M. D. Adams, J. M. Kelley, J. D. Gocayne, M. Dubnick, M. H. Polymeropoulos et al., Complementary DNA sequencing: expressed sequence tags and human genome project, Science, vol.42, issue.1, pp.2521651-1656, 1991.
DOI : 10.1016/S0092-8674(85)80099-4

R. Ahuja, T. Natarajan, K. R. Rao, and K. , Network Flows, 1993.
DOI : 10.21236/ADA594171

G. P. Alamancos, E. Agirre, and E. Eyras, Methods to Study Splicing from High-Throughput RNA Sequencing Data, Methods Mol. Biol, vol.1126, pp.357-397, 2014.
DOI : 10.1007/978-1-62703-980-2_26

URL : http://arxiv.org/pdf/1304.5952

S. Anders, A. Reyes, and W. Huber, Detecting differential usage of exons from RNA-seq data, Genome Research, vol.22, issue.10, pp.2008-2017, 2012.
DOI : 10.1101/gr.133744.111

G. Ast, How did alternative splicing evolve?, Nature Reviews Genetics, vol.13, issue.10, pp.773-782, 2004.
DOI : 10.1016/S0959-437X(96)80053-0

K. F. Au, H. Jiang, L. Lin, Y. Xing, and W. H. Wong, Detection of splice junctions from paired-end RNA-seq data by SpliceMap, Nucleic Acids Research, vol.38, issue.14, pp.384570-4578, 2010.
DOI : 10.1093/nar/gkq211

K. F. Au, V. Sebastiano, P. T. Afshar, J. D. Durruthy, L. Lee et al., Characterization of the human ESC transcriptome by hybrid sequencing, Proceedings of the National Academy of Sciences, vol.17, issue.6, pp.4821-4830, 2013.
DOI : 10.1101/gr.5650707

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with sparsityinducing penalties, Machine Learning, pp.1-106, 2012.
DOI : 10.1561/2200000015

URL : https://hal.archives-ouvertes.fr/hal-00613125

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Structured Sparsity through Convex Optimization, Statistical Science, vol.27, issue.4, pp.450-468, 2012.
DOI : 10.1214/12-STS394

URL : https://hal.archives-ouvertes.fr/hal-00621245

Y. Barash, J. A. Calarco, W. Gao, Q. Pan, X. Wang et al., Deciphering the splicing code, Nature, vol.27, issue.7294, pp.46553-59, 2010.
DOI : 10.1128/MCB.18.7.3900

N. L. Barbosa-morais, M. Irimia, Q. Pan, H. Y. Xiong, S. Gueroussov et al., The Evolutionary Landscape of Alternative Splicing in Vertebrate Species, Science, vol.20, issue.13, pp.3381587-1593, 2012.
DOI : 10.1093/bioinformatics/bth195

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

J. Behr, A. Kahles, Y. Zhong, V. T. Sreedharan, P. Drewe et al., MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples, Bioinformatics, vol.29, issue.20, pp.292529-2538, 2013.
DOI : 10.1093/bioinformatics/btt442

URL : https://academic.oup.com/bioinformatics/article-pdf/29/20/2529/16917355/btt442.pdf

N. J. Rourke, S. T. Ruediger, E. Rusman, R. M. Sanches-kuiper, M. R. Schenker et al., Accurate whole human genome sequencing using reversible terminator chemistry, J., West, J. S. Nature, issue.7218, pp.45653-59, 2008.

S. M. Berget, C. Moore, and P. A. Sharp, Spliced segments at the 5??? terminus of adenovirus 2 late mRNA, Proceedings of the National Academy of Sciences, vol.74, issue.8, pp.743171-3175, 1977.
DOI : 10.1016/0092-8674(76)90156-2

E. Bernard, L. Jacob, J. Mairal, and J. Vert, Efficient RNA isoform identification and quantification from RNA-Seq data with network flows, Bioinformatics, vol.30, issue.17, pp.302447-2455, 2014.
DOI : 10.1093/bioinformatics/btu317

URL : https://hal.archives-ouvertes.fr/hal-00803134

E. Bernard, L. Jacob, J. Mairal, E. Viara, and J. P. Vert, A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples, BMC Bioinformatics, vol.31, issue.1, p.262, 2015.
DOI : 10.1038/nbt.2450

URL : https://hal.archives-ouvertes.fr/hal-01123141

D. Bertsekas, Network Optimization: Continuous and Discrete Models.A t h e n aS c i e n - tific, 1998.

G. Biamonti, S. Bonomi, S. Gallo, and C. Ghigna, Making alternative splicing decisions during epithelial-to-mesenchymal transition (EMT), Cellular and Molecular Life Sciences, vol.9, issue.15, pp.692515-2526, 2012.
DOI : 10.1038/nrm2525

D. L. Black, Mechanisms of Alternative Pre-Messenger RNA Splicing, Annual Review of Biochemistry, vol.72, issue.1, pp.291-336, 2003.
DOI : 10.1146/annurev.biochem.72.121801.161720

URL : https://cloudfront.escholarship.org/dist/prd/content/qt2hg605wm/qt2hg605wm.pdf

B. J. Blencowe, Alternative Splicing: New Insights from Global Analyses, Cell, vol.126, issue.1, pp.37-47, 2006.
DOI : 10.1016/j.cell.2006.06.023

URL : https://doi.org/10.1016/j.cell.2006.06.023

B. J. Blencowe, S. Ahmad, L. , and L. J. , Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes, Genes & Development, vol.23, issue.12, pp.1379-1386, 2009.
DOI : 10.1101/gad.1788009

URL : http://genesdev.cshlp.org/content/23/12/1379.full.pdf

R. Bohnert and G. Rätsch, rQuant.web: a tool for RNA-Seq-based transcript quantitation, Nucleic Acids Research, vol.38, issue.Web Server, pp.348-351, 2010.
DOI : 10.1093/nar/gkq448

S. Bonnal, L. Vigevani, and J. Valcarcel, The spliceosome as a target of novel antitumour drugs, Nature Reviews Drug Discovery, vol.17, issue.11, pp.847-859, 2012.
DOI : 10.1002/chem.201002402

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

N. L. Bray, H. Pimentel, P. Melsted, and L. Pachter, Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology, vol.34, issue.5, pp.525-527, 2016.
DOI : 10.1093/bioinformatics/bts480

S. A. Byron, K. R. Van-keuren-jensen, D. M. Engelthaler, J. D. Carpten, C. et al., Translating RNA sequencing into clinical diagnostics: opportunities and challenges, Nature Reviews Genetics, vol.4, issue.5, 2016.
DOI : 10.3402/jev.v4.27494

S. Canzar, S. Andreotti, D. Weese, K. Reinert, and G. W. Klau, CIDANE: comprehensive isoform discovery and abundance estimation, Genome Biology, vol.33, issue.1, p.16, 2016.
DOI : 10.18637/jss.v033.i01

URL : https://hal.archives-ouvertes.fr/hal-01248849

L. Cartegni, S. L. Chew, and A. R. Krainer, LISTENING TO SILENCE AND UNDERSTANDING NONSENSE: EXONIC MUTATIONS THAT AFFECT SPLICING, Nature Reviews Genetics, vol.3, issue.4, pp.285-298, 2002.
DOI : 10.1038/nrg775

E. Celniker, L. A. Dillon, M. B. Gerstein, K. C. Gunsalus, S. Henikoff et al., Unlocking the secrets of the genome, Nature, issue.7249, pp.459927-930, 2009.

