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2017
Autores
Carvalho, S; Gueiral, N; Nogueira, E; Henrique, R; Oliveira, L; Tuchin, VV;
Publicação
DYNAMICS AND FLUCTUATIONS IN BIOMEDICAL PHOTONICS XIV
Abstract
Optical properties of biological tissues are unique and may be used for tissue identification, tissue discrimination or even to identify pathologies. Early stage colorectal cancer evolves from adenomatous polyps that arise in the inner layer of the colorectal tube - the mucosa. The identification of different optical properties between healthy and pathological colorectal tissues might be used to identify different tissue components and to develop an early stage diagnosis method using optical technologies. Since most of the biomedical optics techniques use light within the visible and near infrared wavelength ranges, we used the inverse adding-doubling method to make a fast estimation of the optical properties of colorectal mucosa and early stage adenocarcinoma between 400 and 1000 nm. The estimated wavelength dependencies have provided information about higher lipid content in healthy mucosa and higher blood content in pathological tissue. Such data has also indicated that the wavelength dependence of the scattering coefficient for healthy mucosa is dominated by Rayleigh scattering and for pathological mucosa it is dominated by Mie scattering. Such difference indicates smaller scatterer size in healthy mucosa tissue. Such information can now be used to develop new diagnosis or treatment methods for early cancer detection or removal. One possibility is to use optical clearing technique to improve tissue transparency and create localized and temporary tissue dehydration for image contrast improvement during diagnosis or polyp laser removal. Such techniques can now be developed based on the different results that we have found for healthy and pathological colorectal mucosa.
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.
2017
Autores
Tukiainen, T; Villani, A; Yen, A; Rivas, MA; Marshall, JL; Satija, R; Aguirre, M; Gauthier, L; Fleharty, M; Kirby, A; Cummings, BB; Castel, SE; Karczewski, KJ; Aguet, F; Byrnes, A; 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, ADH; 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; 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; 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; Lappalainen, T; Regev, A; Ardlie, KG; Hacohen, N; MacArthur, DG;
Publicação
Nature
Abstract
X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals1,2. The extent to which XCI is shared between cells and tissues remains poorly characterized3,4, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression5 and phenotypic traits6. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity6,7. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
2017
Autores
Nogueira, DM; Ferreira, CA; Jorge, AM;
Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)
Abstract
Phonocardiogram signals contain very useful information about the condition of the heart. It is a method of registration of heart sounds, which can be visually represented on a chart. By analyzing these signals, early detections and diagnosis of heart diseases can be done. Intelligent and automated analysis of the phonocardiogram is therefore very important, to determine whether the patient's heart works properly or should be referred to an expert for further evaluation. In this work, we use electrocardiograms and phonocardiograms collected simultaneously, from the Physionet challenge database, and we aim to determine whether a phonocardiogram corresponds to a "normal" or "abnormal" physiological state. The main idea is to translate a 1D phonocardiogram signal into a 2D image that represents temporal and Mel-frequency cepstral coefficients features. To do that, we develop a novel approach that uses both features. First we segment the phonocardiogram signals with an algorithm based on a logistic regression hidden semi-Markov model, which uses the electrocardiogram signals as reference. After that, we extract a group of features from the time and frequency domain (Mel-frequency cepstral coefficients) of the phonocardiogram. Then, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, we run a binary classifier to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, we study the contribution of temporal and Mel-frequency cepstral coefficients features and evaluate three classification algorithms: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when we map both temporal and Mel-frequency cepstral coefficients features into a 2D image and use the Support Vector Machines with a radial basis function kernel. Indeed, by including both temporal and Mel-frequency cepstral coefficients features, we obtain sligthly better results than the ones reported by the challenge participants, which use large amounts of data and high computational power.
2017
Autores
Tan, MH; Li, Q; Shanmugam, R; Piskol, R; Kohler, J; Young, AN; Liu, KI; Zhang, R; Ramaswami, G; Ariyoshi, K; Gupte, A; Keegan, LP; George, CX; Ramu, A; Huang, N; Pollina, EA; Leeman, DS; Rustighi, A; Goh, YPS; 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, ADH; 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; 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; 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; Chawla, A; Del Sal, G; Peltz, G; Brunet, A; Conrad, DF; Samuel, CE; O’Connell, MA; Walkley, CR; Nishikura, K; Li, JB;
Publicação
Nature
Abstract
Adenosine-to-inosine (A-to-I) RNA editing is a conserved posttranscriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules1. Although many editing sites have recently been discovered2-7, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood8-10. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of nonrepetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis-and trans-regulation of A-to-I editing.
