SNPlice: Variants That Modulate Intron Retention from RNA-sequencing Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2014 Dec 7

PMID 25481010

Citations 12

Authors

Prakriti Mudvari

Mercedeh Movassagh

Kamran Kowsari

Ali Seyfi

Maria Kokkinaki

Nathan J Edwards

Nady Golestaneh

Anelia Horvath

Affiliations

Soon will be listed here.

Abstract

Rationale: The growing recognition of the importance of splicing, together with rapidly accumulating RNA-sequencing data, demand robust high-throughput approaches, which efficiently analyze experimentally derived whole-transcriptome splice profiles.

Results: We have developed a computational approach, called SNPlice, for identifying cis-acting, splice-modulating variants from RNA-seq datasets. SNPlice mines RNA-seq datasets to find reads that span single-nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-occurrence of variants and molecules that remain unspliced at nearby exon-intron boundaries. Hence, SNPlice highlights variants preferentially occurring on intron-containing molecules, possibly resulting from altered splicing. To illustrate co-occurrence of variant nucleotide and exon-intron boundary, allele-specific sequencing was used. SNPlice results are generally consistent with splice-prediction tools, but also indicate splice-modulating elements missed by other algorithms. SNPlice can be applied to identify variants that correlate with unexpected splicing events, and to measure the splice-modulating potential of canonical splice-site SNVs.

Availability And Implementation: SNPlice is freely available for download from https://code.google.com/p/snplice/ as a self-contained binary package for 64-bit Linux computers and as python source-code.

Contact: pmudvari@gwu.edu or horvatha@gwu.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

Pan-Cancer Profiling of Intron Retention and Its Clinical Significance in Diagnosis and Prognosis.

Huang L, Zeng X, Ma H, Yang Y, Akimoto Y, Wei G Cancers (Basel). 2023; 15(23).

PMID: 38067392 PMC: 10705125. DOI: 10.3390/cancers15235689.

Association of the fibronectin type III domain-containing protein 5 rs1746661 single nucleotide polymorphism with reduced brain glucose metabolism in elderly humans.

Lima-Filho R, Benedet A, De Bastiani M, Povala G, Cozachenco D, Ferreira S Brain Commun. 2023; 5(4):fcad216.

PMID: 37601408 PMC: 10438215. DOI: 10.1093/braincomms/fcad216.

Genetic association study of intron variants in the forkhead box protein P3 gene in Chinese patients diagnosed with cervical cancer.

Shi F, Pang X, Li G, Chen Z, Dong M, Wang J J Cell Mol Med. 2022; 26(9):2658-2672.

PMID: 35322929 PMC: 9077298. DOI: 10.1111/jcmm.17276.

rMAPS2: an update of the RNA map analysis and plotting server for alternative splicing regulation.

Hwang J, Jung S, Kook T, Rouchka E, Bok J, Park J Nucleic Acids Res. 2020; 48(W1):W300-W306.

PMID: 32286627 PMC: 7319468. DOI: 10.1093/nar/gkaa237.

Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data.

N M P, Liu H, Bousounis P, Spurr L, Alomran N, Ibeawuchi H Genes (Basel). 2020; 11(3).

PMID: 32106453 PMC: 7140866. DOI: 10.3390/genes11030240.

References

Woolfe A, Mullikin J, Elnitski L . Genomic features defining exonic variants that modulate splicing. Genome Biol. 2010; 11(2):R20. PMC: 2872880. DOI: 10.1186/gb-2010-11-2-r20. View

Faber K, Glatting K, Mueller P, Risch A, Hotz-Wagenblatt A . Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites. BMC Bioinformatics. 2011; 12 Suppl 4:S2. PMC: 3194194. DOI: 10.1186/1471-2105-12-S4-S2. View

Barbosa-Morais N, Irimia M, Pan Q, Xiong H, Gueroussov S, Lee L . The evolutionary landscape of alternative splicing in vertebrate species. Science. 2012; 338(6114):1587-93. DOI: 10.1126/science.1230612. View