L. Chen, Statistical and Computational Methods for High-Throughput Sequencing Data Analysis of Alternative Splicing, Statistics in Biosciences, vol.17, issue.2, pp.138-155, 2013.
DOI : 10.1038/nsmb.1745

M. Chen and J. L. Manley, Mechanisms of alternative splicing regulation: insights from molecular and genomics approaches, Nature Reviews Molecular Cell Biology, vol.78, issue.11, pp.741-754, 2009.
DOI : 10.1128/MCB.12.7.3204

S. S. Chen, D. L. Donoho, and M. Saunders, Atomic decomposition by basis pursuit, 1998.
DOI : 10.1137/s1064827596304010

URL : http://www-stat.stanford.edu/~donoho/Reports/1995/30401.pdf

L. T. Chow, R. E. Gelinas, T. R. Broker, and R. J. Roberts, An amazing sequence arrangement at the 5??? ends of adenovirus 2 messenger RNA, Cell, vol.12, issue.1, pp.1-8, 1977.
DOI : 10.1016/0092-8674(77)90180-5

S. Clancy, RNA splicing: introns, exons and spliceosome, Nature Education, vol.1, issue.1, p.31, 2008.

S. Cleveland and S. Devlin, Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, Journal of the American Statistical Association, vol.41, issue.810345, pp.596-610, 1988.
DOI : 10.1007/BF02296972

M. Colombo, M. J. Blok, P. Whiley, M. Santamarina, S. Gutierrez-enriquez et al., Comprehensive annotation of splice junctions supports pervasive alternative splicing at the BRCA1 locus: a report from the ENIGMA consortium, Human Molecular Genetics, vol.23, issue.14, pp.3666-3680, 2014.
DOI : 10.1093/hmg/ddu075

H. G. Consortiuminternational, Finishing the euchromatic sequence of the human genome, Nature, issue.7011, pp.431931-945, 2004.

F. Cunningham, M. R. Amode, and D. Barrell, Ensembl 2015, Nucleic Acids Research, vol.43, issue.D1, pp.662-669, 2015.
DOI : 10.1093/nar/gku1010

URL : https://academic.oup.com/nar/article-pdf/43/D1/D662/7311412/gku1010.pdf

M. Danan-gotthold, R. Golan-gerstl, E. Eisenberg, K. Meir, R. Karni et al., Identification of recurrent regulated alternative splicing events across human solid tumors, Nucleic Acids Research, vol.43, issue.10, pp.435130-5144, 2015.
DOI : 10.1093/nar/gkv210

I. Daubechies, M. Defrise, D. Mol, and C. , An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.571413-1457, 2004.
DOI : 10.1002/0471221317

C. J. David and J. L. Manley, Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged, Genes & Development, vol.24, issue.21, pp.2343-2364, 2010.
DOI : 10.1101/gad.1973010

URL : http://genesdev.cshlp.org/content/24/21/2343.full.pdf

R. V. Davuluri, Y. Suzuki, S. Sugano, C. Plass, and T. H. Huang, The functional consequences of alternative promoter use in mammalian genomes, Trends in Genetics, vol.24, issue.4, pp.167-177, 2008.
DOI : 10.1016/j.tig.2008.01.008

M. De-la-hoya, O. Soukarieh, I. Lopez-perolio, A. Vega, L. C. Walker et al., Combined genetic and splicing analysis of BRCA1 c, 641A¿G] highlights the relevance of naturally occurring in-frame transcripts for developing disease gene variant classification algorithms, pp.594-596, 2016.

J. T. Den-dunnen and S. E. Antonarakis, Mutation nomenclature extensions and suggestions to describe complex mutations: A discussion, Human Mutation, vol.10, issue.1, pp.7-12, 2000.
DOI : 10.1038/ng0795-259

A. Dobin, A. Carrie, F. Schlesinger, J. Drenkow, C. Zaleski et al., STAR: ultrafast universal RNA-seq aligner, Bioinformatics, vol.29, issue.1, pp.15-21, 2013.
DOI : 10.1093/bioinformatics/bts635

URL : https://academic.oup.com/bioinformatics/article-pdf/29/1/15/17101697/bts635.pdf

A. G. Douglas and M. J. Wood, RNA splicing: disease and therapy, Briefings in Functional Genomics, vol.10, issue.3, pp.151-164, 2011.
DOI : 10.1093/bfgp/elr020

URL : https://academic.oup.com/bfg/article-pdf/10/3/151/524245/elr020.pdf

H. Dvinge and R. K. Bradley, Widespread intron retention diversifies most cancer transcriptomes, Genome Medicine, vol.96, issue.1, p.45, 2015.
DOI : 10.1073/pnas.96.26.14937

URL : https://genomemedicine.biomedcentral.com/track/pdf/10.1186/s13073-015-0168-9?site=genomemedicine.biomedcentral.com

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann. Stat, vol.32, issue.2, pp.407-499, 2004.

