KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data

Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn grap...
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Autor*in:	Sacomoto, Gustavo AT [verfasserIn] Kielbassa, Janice Chikhi, Rayan Uricaru, Raluca Antoniou, Pavlos Sagot, Marie-France Peterlongo, Pierre Lacroix, Vincent

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Short Path Reference Genome Splice Event Variable Part Alternative Transcript

Anmerkung:	© Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 13(2012), Suppl 6 vom: 19. Apr.
Übergeordnetes Werk:	volume:13 ; year:2012 ; number:Suppl 6 ; day:19 ; month:04

Links:	Volltext

DOI / URN:	10.1186/1471-2105-13-S6-S5

Katalog-ID:	SPR026881934

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520			\|a Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/.
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700	1		\|a Peterlongo, Pierre \|4 aut
700	1		\|a Lacroix, Vincent \|4 aut
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author_variant	g a s ga gas j k jk r c rc r u ru p a pa m f s mfs p p pp v l vl
matchkey_str	article:14712105:2012----::islcdnvcliglentvslcnee
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allfields	10.1186/1471-2105-13-S6-S5 doi (DE-627)SPR026881934 (SPR)1471-2105-13-S6-S5-e DE-627 ger DE-627 rakwb eng Sacomoto, Gustavo AT verfasserin aut KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. Short Path (dpeaa)DE-He213 Reference Genome (dpeaa)DE-He213 Splice Event (dpeaa)DE-He213 Variable Part (dpeaa)DE-He213 Alternative Transcript (dpeaa)DE-He213 Kielbassa, Janice aut Chikhi, Rayan aut Uricaru, Raluca aut Antoniou, Pavlos aut Sagot, Marie-France aut Peterlongo, Pierre aut Lacroix, Vincent aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 6 vom: 19. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 6 day:19 month:04 https://dx.doi.org/10.1186/1471-2105-13-S6-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 6 19 04
spelling	10.1186/1471-2105-13-S6-S5 doi (DE-627)SPR026881934 (SPR)1471-2105-13-S6-S5-e DE-627 ger DE-627 rakwb eng Sacomoto, Gustavo AT verfasserin aut KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. Short Path (dpeaa)DE-He213 Reference Genome (dpeaa)DE-He213 Splice Event (dpeaa)DE-He213 Variable Part (dpeaa)DE-He213 Alternative Transcript (dpeaa)DE-He213 Kielbassa, Janice aut Chikhi, Rayan aut Uricaru, Raluca aut Antoniou, Pavlos aut Sagot, Marie-France aut Peterlongo, Pierre aut Lacroix, Vincent aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 6 vom: 19. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 6 day:19 month:04 https://dx.doi.org/10.1186/1471-2105-13-S6-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 6 19 04
allfields_unstemmed	10.1186/1471-2105-13-S6-S5 doi (DE-627)SPR026881934 (SPR)1471-2105-13-S6-S5-e DE-627 ger DE-627 rakwb eng Sacomoto, Gustavo AT verfasserin aut KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. Short Path (dpeaa)DE-He213 Reference Genome (dpeaa)DE-He213 Splice Event (dpeaa)DE-He213 Variable Part (dpeaa)DE-He213 Alternative Transcript (dpeaa)DE-He213 Kielbassa, Janice aut Chikhi, Rayan aut Uricaru, Raluca aut Antoniou, Pavlos aut Sagot, Marie-France aut Peterlongo, Pierre aut Lacroix, Vincent aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 6 vom: 19. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 6 day:19 month:04 https://dx.doi.org/10.1186/1471-2105-13-S6-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 6 19 04
allfieldsGer	10.1186/1471-2105-13-S6-S5 doi (DE-627)SPR026881934 (SPR)1471-2105-13-S6-S5-e DE-627 ger DE-627 rakwb eng Sacomoto, Gustavo AT verfasserin aut KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. Short Path (dpeaa)DE-He213 Reference Genome (dpeaa)DE-He213 Splice Event (dpeaa)DE-He213 Variable Part (dpeaa)DE-He213 Alternative Transcript (dpeaa)DE-He213 Kielbassa, Janice aut Chikhi, Rayan aut Uricaru, Raluca aut Antoniou, Pavlos aut Sagot, Marie-France aut Peterlongo, Pierre aut Lacroix, Vincent aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 6 vom: 19. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 6 day:19 month:04 https://dx.doi.org/10.1186/1471-2105-13-S6-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 6 19 04
