MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

Abstract We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14%...
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Autor*in:	Mort, Matthew [verfasserIn] Sterne-Weiler, Timothy Li, Biao Ball, Edward V Cooper, David N Radivojac, Predrag Sanford, Jeremy R Mooney, Sean D

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Splice Site Random Forest Single Base Substitution Exonic Variant Human Splice Finder

Anmerkung:	© Mort et al.; licensee BioMed Central Ltd. 2014. 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: Genome biology - London : BioMed Central, 2000, 15(2014), 1 vom: 13. Jan.
Übergeordnetes Werk:	volume:15 ; year:2014 ; number:1 ; day:13 ; month:01

Links:	Volltext

DOI / URN:	10.1186/gb-2014-15-1-r19

Katalog-ID:	SPR030019133

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520			\|a Abstract We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.
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allfields	10.1186/gb-2014-15-1-r19 doi (DE-627)SPR030019133 (SPR)gb-2014-15-1-r19-e DE-627 ger DE-627 rakwb eng Mort, Matthew verfasserin aut MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Mort et al.; licensee BioMed Central Ltd. 2014. 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 We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. Splice Site (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Single Base Substitution (dpeaa)DE-He213 Exonic Variant (dpeaa)DE-He213 Human Splice Finder (dpeaa)DE-He213 Sterne-Weiler, Timothy aut Li, Biao aut Ball, Edward V aut Cooper, David N aut Radivojac, Predrag aut Sanford, Jeremy R aut Mooney, Sean D aut Enthalten in Genome biology London : BioMed Central, 2000 15(2014), 1 vom: 13. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:15 year:2014 number:1 day:13 month:01 https://dx.doi.org/10.1186/gb-2014-15-1-r19 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 13 01
spelling	10.1186/gb-2014-15-1-r19 doi (DE-627)SPR030019133 (SPR)gb-2014-15-1-r19-e DE-627 ger DE-627 rakwb eng Mort, Matthew verfasserin aut MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Mort et al.; licensee BioMed Central Ltd. 2014. 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 We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. Splice Site (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Single Base Substitution (dpeaa)DE-He213 Exonic Variant (dpeaa)DE-He213 Human Splice Finder (dpeaa)DE-He213 Sterne-Weiler, Timothy aut Li, Biao aut Ball, Edward V aut Cooper, David N aut Radivojac, Predrag aut Sanford, Jeremy R aut Mooney, Sean D aut Enthalten in Genome biology London : BioMed Central, 2000 15(2014), 1 vom: 13. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:15 year:2014 number:1 day:13 month:01 https://dx.doi.org/10.1186/gb-2014-15-1-r19 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 13 01
allfields_unstemmed	10.1186/gb-2014-15-1-r19 doi (DE-627)SPR030019133 (SPR)gb-2014-15-1-r19-e DE-627 ger DE-627 rakwb eng Mort, Matthew verfasserin aut MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Mort et al.; licensee BioMed Central Ltd. 2014. 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 We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. Splice Site (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Single Base Substitution (dpeaa)DE-He213 Exonic Variant (dpeaa)DE-He213 Human Splice Finder (dpeaa)DE-He213 Sterne-Weiler, Timothy aut Li, Biao aut Ball, Edward V aut Cooper, David N aut Radivojac, Predrag aut Sanford, Jeremy R aut Mooney, Sean D aut Enthalten in Genome biology London : BioMed Central, 2000 15(2014), 1 vom: 13. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:15 year:2014 number:1 day:13 month:01 https://dx.doi.org/10.1186/gb-2014-15-1-r19 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 13 01
allfieldsGer	10.1186/gb-2014-15-1-r19 doi (DE-627)SPR030019133 (SPR)gb-2014-15-1-r19-e DE-627 ger DE-627 rakwb eng Mort, Matthew verfasserin aut MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Mort et al.; licensee BioMed Central Ltd. 2014. 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 We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. Splice Site (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Single Base Substitution (dpeaa)DE-He213 Exonic Variant (dpeaa)DE-He213 Human Splice Finder (dpeaa)DE-He213 Sterne-Weiler, Timothy aut Li, Biao aut Ball, Edward V aut Cooper, David N aut Radivojac, Predrag aut Sanford, Jeremy R aut Mooney, Sean D aut Enthalten in Genome biology London : BioMed Central, 2000 15(2014), 1 vom: 13. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:15 year:2014 number:1 day:13 month:01 https://dx.doi.org/10.1186/gb-2014-15-1-r19 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 13 01