L. R. Ford and D. R. Fulkerson, Maximal flow through a network, Journal canadien de math??matiques, vol.8, issue.0, pp.399-404, 1956.
DOI : 10.4153/CJM-1956-045-5

M. Gabut, P. Samavarchi-tehrani, X. Wang, V. Slobodeniuc, D. O-'hanlon et al., An Alternative Splicing Switch Regulates Embryonic Stem Cell Pluripotency and Reprogramming, Cell, vol.147, issue.1, pp.132-146, 2011.
DOI : 10.1016/j.cell.2011.08.023

URL : https://doi.org/10.1016/j.cell.2011.08.023

M. Garber, M. G. Grabherr, M. Guttman, and C. Trapnell, Computational methods for transcriptome annotation and quantification using RNA-seq, Nature Methods, vol.19, issue.6, pp.469-477, 2011.
DOI : 10.1093/nar/gkq1015

D. Gautheret, O. Poirot, F. Lopez, S. Audic, C. et al., Alternate Polyadenylation in Human mRNAs: A Large-Scale Analysis by EST???Clustering, Genome Research, vol.15, issue.5, pp.524-530, 1998.
DOI : 10.1128/MCB.15.4.2219

C. Gawad, W. Koh, and S. R. Quake, Single-cell genome sequencing: current state of the science, Nature Reviews Genetics, vol.148, issue.3, pp.175-188, 2016.
DOI : 10.1038/onc.2013.29

P. Glaus, A. Honkela, and M. Rattray, Identifying differentially expressed transcripts from RNA-seq data with biological variation, Bioinformatics, vol.28, issue.13, pp.281721-1728, 2012.
DOI : 10.1093/bioinformatics/bts260

URL : https://academic.oup.com/bioinformatics/article-pdf/28/13/1721/16905223/bts260.pdf

A. V. Goldberg, An Efficient Implementation of a Scaling Minimum-Cost Flow Algorithm, Journal of Algorithms, vol.22, issue.1, 1997.
DOI : 10.1006/jagm.1995.0805

A. V. Goldberg and R. E. Tarjan, A new approach to the maximum-flow problem, J, 1988.

A. V. Goldberg and R. E. Tarjan, Finding Minimum-Cost Circulations by Successive Approximation, Mathematics of Operations Research, vol.15, issue.3, pp.430-466, 1990.
DOI : 10.1287/moor.15.3.430

URL : http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA192724&Location=U2&doc=GetTRDoc.pdf

S. Goodwin, J. D. Mcpherson, and W. R. Mccombie, Coming of age: ten years of next-generation sequencing technologies, Nature Reviews Genetics, vol.4, issue.6, pp.333-351, 2016.
DOI : 10.1111/j.1755-0998.2011.03024.x

M. G. Grabherr, B. J. Haas, M. Yassour, J. Z. Levin, D. A. Thompson et al., Full-length transcriptome assembly from RNA-Seq data without a reference genome, Nature Biotechnology, vol.30, issue.7, pp.644-652, 2011.

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571712/pdf

M. Guttman, M. Garber, J. Z. Levin, J. Donaghey, J. Robinson et al., Ab initio reconstruction of cell type???specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs, Nature Biotechnology, vol.10, issue.5, pp.503-510, 2010.
DOI : 10.1186/gb-2009-10-3-r25

T. Hartman, A. Hassidim, H. Kaplan, D. Raz, and M. Segalov, How to split a flow?, 2012 Proceedings IEEE INFOCOM, pp.828-836, 2012.
DOI : 10.1109/INFCOM.2012.6195830

M. L. Hastings and A. R. Krainer, Pre-mRNA splicing in the new millennium, Current Opinion in Cell Biology, vol.13, issue.3, pp.302-309, 2001.
DOI : 10.1016/S0955-0674(00)00212-X

K. E. Hayer, A. Pizarro, N. F. Lahens, J. B. Hogenesch, and G. R. Grant, Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data, Bioinformatics, issue.24, pp.313938-3945, 2015.

S. Heber, Splicing graphs and EST assembly problem, Bioinformatics, vol.18, issue.Suppl 1, pp.181-188, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S181

D. Hiller and W. H. Wong, Simultaneous Isoform Discovery and Quantification from RNA-Seq, Statistics in Biosciences, vol.27, issue.19, pp.100-118, 2013.
DOI : 10.1093/bioinformatics/btr449

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3718502/pdf

C. Houdayer, In Silico Prediction of Splice-Affecting Nucleotide Variants, Methods Mol. Biol, vol.760, pp.269-281, 2011.
DOI : 10.1007/978-1-61779-176-5_17

C. Houdayer, V. Caux-moncoutier, S. Krieger, M. Barrois, F. Bonnet et al., Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico, Hum. Mutat, issue.8, pp.331228-1238, 2012.

C. Houdayer, C. Dehainault, C. Mattler, D. Michaux, V. Caux-moncoutier et al., Evaluation of in silico splice tools for decision-making in molecular diagnosis, Human Mutation, vol.24, issue.7, pp.975-982, 2008.
DOI : 10.1002/humu.20765

B. E. Howard and S. Heber, Towards reliable isoform quantification using RNA-SEQ data, BMC Bioinformatics, vol.11, issue.Suppl 3, p.6, 2010.
DOI : 10.1186/1471-2105-11-S3-S6

. Soapsplice, Genome-Wide ab initio Detection of Splice Junctions from RNA-Seq Data, Front Genet, vol.2, p.46

W. Huber, J. Carey, V. Gentleman, R. Anders, S. Carlson et al., Orchestrating high-throughput genomic analysis with Bioconductor, Nature Methods, vol.46, issue.2, pp.115-121, 2015.
DOI : 10.1093/bioinformatics/btu168

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4509590/pdf

G. Jean, A. Kahles, V. T. Sreedharan, F. De-bona, R. et al., RNA-Seq Read Alignments with PALMapper, Curr Protoc Bioinformatics, vol.25, issue.11, 2010.
DOI : 10.1093/bioinformatics/btp120

URL : https://hal.archives-ouvertes.fr/hal-00909100

X. Jian, E. Boerwinkle, and X. Liu, In silico tools for splicing defect prediction: a survey from the viewpoint of end users, Genetics in Medicine, vol.8, issue.7, pp.497-503, 2014.
DOI : 10.1371/journal.pone.0057173

H. Jiang and W. H. Wong, Statistical inferences for isoform expression in RNA-Seq, Bioinformatics, vol.25, issue.8, pp.251026-1032, 2009.
DOI : 10.1093/bioinformatics/btp113

URL : https://academic.oup.com/bioinformatics/article-pdf/25/8/1026/16892810/btp113.pdf

J. M. Johnson, J. Castle, P. Garrett-engele, Z. Kan, P. M. Loerch et al., Genome-Wide Survey of Human Alternative Pre-mRNA Splicing with Exon Junction Microarrays, Science, vol.302, issue.5653, pp.3022141-2144, 2003.
DOI : 10.1126/science.1090100

A. Kalsotra and T. A. Cooper, Functional consequences of developmentally regulated alternative splicing, Nature Reviews Genetics, vol.234, issue.10, pp.715-729, 2011.
DOI : 10.1002/dvdy.20489