Moore M, Silver P . Global analysis of mRNA splicing. RNA. 2007; 14(2):197-203. PMC: 2212243. DOI: 10.1261/rna.868008. View

Han H, Irimia M, Ross P, Sung H, Alipanahi B, David L . MBNL proteins repress ES-cell-specific alternative splicing and reprogramming. Nature. 2013; 498(7453):241-5. PMC: 3933998. DOI: 10.1038/nature12270. View

Cheung V, Spielman R, Ewens K, Weber T, Morley M, Burdick J . Mapping determinants of human gene expression by regional and genome-wide association. Nature. 2005; 437(7063):1365-9. PMC: 3005311. DOI: 10.1038/nature04244. View

ElSharawy A, Hundrieser B, Brosch M, Wittig M, Huse K, Platzer M . Systematic evaluation of the effect of common SNPs on pre-mRNA splicing. Hum Mutat. 2009; 30(4):625-32. DOI: 10.1002/humu.20906. View

Ray D, Kazan H, Chan E, Pena Castillo L, Chaudhry S, Talukder S . Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat Biotechnol. 2009; 27(7):667-70. DOI: 10.1038/nbt.1550. View

Brunak S, Engelbrecht J, Knudsen S . Prediction of human mRNA donor and acceptor sites from the DNA sequence. J Mol Biol. 1991; 220(1):49-65. DOI: 10.1016/0022-2836(91)90380-o. View

10.

Clark F, Thanaraj T . Categorization and characterization of transcript-confirmed constitutively and alternatively spliced introns and exons from human. Hum Mol Genet. 2002; 11(4):451-64. DOI: 10.1093/hmg/11.4.451. View

11.

Barash Y, Calarco J, Gao W, Pan Q, Wang X, Shai O . Deciphering the splicing code. Nature. 2010; 465(7294):53-9. DOI: 10.1038/nature09000. View

12.

Cavaloc Y, Bourgeois C, Kister L, Stevenin J . The splicing factors 9G8 and SRp20 transactivate splicing through different and specific enhancers. RNA. 1999; 5(3):468-83. PMC: 1369774. DOI: 10.1017/s1355838299981967. View

13.

Braunschweig U, Gueroussov S, Plocik A, Graveley B, Blencowe B . Dynamic integration of splicing within gene regulatory pathways. Cell. 2013; 152(6):1252-69. PMC: 3642998. DOI: 10.1016/j.cell.2013.02.034. View

14.

Pan Q, Shai O, Lee L, Frey B, Blencowe B . Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet. 2008; 40(12):1413-5. DOI: 10.1038/ng.259. View

15.

Trapnell C, Williams B, Pertea G, Mortazavi A, Kwan G, van Baren M . Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010; 28(5):511-5. PMC: 3146043. DOI: 10.1038/nbt.1621. View

16.

GART J, Zweifel J . On the bias of various estimators of the logit and its variance with application to quantal bioassay. Biometrika. 1967; 54(1):181-7. View

17.

Kamath U, Compton J, Islamaj-Dogan R, De Jong K, Shehu A . An evolutionary algorithm approach for feature generation from sequence data and its application to DNA splice site prediction. IEEE/ACM Trans Comput Biol Bioinform. 2012; 9(5):1387-98. DOI: 10.1109/TCBB.2012.53. View

18.

Piva F, Giulietti M, Burini A, Principato G . SpliceAid 2: a database of human splicing factors expression data and RNA target motifs. Hum Mutat. 2011; 33(1):81-5. DOI: 10.1002/humu.21609. View

19.

Yeo G, Burge C . Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol. 2004; 11(2-3):377-94. DOI: 10.1089/1066527041410418. View

20.

Merkin J, Russell C, Chen P, Burge C . Evolutionary dynamics of gene and isoform regulation in Mammalian tissues. Science. 2012; 338(6114):1593-9. PMC: 3568499. DOI: 10.1126/science.1228186. View