allfieldsSound	10.1186/1471-2105-13-S6-S5 doi (DE-627)SPR026881934 (SPR)1471-2105-13-S6-S5-e DE-627 ger DE-627 rakwb eng Sacomoto, Gustavo AT verfasserin aut KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. Short Path (dpeaa)DE-He213 Reference Genome (dpeaa)DE-He213 Splice Event (dpeaa)DE-He213 Variable Part (dpeaa)DE-He213 Alternative Transcript (dpeaa)DE-He213 Kielbassa, Janice aut Chikhi, Rayan aut Uricaru, Raluca aut Antoniou, Pavlos aut Sagot, Marie-France aut Peterlongo, Pierre aut Lacroix, Vincent aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 6 vom: 19. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 6 day:19 month:04 https://dx.doi.org/10.1186/1471-2105-13-S6-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 6 19 04
language	English
source	Enthalten in BMC bioinformatics 13(2012), Suppl 6 vom: 19. Apr. volume:13 year:2012 number:Suppl 6 day:19 month:04
sourceStr	Enthalten in BMC bioinformatics 13(2012), Suppl 6 vom: 19. Apr. volume:13 year:2012 number:Suppl 6 day:19 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Short Path Reference Genome Splice Event Variable Part Alternative Transcript
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Sacomoto, Gustavo AT @@aut@@ Kielbassa, Janice @@aut@@ Chikhi, Rayan @@aut@@ Uricaru, Raluca @@aut@@ Antoniou, Pavlos @@aut@@ Sagot, Marie-France @@aut@@ Peterlongo, Pierre @@aut@@ Lacroix, Vincent @@aut@@
publishDateDaySort_date	2012-04-19T00:00:00Z
hierarchy_top_id	326644814
id	SPR026881934
language_de	englisch
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topic_title	KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data Short Path (dpeaa)DE-He213 Reference Genome (dpeaa)DE-He213 Splice Event (dpeaa)DE-He213 Variable Part (dpeaa)DE-He213 Alternative Transcript (dpeaa)DE-He213
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title	KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
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title_full	KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
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author_browse	Sacomoto, Gustavo AT Kielbassa, Janice Chikhi, Rayan Uricaru, Raluca Antoniou, Pavlos Sagot, Marie-France Peterlongo, Pierre Lacroix, Vincent
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title_sort	kis splice: de-novo calling alternative splicing events from rna-seq data
title_auth	KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
abstract	Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background In this paper, we address the problem of identifying and quantifying polymorphisms in RNA-seq data when no reference genome is available, without assembling the full transcripts. Based on the fundamental idea that each polymorphism corresponds to a recognisable pattern in a De Bruijn graph constructed from the RNA-seq reads, we propose a general model for all polymorphisms in such graphs. We then introduce an exact algorithm, called KIS SPLICE, to extract alternative splicing events. Results We show that KIS SPLICE enables to identify more correct events than general purpose transcriptome assemblers. Additionally, on a 71 M reads dataset from human brain and liver tissues, KIS SPLICE identified 3497 alternative splicing events, out of which 56% are not present in the annotations, which confirms recent estimates showing that the complexity of alternative splicing has been largely underestimated so far. Conclusions We propose new models and algorithms for the detection of polymorphism in RNA-seq data. This opens the way to a new kind of studies on large HTS RNA-seq datasets, where the focus is not the global reconstruction of full-length transcripts, but local assembly of polymorphic regions. KIS SPLICE is available for download at http://alcovna.genouest.org/kissplice/. © Sacomoto et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
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title_short	KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
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