D. Karolchik, A. S. Hinrichs, and T. S. Furey, The UCSC Table Browser data retrieval tool, Nucleic Acids Research, vol.32, issue.90001, pp.32-493, 2004.
DOI : 10.1093/nar/gkh103

URL : https://academic.oup.com/nar/article-pdf/32/suppl_1/D493/7621914/gkh103.pdf

H. Keren, G. Lev-maor, and G. Ast, Alternative splicing and evolution: diversification, exon definition and function, Nature Reviews Genetics, vol.31, issue.5, pp.345-355, 2010.
DOI : 10.4161/cc.8.22.10289

P. V. Kharchenko, L. Silberstein, and D. T. Scadden, Bayesian approach to single-cell differential expression analysis, Nature Methods, vol.11, issue.7, pp.740-742, 2014.
DOI : 10.1038/nprot.2008.65

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112276/pdf

D. Kim, G. Pertea, C. Trapnell, H. Pimentel, R. Kelley et al., TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions, Genome Biology, vol.14, issue.4, 2013.
DOI : 10.1186/gb-2009-10-3-r25

E. Kim, A. Magen, and G. Ast, Different levels of alternative splicing among eukaryotes, Nucleic Acids Research, vol.35, issue.1, pp.125-131, 2007.
DOI : 10.1093/nar/gkl924

V. King, S. Rao, and R. Tarjan, A Faster Deterministic Maximum Flow Algorithm, Journal of Algorithms, vol.17, issue.3, pp.447-474, 1994.
DOI : 10.1006/jagm.1994.1044

D. C. Koboldt, K. M. Steinberg, D. E. Larson, R. K. Wilson, and E. R. Mardis, The Next-Generation Sequencing Revolution and Its Impact on Genomics, Cell, vol.155, issue.1, pp.27-38, 2013.
DOI : 10.1016/j.cell.2013.09.006

A. R. Kornblihtt, Promoter usage and alternative splicing, Current Opinion in Cell Biology, vol.17, issue.3, pp.262-268, 2005.
DOI : 10.1016/j.ceb.2005.04.014

M. Krawczak, J. Reiss, C. , and D. N. , The mutational spectrum of single basepair substitutions in mRNA splice junctions of human genes: causes and consequences, Hum. Genet, vol.90, issue.12, pp.41-54, 1992.

L. F. Lareau, A. N. Brooks, D. A. Soergel, Q. Meng, and S. E. Brenner, The Coupling of Alternative Splicing and Nonsense-Mediated mRNA Decay, Adv. Exp. Med. Biol, vol.623, pp.190-211, 2007.
DOI : 10.1007/978-0-387-77374-2_12

J. Z. Levin, M. F. Berger, X. Adiconis, P. Rogov, A. Melnikov et al., Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts, Genome Biology, vol.10, issue.10, p.115, 2009.
DOI : 10.1186/gb-2009-10-10-r115

J. Z. Levin, M. Yassour, X. Adiconis, C. Nusbaum, D. A. Thompson et al., Comprehensive comparative analysis of strand-specific RNA sequencing methods, Nature Methods, vol.5, issue.9, pp.709-715, 2010.
DOI : 10.1038/nmeth.1491

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005310/pdf

B. Li and C. N. Dewey, RSEM, p.323, 2011.
DOI : 10.1201/b16589-5

J. Li and H. Jiang, Robust estimation of isoform expression with RNA-Seq data, 2014.

J. Li, H. Jiang, and W. H. Wong, Modeling non-uniformity in short-read rates in RNA-Seq data, Genome Biology, vol.11, issue.5, p.50, 2010.
DOI : 10.1186/gb-2010-11-5-r50

URL : https://genomebiology.biomedcentral.com/track/pdf/10.1186/gb-2010-11-5-r50?site=genomebiology.biomedcentral.com

J. B. Li, E. Y. Levanon, J. K. Yoon, J. Aach, B. Xie et al., Genome-Wide Identification of Human RNA Editing Sites by Parallel DNA Capturing and Sequencing, Science, vol.14, issue.5, pp.3241210-1213, 2009.
DOI : 10.1177/1073858408319187

J. J. Li, C. Jiang, J. , B. B. Huang, H. Bickel et al., Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation, Proceedings of the National Academy of Sciences, vol.8, issue.2, pp.19867-19872, 2011.
DOI : 10.1186/1471-2105-8-1

W. Li, J. Feng, and T. Jiang, IsoLasso: A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembly, Journal of Computational Biology, vol.18, issue.11, pp.1693-1707, 2011.
DOI : 10.1089/cmb.2011.0171

URL : http://www.cs.ucr.edu/~jiang/nsf/wli-recomb.pdf

W. Li and T. Jiang, Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads, Bioinformatics, vol.28, issue.22, pp.282914-2921, 2012.
DOI : 10.1093/bioinformatics/bts559

URL : https://academic.oup.com/bioinformatics/article-pdf/28/22/2914/16910178/bts559.pdf

W. Li and T. Jiang, Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads, Bioinformatics, vol.28, issue.22, pp.282914-2921, 2012.
DOI : 10.1093/bioinformatics/bts559

URL : https://academic.oup.com/bioinformatics/article-pdf/28/22/2914/16910178/bts559.pdf

Y. Lin, P. Dao, F. Hach, M. Bakhshi, F. Mo et al., CLIIQ: Accurate Comparative Detection and Quantification of Expressed Isoforms in a Population, WABI, pp.178-189, 2012.
DOI : 10.1007/978-3-642-33122-0_14

N. Lopez-bigas, B. Audit, C. Ouzounis, G. Parra, and R. Guigo, Are splicing mutations the most frequent cause of hereditary disease?, FEBS Letters, vol.19, issue.9, pp.1900-1903, 2005.
DOI : 10.1016/S0168-9525(03)00195-1

URL : https://hal.archives-ouvertes.fr/ensl-00175527

K. Lounici, M. Pontil, A. B. Tsybakov, and S. Van-de-geer, Taking advantage of sparsity in multi-task learning, Proceedings of the 22nd Conference on Information Theory, pp.73-82, 2009.