allfieldsSound	10.1186/gb-2014-15-1-r19 doi (DE-627)SPR030019133 (SPR)gb-2014-15-1-r19-e DE-627 ger DE-627 rakwb eng Mort, Matthew verfasserin aut MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Mort et al.; licensee BioMed Central Ltd. 2014. 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 We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. Splice Site (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Single Base Substitution (dpeaa)DE-He213 Exonic Variant (dpeaa)DE-He213 Human Splice Finder (dpeaa)DE-He213 Sterne-Weiler, Timothy aut Li, Biao aut Ball, Edward V aut Cooper, David N aut Radivojac, Predrag aut Sanford, Jeremy R aut Mooney, Sean D aut Enthalten in Genome biology London : BioMed Central, 2000 15(2014), 1 vom: 13. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:15 year:2014 number:1 day:13 month:01 https://dx.doi.org/10.1186/gb-2014-15-1-r19 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 13 01
language	English
source	Enthalten in Genome biology 15(2014), 1 vom: 13. Jan. volume:15 year:2014 number:1 day:13 month:01
sourceStr	Enthalten in Genome biology 15(2014), 1 vom: 13. Jan. volume:15 year:2014 number:1 day:13 month:01
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topic_facet	Splice Site Random Forest Single Base Substitution Exonic Variant Human Splice Finder
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container_title	Genome biology
authorswithroles_txt_mv	Mort, Matthew @@aut@@ Sterne-Weiler, Timothy @@aut@@ Li, Biao @@aut@@ Ball, Edward V @@aut@@ Cooper, David N @@aut@@ Radivojac, Predrag @@aut@@ Sanford, Jeremy R @@aut@@ Mooney, Sean D @@aut@@
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author	Mort, Matthew
spellingShingle	Mort, Matthew misc Splice Site misc Random Forest misc Single Base Substitution misc Exonic Variant misc Human Splice Finder MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
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topic_title	MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing Splice Site (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Single Base Substitution (dpeaa)DE-He213 Exonic Variant (dpeaa)DE-He213 Human Splice Finder (dpeaa)DE-He213
topic	misc Splice Site misc Random Forest misc Single Base Substitution misc Exonic Variant misc Human Splice Finder
topic_unstemmed	misc Splice Site misc Random Forest misc Single Base Substitution misc Exonic Variant misc Human Splice Finder
topic_browse	misc Splice Site misc Random Forest misc Single Base Substitution misc Exonic Variant misc Human Splice Finder
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
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title_full	MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
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author_browse	Mort, Matthew Sterne-Weiler, Timothy Li, Biao Ball, Edward V Cooper, David N Radivojac, Predrag Sanford, Jeremy R Mooney, Sean D
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title_sort	mutpred splice: machine learning-based prediction of exonic variants that disrupt splicing
title_auth	MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
abstract	Abstract We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. © Mort et al.; licensee BioMed Central Ltd. 2014. 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	Abstract We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. © Mort et al.; licensee BioMed Central Ltd. 2014. 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	Abstract We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice. © Mort et al.; licensee BioMed Central Ltd. 2014. 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 (
collection_details	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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
url	https://dx.doi.org/10.1186/gb-2014-15-1-r19
remote_bool	true
author2	Sterne-Weiler, Timothy Li, Biao Ball, Edward V Cooper, David N Radivojac, Predrag Sanford, Jeremy R Mooney, Sean D
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MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

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