2017
Autores
Cummings, BB; Marshall, JL; Tukiainen, T; Lek, M; Donkervoort, S; Foley, AR; Bolduc, V; Waddell, LB; Sandaradura, SA; O'Grady, GL; Estrella, E; Reddy, HM; Zhao, F; Weisburd, B; Karczewski, KJ; O'Donnell Luria, AH; Birnbaum, D; Sarkozy, A; Hu, Y; Gonorazky, H; Claeys, K; Joshi, H; Bournazos, A; Oates, EC; Ghaoui, R; Davis, MR; Laing, NG; Topf, A; Kang, PB; Beggs, AH; North, KN; Straub, V; Dowling, JJ; Muntoni, F; Clarke, NF; Cooper, ST; Bönnemann, CG; MacArthur, DG; Ardlie, KG; Getz, G; Gelfand, ET; Segrè, AV; Aguet, F; Sullivan, TJ; Li, X; Nedzel, JL; Trowbridge, CA; Hadley, K; Huang, KH; Noble, MS; Nguyen, DT; Nobel, AB; Wright, FA; Shabalin, AA; Palowitch, JJ; Zhou, YH; Dermitzakis, ET; McCarthy, MI; Payne, AJ; Lappalainen, T; Castel, S; Kim Hellmuth, S; Mohammadi, P; Battle, A; Parsana, P; Mostafavi, S; Brown, A; Ongen, H; Delaneau, O; Panousis, N; Howald, C; Van De Bunt, M; Guigo, R; Monlong, J; Reverter, F; Garrido, D; Munoz, M; Bogu, G; Sodaei, R; Papasaikas, P; Ndungu, AW; Montgomery, SB; Li, X; Fresard, L; Davis, JR; Tsang, EK; Zappala, Z; Abell, NS; Gloudemans, MJ; Liu, B; Damani, FN; Saha, A; Kim, Y; Strober, BJ; He, Y; Stephens, M; Pritchard, JK; Wen, X; Urbut, S; Cox, NJ; Nicolae, DL; Gamazon, ER; Im, HK; Brown, CD; Engelhardt, BE; Park, Y; Jo, B; McDowell, IC; Gewirtz, A; Gliner, G; Conrad, D; Hall, I; Chiang, C; Scott, A; Sabatti, C; Eskin, E; Peterson, C; Hormozdiari, F; Kang, EY; Mangul, S; Han, B; Sul, JH; Feinberg, AP; Rizzardi, LF; Hansen, KD; Hickey, P; Akey, J; Kellis, M; Li, JB; Snyder, M; Tang, H; Jiang, L; Lin, S; Stranger, BE; Fernando, M; Oliva, M; Stamatoyannopoulos, J; Kaul, R; Halow, J; Sandstrom, R; Haugen, E; Johnson, A; Lee, K; Bates, D; Diegel, M; Pierce, BL; Chen, L; Kibriya, MG; Jasmine, F; Doherty, J; Demanelis, K; Smith, KS; Li, Q; Zhang, R; Nierras, CR; Moore, HM; Rao, A; Guan, P; Vaught, JB; Branton, PA; Carithers, LJ; Volpi, S; Struewing, JP; Martin, CG; Nicole, LC; Koester, SE; Addington, AM; Little, AR; Leinweber, WF; Thomas, JA; Kopen, G; McDonald, A; Mestichelli, B; Shad, S; Lonsdale, JT; Salvatore, M; Hasz, R; Walters, G; Johnson, M; Washington, M; Brigham, LE; Johns, C; Wheeler, J; Roe, B; Hunter, M; Myer, K; Foster, BA; Moser, MT; Karasik, E; Gillard, BM; Kumar, R; Bridge, J; Miklos, M; Jewell, SD; Rohrer, DC; Valley, D; Montroy, RG; Mash, DC; Davis, DA; 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; Tomadzewski, MM; Siminoff, LA; Traino, HM; Mosavel, M; Barker, LK; Zerbino, DR; Juettmann, T; Taylor, K; Ruffier, M; Sheppard, D; Trevanion, S; Flicek, P; Kent, WJ; Rosenbloom, KR; Haeussler, M; Lee, CM; Paten, B; Vivan, J; Zhu, J; Goldman, M; Craft, B; Li, G; Ferreira, PG; Yeger Lotem, E; Maurano, MT; Barshir, R; Basha, O; Xi, HS; Quan, J; Sammeth, M; Zaugg, JB;
Publicação
Science Translational Medicine
Abstract
Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches. 2017 © The Authors.
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