R. F. Luco, M. Allo, I. E. Schor, A. R. Kornblihtt, and T. Misteli, Epigenetics in Alternative Pre-mRNA Splicing, Cell, vol.144, issue.1, pp.16-26, 2011.
DOI : 10.1016/j.cell.2010.11.056

URL : https://doi.org/10.1016/j.cell.2010.11.056

S. Lykke-andersen and T. H. Jensen, Nonsense-mediated mRNA decay: an intricate machinery that shapes transcriptomes, Nature Reviews Molecular Cell Biology, vol.6, issue.11, pp.665-677, 2015.
DOI : 10.1093/nar/gks1320

J. Mairal, Sparse coding for machine learning, image processing and computer vision, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00595312

J. Mairal, F. Bach, and J. Ponce, Sparse Modeling for Image and Vision Processing, Foundations and Trends?? in Computer Graphics and Vision, vol.8, issue.2-3, pp.85-283, 2014.
DOI : 10.1561/0600000058

URL : https://hal.archives-ouvertes.fr/hal-01081139

J. Mairal, R. Jenatton, G. Obozinski, and F. Bach, Convex and network flow optimization for structured sparsity, J. Mach. Learn. Res, vol.12, pp.2681-2720, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00584817

J. Mairal and B. Yu, Supervised feature selection in graphs with path coding penalties and network flows, J. Mach. Learn. Res, vol.14, pp.2449-2485, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00806372

L. Malcovati, E. Papaemmanuil, I. Ambaglio, C. Elena, A. Galli et al., Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia, Blood, vol.124, issue.9, pp.1513-1521, 2014.
DOI : 10.1182/blood-2014-03-560227

URL : http://www.bloodjournal.org/content/bloodjournal/124/9/1513.full.pdf

L. Mamanova, A. J. Coffey, C. E. Scott, I. Kozarewa, E. H. Turner et al., Target-enrichment strategies for next-generation sequencing, Nature Methods, vol.5, issue.2, pp.111-118, 2010.
DOI : 10.1002/j.1538-7305.1950.tb00463.x

E. R. Mardis, A decade???s perspective on DNA sequencing technology, Nature, vol.364, issue.7333, pp.198-203, 2011.
DOI : 10.1056/NEJMoa1012928

L. Maretty, J. A. Sibbesen, and A. Krogh, Bayesian transcriptome assembly, Genome Biology, vol.9, issue.10, p.501, 2014.
DOI : 10.1038/nmeth.1923

J. Martin and Z. Wang, Next-generation transcriptome assembly, Nature Reviews Genetics, vol.323, issue.10, pp.671-682, 2011.
DOI : 10.1126/science.1162986

URL : https://digital.library.unt.edu/ark:/67531/metadc830328/m2/1/high_res_d/1076789.pdf

S. J. Marygold, P. C. Leyland, and R. L. Seal, FlyBase: improvements to the bibliography, Nucleic Acids Research, vol.41, issue.D1, pp.41-751, 2013.
DOI : 10.1093/nar/gks1024

URL : https://academic.oup.com/nar/article-pdf/41/D1/D751/3588267/gks1024.pdf

A. J. Matlin, F. Clark, and C. W. Smith, Understanding alternative splicing: towards a cellular code, Nature Reviews Molecular Cell Biology, vol.270, issue.5, pp.386-398, 2005.
DOI : 10.1038/ng1469

K. J. Mckernan, H. E. Peckham, G. L. Costa, S. F. Mclaughlin, Y. Fu et al., Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding, Genome Research, vol.19, issue.9, pp.1527-1541, 2009.
DOI : 10.1101/gr.091868.109

A. Mcpherson, C. Wu, A. W. Wyatt, S. Shah, C. Collins et al., nFuse: Discovery of complex genomic rearrangements in cancer using high-throughput sequencing, Genome Research, vol.22, issue.11, 2012.
DOI : 10.1101/gr.136572.111

P. Medvedev and M. Brudno, Maximum Likelihood Genome Assembly, Journal of Computational Biology, vol.16, issue.8, pp.1101-1116, 2009.
DOI : 10.1089/cmb.2009.0047

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154397/pdf

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-473, 2010.
DOI : 10.1186/1471-2105-9-307

URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2010.00740.x/pdf

E. Melamud and J. Moult, Stochastic noise in splicing machinery, Nucleic Acids Research, vol.37, issue.14, pp.4873-4886, 2009.
DOI : 10.1093/nar/gkp471

URL : https://academic.oup.com/nar/article-pdf/37/14/4873/16753728/gkp471.pdf

T. R. Mercer, D. J. Gerhardt, M. E. Dinger, J. Crawford, C. Trapnell et al., Targeted RNA sequencing reveals the deep complexity of the human transcriptome, Nature Biotechnology, vol.30, issue.1, pp.99-104, 2012.
DOI : 10.1093/bioinformatics/btm098

E. C. Merkhofer, P. Hu, J. , and T. L. , Introduction to Cotranscriptional RNA Splicing, Methods Mol. Biol, vol.1126, pp.83-96, 2014.
DOI : 10.1007/978-1-62703-980-2_6

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102251/pdf

A. M. Mezlini, M. , S. E. Fiume, M. Buske, O. Savich et al., iReckon: Simultaneous isoform discovery and abundance estimation from RNA-seq data, Genome Research, vol.23, issue.3, pp.519-529, 2013.
DOI : 10.1101/gr.142232.112

URL : http://genome.cshlp.org/content/23/3/519.full.pdf

B. Modrek, A. Resch, C. Grasso, L. , and C. , Genome-wide detection of alternative splicing in expressed sequences of human genes, Nucleic Acids Research, vol.29, issue.13, pp.292850-2859, 2001.
DOI : 10.1093/nar/29.13.2850

S. B. Montgomery, Transcriptome genetics using second generation sequencing in a Caucasian population, Nature, vol.4, issue.7289, pp.464773-777, 2010.
DOI : 10.1038/nature08903

A. Mortazavi, Scaffolding a Caenorhabditis nematode genome with RNA-seq, Genome Research, vol.20, issue.12, 2010.
DOI : 10.1101/gr.111021.110

URL : http://genome.cshlp.org/content/20/12/1740.full.pdf

A. Mortazavi, B. A. Williams, K. Mccue, and L. Schaeffer, Mapping and quantifying mammalian transcriptomes by RNA-Seq, Nature Methods, vol.14, issue.7, pp.621-628, 2008.
DOI : 10.1128/MCB.14.3.1647

J. M. Mudge, A. Frankish, J. Fernandez-banet, T. Alioto, T. Derrien et al., The Origins, Evolution, and Functional Potential of Alternative Splicing in Vertebrates, Molecular Biology and Evolution, vol.28, issue.10, pp.282949-2959, 2011.
DOI : 10.1093/molbev/msr127

U. Nagalakshmi, Z. Wang, K. Waern, C. Shou, D. Raha et al., The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing, Science, vol.265, issue.12, pp.3201344-1349, 2008.
DOI : 10.1146/annurev.micro.59.031805.133833

