Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants.
BPP
Benchmark
Branch point
Branchpointer
HSF
LaBranchoR
Prediction
RNA
RNABPS
SVM-BPfinder
Variants
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
28 Jan 2020
28 Jan 2020
Historique:
received:
16
07
2019
accepted:
10
01
2020
entrez:
30
1
2020
pubmed:
30
1
2020
medline:
29
9
2020
Statut:
epublish
Résumé
Branch points (BPs) map within short motifs upstream of acceptor splice sites (3'ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3'ss. We used a large set of constitutive and alternative human 3'ss collected from Ensembl (n = 264,787 3'ss) and from in-house RNAseq experiments (n = 51,986 3'ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3'ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%. Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3'ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area.
Sections du résumé
BACKGROUND
BACKGROUND
Branch points (BPs) map within short motifs upstream of acceptor splice sites (3'ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3'ss.
RESULTS
RESULTS
We used a large set of constitutive and alternative human 3'ss collected from Ensembl (n = 264,787 3'ss) and from in-house RNAseq experiments (n = 51,986 3'ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3'ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%.
CONCLUSIONS
CONCLUSIONS
Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3'ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area.
Identifiants
pubmed: 31992191
doi: 10.1186/s12864-020-6484-5
pii: 10.1186/s12864-020-6484-5
pmc: PMC6988378
doi:
Substances chimiques
RNA Precursors
0
RNA Splice Sites
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
86Subventions
Organisme : Fondation de France
ID : 200412859
Organisme : Groupement des Entreprises Françaises dans la Lutte contre le Cancer
ID : Gefluc, # R18064EE
Organisme : Association Nationale de la Recherche et de la Technologie
ID : (#2015/0335
Organisme : NHMRC Senior Research Fellowship
ID : 1061779
Références
Mol Cell. 2003 Jul;12(1):5-14
pubmed: 12887888
Nucleic Acids Res. 2001 Jan 1;29(1):255-9
pubmed: 11125105
Cold Spring Harb Perspect Biol. 2011 Jul 01;3(7):null
pubmed: 21441581
PLoS Comput Biol. 2010 Nov 24;6(11):e1001016
pubmed: 21124863
Bioinformatics. 2019 Oct 16;:null
pubmed: 31617569
Bioinformatics. 2017 Oct 15;33(20):3166-3172
pubmed: 28633445
Genome Res. 2015 Feb;25(2):290-303
pubmed: 25561518
Nucleic Acids Res. 2018 Jan 4;46(D1):D754-D761
pubmed: 29155950
Bioinformatics. 2018 Mar 15;34(6):920-927
pubmed: 29092009
RNA. 2018 Dec;24(12):1647-1658
pubmed: 30224349
Nucleic Acids Res. 2016 Jan 4;44(D1):D733-45
pubmed: 26553804
Bioinformatics. 2004 Aug 4;20 Suppl 1:i69-76
pubmed: 15262783
Genome Biol. 2018 Jun 1;19(1):71
pubmed: 29859120
Nat Genet. 2008 Dec;40(12):1413-5
pubmed: 18978789
J Appl Genet. 2018 Aug;59(3):253-268
pubmed: 29680930
Bioinformatics. 2014 Apr 1;30(7):923-30
pubmed: 24227677
Hum Mutat. 2012 Aug;33(8):1228-38
pubmed: 22505045
Genome Biol. 2019 Mar 1;20(1):48
pubmed: 30823901
J Comput Biol. 2004;11(2-3):377-94
pubmed: 15285897
BMC Bioinformatics. 2017 Oct 24;18(1):459
pubmed: 29065858
Nucleic Acids Res. 2018 Nov 30;46(21):11656-11657
pubmed: 30321405
Hum Mutat. 2016 Jun;37(6):564-9
pubmed: 26931183
Wiley Interdiscip Rev RNA. 2013 Jan-Feb;4(1):49-60
pubmed: 23044818
Genome Res. 2002 Jun;12(6):996-1006
pubmed: 12045153
FEBS Lett. 2005 Mar 28;579(9):1900-3
pubmed: 15792793
Hum Mutat. 2006 Aug;27(8):803-13
pubmed: 16835862
Nat Struct Mol Biol. 2019 Oct;26(10):930-940
pubmed: 31570875
Bioinformatics. 2013 Jan 1;29(1):15-21
pubmed: 23104886
Nucleic Acids Res. 2008 Apr;36(7):2257-67
pubmed: 18285363
Nucleic Acids Res. 2009 May;37(9):e67
pubmed: 19339519
Eur J Hum Genet. 2017 Oct;25(10):1147-1154
pubmed: 28905878
Genome Biol. 2006;7(1):R1
pubmed: 16507133
Mol Cell Biol. 1993 Aug;13(8):4939-52
pubmed: 8336728
Genes Dev. 2018 Apr 1;32(7-8):577-591
pubmed: 29666160