPUTATION AND MODELLING IN PHYSIOLOGY NEW CHALLENGES AND OPPORTUNITIES
Abstract
This work focus on detection of diseases from Heart Rate Variability (HRV) series using Long Short-Term Memory (LSTM) networks. First, non-linear models are used to extract sequences of features that characterize the HRV series. These time sequences are then used as input for the LSTM. HRV recordings from the Noltisalis database are used for training and testing this approach. The results indicate that the procedure provides accuracy scores in the range of 86.7% to 90.0% on the test set.
2020
Autores
Martins, A; Amado, C; Rocha, AP; Silva, ME; Pernice, R; Javorka, M; Faes, L;
Publicação
2020 11TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO): COMPUTATION AND MODELLING IN PHYSIOLOGY NEW CHALLENGES AND OPPORTUNITIES
Abstract
Cardiovascular variability is the result of the activity of several physiological control mechanisms, which involve different variables and operate across multiple time scales encompassing short term dynamics and long range correlations. This study presents a new approach to assess the multiscale complexity of multivariate time series, based on linear parametric models incorporating autoregressive coefficients and fractional integration. The approach extends to the multivariate case recent works introducing a linear parametric representation of multiscale entropy, and is exploited to assess the complexity of cardiovascular and respiratory time series in healthy subjects studied during postural and mental stress.
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