S. H. Nagaraj, R. B. Gasser, and S. And-ranganathan, A hitchhiker's guide to expressed sequence tag (EST) analysis, Briefings in Bioinformatics, vol.8, issue.1, pp.6-21, 2007.
DOI : 10.1093/bib/bbl015

URL : https://academic.oup.com/bib/article-pdf/8/1/6/735879/bbl015.pdf

B. K. Natarajan, Sparse Approximate Solutions to Linear Systems, SIAM Journal on Computing, vol.24, issue.2, pp.227-234, 1995.
DOI : 10.1137/S0097539792240406

N. E. Navin, The first five years of single-cell cancer genomics and beyond, Genome Research, vol.25, issue.10, pp.1499-1507, 2015.
DOI : 10.1101/gr.191098.115

M. Nicolae, S. Mangul, I. I. Ndoiu, and A. Zelikovsky, Estimation of alternative splicing isoform frequencies from RNA-Seq data, Algorithms for Molecular Biology, vol.6, issue.1, p.9, 2011.
DOI : 10.1186/1471-2164-10-221

T. W. Nilsen and B. R. Graveley, Expansion of the eukaryotic proteome by alternative splicing, Nature, vol.114, issue.7280, pp.463457-463, 2010.
DOI : 10.1371/journal.pcbi.0010015

F. Ozsolak and P. M. Milos, RNA sequencing: advances, challenges and opportunities, Nature Reviews Genetics, vol.6, issue.2, pp.87-98, 2011.
DOI : 10.1038/nmeth.1354

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031867/pdf

L. Pachter, Models for transcript quantification from RNA-Seq. ArXiv e-prints, 2011.

S. Pal, R. Gupta, and R. V. Davuluri, Alternative transcription and alternative splicing in cancer, Pharmacology & Therapeutics, vol.136, issue.3, pp.283-294, 2012.
DOI : 10.1016/j.pharmthera.2012.08.005

Q. Pan, O. Shai, L. J. Lee, B. J. Frey, and B. J. Blencowe, Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing, Nature Genetics, vol.76, issue.12, 2008.
DOI : 10.1016/j.molcel.2004.12.004

Q. Pan, O. Shai, C. Misquitta, W. Zhang, A. L. Saltzman et al., Revealing Global Regulatory Features of Mammalian Alternative Splicing Using a Quantitative Microarray Platform, Molecular Cell, vol.16, issue.6, pp.16929-941, 2004.
DOI : 10.1016/j.molcel.2004.12.004

E. Papaemmanuil, M. Cazzola, J. Boultwood, L. Malcovati, P. Vyas et al., Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts, N. Engl. J. Med, issue.15, pp.3651384-1395, 2011.
DOI : 10.1056/nejmoa1103283

R. Patro, S. M. Mount, and C. Kingsford, Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms, Nature Biotechnology, vol.23, issue.5, pp.462-464, 2014.
DOI : 10.1093/bioinformatics/btp120

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077321/pdf

M. Pertea, G. M. Pertea, C. M. Antonescu, T. C. Chang, J. T. Mendell et al., StringTie enables improved reconstruction of a transcriptome from RNA-seq reads, Nature Biotechnology, vol.16, issue.3, 2015.
DOI : 10.1287/moor.16.2.351

M. W. Popp and L. E. Maquat, Organizing Principles of Mammalian Nonsense-Mediated mRNA Decay, Annual Review of Genetics, vol.47, issue.1, pp.139-165, 2013.
DOI : 10.1146/annurev-genet-111212-133424

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4148824/pdf

K. Pruitt, T. Tatusova, and D. R. Maglott, Ncbi reference sequence (refseq): a curated non-redundant sequence database of genomes, transcripts and proteins, Nucleic Acids Res, issue.supp1, pp.33-501, 2005.

A. R. Quinlan and I. M. Hall, BEDTools: a flexible suite of utilities for comparing genomic features, Bioinformatics, vol.26, issue.6, pp.841-842, 2010.
DOI : 10.1093/bioinformatics/btq033

A. Roberts and L. Pachter, Streaming fragment assignment for real-time analysis of sequencing experiments, Nature Methods, vol.10, issue.1, pp.71-73, 2013.
DOI : 10.1093/nar/gkr1246

A. Roberts, C. Trapnell, J. Donaghey, J. L. Rinn, and L. Pachter, Improving RNA-Seq expression estimates by correcting for fragment bias, Genome Biology, vol.12, issue.3, p.22, 2011.
DOI : 10.1186/gb-2009-10-3-r25

URL : http://doi.org/10.1186/gb-2011-12-3-r22

G. Robertson, J. Schein, R. Chiu, R. Corbett, M. Field et al., De novo assembly and analysis of RNA-seq data, Nature Methods, vol.7, issue.11, pp.7909-912, 2010.
DOI : 10.1038/nbt0509-455

J. T. Robinson, H. Thorvaldsdottir, W. Winckler, M. Guttman, E. S. Lander et al., Integrative genomics viewer, Nature Biotechnology, vol.306, issue.1, pp.24-26, 2011.
DOI : 10.1093/bioinformatics/btp472

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3346182/pdf

A. Romero, F. Garcia-garcia, I. Lopez-perolio, G. Ruiz-de-garibay, J. A. Garcia-saenz et al., BRCA1 Alternative splicing landscape in breast tissue samples, BMC Cancer, vol.32, issue.1, p.219, 2015.
DOI : 10.1016/j.semcdb.2014.03.016

URL : https://bmccancer.biomedcentral.com/track/pdf/10.1186/s12885-015-1145-9?site=bmccancer.biomedcentral.com

D. Rossell, C. Stephan-otto-attolini, M. Kroiss, and A. Stocker, Corrigendum: Quantifying alternative splicing from paired-end RNA-seq data, The Annals of Applied Statistics, vol.9, issue.3, pp.309-330, 2014.
DOI : 10.1214/15-AOAS855

S. Rosset and J. Zhu, Piecewise linear regularized solution paths, The Annals of Statistics, vol.35, issue.3, pp.1012-1030, 2007.
DOI : 10.1214/009053606000001370

URL : http://doi.org/10.1214/009053606000001370

J. M. Rothberg, W. Hinz, T. M. Rearick, J. Schultz, W. Mileski et al., An integrated semiconductor device enabling non-optical genome sequencing, Nature, vol.32, issue.7, pp.475348-352, 2011.
DOI : 10.1002/humu.21382

URL : http://www.nature.com/nature/journal/v475/n7356/pdf/nature10242.pdf

R. Roy, J. Chun, P. , and S. N. , BRCA1 and BRCA2: different roles in a common pathway of genome protection, Nature Reviews Cancer, vol.336, issue.1, pp.68-78, 2012.
DOI : 10.1056/NEJM199705153362003

H. K. Salz, Sex determination in insects: a binary decision based on alternative splicing, Current Opinion in Genetics & Development, vol.21, issue.4, pp.395-400, 2011.
DOI : 10.1016/j.gde.2011.03.001

J. Salzman, H. Jiang, and W. H. Wong, Statistical Modeling of RNA-Seq Data, Statistical Science, vol.26, issue.1, pp.62-83, 2011.
DOI : 10.1214/10-STS343

F. Sanger, S. Nicklen, and A. R. Coulson, DNA sequencing with chain-terminating inhibitors, Proceedings of the National Academy of Sciences, vol.74, issue.12, pp.745463-5467, 1977.
DOI : 10.1073/pnas.74.12.5463

URL : http://www.pnas.org/content/74/12/5463.full.pdf

D. Schmucker, J. C. Clemens, H. Shu, C. A. Worby, J. Xiao et al., Drosophila Dscam Is an Axon Guidance Receptor Exhibiting Extraordinary Molecular Diversity, Cell, vol.101, issue.6, pp.671-684, 2000.
DOI : 10.1016/S0092-8674(00)80878-8

URL : https://doi.org/10.1016/s0092-8674(00)80878-8

M. H. Schulz, D. R. Zerbino, M. Vingron, and E. Birney, Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels, Bioinformatics, vol.28, issue.8, pp.281086-1092, 2012.
DOI : 10.1093/bioinformatics/bts094

S. Schwartz and G. Ast, Chromatin density and splicing destiny: on the cross-talk between chromatin structure and splicing, The EMBO Journal, vol.14, issue.10, pp.1629-1636, 2010.
DOI : 10.1073/pnas.93.14.6975

URL : http://emboj.embopress.org/content/embojnl/29/10/1629.full.pdf

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

C. Schwerk and K. Schulze-osthoff, Regulation of Apoptosis by Alternative Pre-mRNA Splicing, Molecular Cell, vol.19, issue.1, pp.1-13, 2005.
DOI : 10.1016/j.molcel.2005.05.026

M. M. Scotti and M. S. Swanson, RNA mis-splicing in disease, Nature Reviews Genetics, vol.11, issue.1, pp.19-32, 2016.
DOI : 10.1093/hmg/ddv034

M. M. Scotti and M. S. Swanson, RNA mis-splicing in disease, Nature Reviews Genetics, vol.11, issue.1, pp.19-32, 2016.
DOI : 10.1093/hmg/ddv034

E. Sebestyen, M. Zawisza, and E. Eyras, Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer, Nucleic Acids Research, vol.43, issue.3, pp.1345-1356, 2015.
DOI : 10.1093/nar/gku1392

I. M. Shapiro, A. W. Cheng, N. C. Flytzanis, M. Balsamo, J. S. Condeelis et al., An EMT???Driven Alternative Splicing Program Occurs in Human Breast Cancer and Modulates Cellular Phenotype, PLoS Genetics, vol.19, issue.Pt 6, p.1002218, 2011.
DOI : 10.1371/journal.pgen.1002218.s020

URL : https://doi.org/10.1371/journal.pgen.1002218

D. Singh, FDM: a graph-based statistical method to detect differential transcription using RNA-seq data, Bioinformatics, vol.27, issue.19, pp.272633-2640, 2011.
DOI : 10.1093/bioinformatics/btr458

URL : https://academic.oup.com/bioinformatics/article-pdf/27/19/2633/16899811/btr458.pdf

A. Skandalis, M. Frampton, J. Seger, and M. H. Richards, The adaptive significance of unproductive alternative splicing in primates, RNA, vol.16, issue.10, pp.162014-2022, 2010.
DOI : 10.1261/rna.2127910

L. M. Smith, J. Z. Sanders, R. J. Kaiser, P. Hughes, C. Dodd et al., Fluorescence detection in automated DNA sequence analysis, Nature, vol.13, issue.6071, pp.321674-679, 1986.
DOI : 10.1038/321674a0

L. Song and L. Florea, CLASS: constrained transcript assembly of RNA-seq reads, BMC Bioinformatics, vol.14, issue.Suppl 5, p.14, 2013.
DOI : 10.1093/bioinformatics/btp120

URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-14-S5-S14?site=bmcbioinformatics.biomedcentral.com

P. Spitali and A. Aartsma-rus, Splice Modulating Therapies for Human Disease, Cell, vol.148, issue.6, pp.1085-1088, 2012.
DOI : 10.1016/j.cell.2012.02.014

URL : https://doi.org/10.1016/j.cell.2012.02.014

A. B. Spurdle, F. J. Couch, F. B. Hogervorst, P. Radice, O. M. Sinilnikova et al., Prediction and assessment of splicing alterations: implications for clinical testing, Human Mutation, vol.25, issue.Web Server issu, pp.291304-1313, 2008.
DOI : 10.1002/humu.20901

A. Srebrow and A. R. Kornblihtt, The connection between splicing and cancer, Journal of Cell Science, vol.119, issue.13, pp.2635-2641, 2006.
DOI : 10.1242/jcs.03053

URL : http://jcs.biologists.org/content/joces/119/13/2635.full.pdf

T. Steijger, J. F. Abril, P. G. Engstrom, F. Kokocinski, T. J. Hubbard et al., Assessment of transcript reconstruction methods for RNA-seq, Nat. Methods, issue.12, pp.101177-1184, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00909081

R. Tibshirani, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. Series B Stat. Methodol, vol.58, issue.1, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

A. Tomescu, A. Kuosmanen, R. Rizzi, and V. Makinen, A novel min-cost flow method for estimating transcript expression with rna-seq, BMC Bioinformatics, pp.14-15, 2013.
DOI : 10.1007/978-3-642-40453-5_8

C. Trapnell, L. Patcher, and S. L. Salzberg, TopHat: discovering splice junctions with RNA-Seq, Bioinformatics, vol.25, issue.9, pp.1105-1111, 2009.
DOI : 10.1093/bioinformatics/btp120

URL : https://academic.oup.com/bioinformatics/article-pdf/25/9/1105/16892242/btp120.pdf

C. Trapnell, B. A. Williams, G. Pertea, A. M. Mortazavi, G. Kwan et al., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation, Nature Biotechnology, vol.25, issue.5, pp.511-515, 2010.
DOI : 10.1093/bioinformatics/btp352

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146043/pdf

Y. S. Tsai, D. Dominguez, S. M. Gomez, W. , and Z. , Transcriptome-wide identification and study of cancer-specific splicing events across multiple tumors, Oncotarget, vol.6, issue.9, pp.6825-6839, 2015.
DOI : 10.18632/oncotarget.3145

E. Turro, S. Y. Su, A. Goncalves, L. J. Coin, S. Richardson et al., Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads, Genome Biology, vol.12, issue.2, p.13, 2011.
DOI : 10.1126/science.1190831

URL : https://genomebiology.biomedcentral.com/track/pdf/10.1186/gb-2011-12-2-r13?site=genomebiology.biomedcentral.com

E. L. Van-dijk, H. Auger, Y. Jaszczyszyn, and C. Thermes, Ten years of next-generation sequencing technology, Trends in Genetics, vol.30, issue.9, pp.418-426, 2014.
DOI : 10.1016/j.tig.2014.07.001

K. Van-keuren-jensen, J. J. Keats, C. , and D. W. , Bringing RNA-seq closer to the clinic, Nature Biotechnology, vol.28, issue.9, pp.884-885, 2014.
DOI : 10.1038/nbt.2403

E. T. Wang, R. Sandberg, S. Luo, I. Khrebtukova, L. Zhang et al., Alternative isoform regulation in human tissue transcriptomes, Nature, vol.15, issue.7221, pp.456470-476, 2008.
DOI : 10.1038/nature07509

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2593745/pdf

E. T. Wang, R. Sandberg, S. Luo, I. Khrebtukova, L. Zhang et al., Alternative isoform regulation in human tissue transcriptomes, Nature, vol.15, issue.7221, pp.456470-476, 2008.
DOI : 10.1038/nature07509

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2593745/pdf

G. S. Wang and T. A. Cooper, Splicing in disease: disruption of the splicing code and the decoding machinery, Nature Reviews Genetics, vol.16, issue.10, pp.749-761, 2007.
DOI : 10.1074/jbc.270.6.2411

K. Wang, D. Singh, Z. Zeng, S. J. Coleman, Y. Huang et al., MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery, Nucleic Acids Research, vol.38, issue.18, pp.38-178, 2010.
DOI : 10.1093/nar/gkq622

Z. Wang, M. Gerstein, and M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics, Nature Reviews Genetics, vol.328, issue.1, pp.57-63, 2009.
DOI : 10.1038/nrg2484

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949280/pdf

J. Q. Wu, L. Habegger, P. Noisa, A. Szekely, C. Qiu et al., Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing, Proceedings of the National Academy of Sciences, pp.5254-5259, 2010.
DOI : 10.1073/pnas.0602280103

T. D. Wu and S. Nacu, Fast and SNP-tolerant detection of complex variants and splicing in short reads, Bioinformatics, vol.26, issue.7, pp.873-881, 2010.
DOI : 10.1093/bioinformatics/btq057

Z. Xia, J. , W. Chang, C. Zhou, and X. , NSMAP: A method for spliced isoforms identification and quantification from RNA-Seq, BMC Bioinformatics, vol.12, issue.1, p.162, 2011.
DOI : 10.1214/009053604000000067

Y. Xie, G. Wu, J. Tang, R. Luo, J. Patterson et al., SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads, Bioinformatics, vol.30, issue.12, pp.301660-1666, 2014.
DOI : 10.1093/bioinformatics/btu077

URL : https://academic.oup.com/bioinformatics/article-pdf/30/12/1660/1081308/btu077.pdf

Q. Xu, K. Modrek, L. , and C. , Genome-wide detection of tissue-specific alternative splicing in the human transcriptome, Nucleic Acids Research, vol.30, issue.17, pp.303754-3766, 2002.
DOI : 10.1093/nar/gkf492

J. Yan and T. G. Marr, Computational analysis of 3'-ends of ESTs shows four classes of alternative polyadenylation in human, mouse, and rat, Genome Research, vol.15, issue.3, pp.369-375, 2005.
DOI : 10.1101/gr.3109605

G. Yeo, D. Holste, G. Kreiman, B. , and B. C. , Variation in alternative splicing across human tissues, Genome Biology, vol.5, issue.10, p.74, 2004.
DOI : 10.1186/gb-2004-5-10-r74

K. Yoshida and S. Ogawa, Splicing factor mutations and cancer, Wiley Interdisciplinary Reviews: RNA, vol.8, issue.4, pp.445-459, 2014.
DOI : 10.1158/1535-7163.MCT-09-0051

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356

D. R. Zerbino, T. Ballinger, B. Paten, G. Hickey, and D. Haussler, Representing and decomposing genomic structural variants as balanced integer flows on sequence graphs, BMC Bioinformatics, vol.28, issue.1, 2013.
DOI : 10.1073/pnas.39.4.315

URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1258-4?site=bmcbioinformatics.biomedcentral.com

J. D. Zhang, T. Schindler, E. Kung, M. Ebeling, C. et al., Highly sensitive amplicon-based transcript quantification by semiconductor sequencing, BMC Genomics, vol.15, issue.1, p.565, 2014.
DOI : 10.1186/1471-2164-15-565

URL : http://doi.org/10.1186/1471-2164-15-565

K. Zhang, J. B. Li, Y. Gao, D. Egli, B. Xie et al., Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human, Nature Methods, vol.5, issue.8, pp.6613-618, 2009.
DOI : 10.1126/science.1158799

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2742772/pdf

Z. Zhang, S. Pal, Y. Bi, J. Tchou, and R. V. Davuluri, Isoform level expression profiles provide better cancer signatures than gene level expression profiles, Genome Medicine, vol.5, issue.4, p.33, 2013.
DOI : 10.1097/PPO.0b013e3181bd0445

URL : https://genomemedicine.biomedcentral.com/track/pdf/10.1186/gm437?site=genomemedicine.biomedcentral.com

W. Zheng, L. M. Chung, and H. Zhao, Bias detection and correction in RNA-Sequencing data, BMC Bioinformatics, vol.12, issue.1, p.290, 2011.
DOI : 10.1038/nature08872

URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-12-290?site=bmcbioinformatics.biomedcentral.com

Z. Zheng, M. Liebers, B. Zhelyazkova, Y. Cao, D. Panditi et al., Anchored multiplex PCR for targeted next-generation sequencing, Nature Medicine, vol.40, issue.12, pp.1479-1484, 2014.
DOI : 10.1093/nar/gks596