Analysis and design of RNA sequencing experiments for identifying isoform regulation

Abstract

Through alternative splicing, most human genes express multiple isoforms that often differ in function. To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), we developed the mixture-of-isoforms (MISO) model, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates. Incorporation of mRNA fragment length distribution in paired-end RNA-seq greatly improved estimation of alternative-splicing levels. MISO also detects differentially regulated exons or isoforms. Application of MISO implicated the RNA splicing factor hnRNP H1 in the regulation of alternative cleavage and polyadenylation, a role that was supported by UV crosslinking-immunoprecipitation sequencing (CLIP-seq) analysis in human cells. Our results provide a probabilistic framework for RNA-seq analysis, give functional insights into pre-mRNA processing and yield guidelines for the optimal design of RNA-seq experiments for studies of gene and isoform expression.The distinct isoforms expressed from metazoan genes through alternative splicing can be important in development, differentiation and disease 1 . For example, the pyruvate kinase gene produces two distinct tissue-specific spliced isoforms that differ in their enzymatic activity, allosteric regulation and ability to support tumor growth 2 . Conservative estimates predict 2-12 mRNA isoforms for most mammalian genes ( Supplementary Fig. 1), though some genes, including neurexins, may express more than 1,000 isoforms each 3 .© 2010 Nature America, Inc. All rights reserved.Correspondence should be addressed to E.M.A. (airoldi@fas.harvard.edu) or C.B.B. (cburge@mit.edu). AUTHOR CONTRIBUTIONS Y.K., development of MISO model and software, analyses involving MISO, writing of main text and methods; E.T.W., hnRNP H CLIP-seq experiments and associated computational analyses, CUGBP1 knockdown RNA-seq experiments and associated computational analyses; E.M.A., development of model and statistical analysis, writing of methods; C.B.B., development of MISO model, contributions to computational analyses, writing of main text. Recently, high-throughput sequencing of short cDNA fragments, RNA-seq, has emerged as a powerful approach to characterizing the transcriptome. RNA-seq data have recently been used to show that the vast majority of human genes are alternatively spliced and that most alternative exons show tissue-specific regulation 4 . To date, RNA-seq analysis methods have focused mostly on estimation of gene expression levels and discovery of novel exons and genes 4-6 , assembly and annotation of mRNA transcripts 5,7 , and estimation of the expression levels of alternative exons 4 . Two recent methods, Cufflinks and Scripture, can produce de novo annotations of transcripts in metazoan genomes using RNA-seq data alone [8][9][10] . COMPETING FINANCIAL INTERESTSAccurate quantification of alternative-exon abundance and detection of differentially regulated exons and isoforms remain challenging. Paired...

High-throughput analyses of hnRNP H1 dissects its multi-functional aspect

“…This protein tends to form a heterodimer with hnRNP F and shows preference for G-rich tracts of RNA. 2,[6][7][8] It has also been shown that hnRNP H1 interacts with the DGCR8 complex and facilitates microRNA processing in HeLa cells. 9 The impact of hnRNP H1 binding on alternative splicing has been associated with several diseases, such as cystic fibrosis, 10 congenital myasthemic syndrome, 11 and amyotrophic lateral sclerosis (ALS).…”

“…18 In addition to these targeted analyses, genome-wide studies have revealed hnRNP H1s role, in conjunction with hnRNP F, in promoting both exon inclusion and skipping. 8 Other recent studies have linked hnRNP H1 to alternative cleavage and polyadenylation, 6 and established the cooperative roles of the hnRNP proteins in the regulation of splicing. 2 By employing a combined strategy with 4 different high-throughput approaches, we were able to provide a more refined and accurate picture for hnRNP H1 in splicing regulation and extend our understanding of its participation in polyadenylation and mRNA decay.…”

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

“…Compared to microarrays, RNA expression profile has two majorly advantageous features to enable to identify complexity of isoform variability on a single gene to produce various splicing events on transcripts and another nature, allelic specific expression and allelic imbalance resulted from genetic differences in transcriptional rates (Beretta et al, 2014;Bernard et al, 2014;Deng et al, 2011;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Katz et al, 2010;Kaur et al, 2012;Kimes et al, 2014;Leon-Novelo et al, 2014;Lerch et al, 2012;Li and Jiang, 2012;Ma and Zhang, 2013;Mezlini et al, 2013;Mills et al, 2013;Nariai et al, 2013;Nariai et al, 2014;Ng et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Pandey et al, 2013;Patro et al, 2014;Rehrauer et al, 2013;Safikhani et al, 2013;Shi and Jiang, 2013;Suo et al, 2014;Trapnell et al, 2010;Vardhanabhuti et al, 2013;Wang et al, 2010;Wu et al, 2011b;Yalamanchili et al, 2014;Zhang et al, 2014;Zheng and Chen, 2009). …”

Robust identification of Ptbp1-dependent splicing events by a junction-centric approach in Xenopus laevis

“…This normalization allows to focus on splicing patterns rather than on transcript levels. To do so, MISO integrates the number of reads aligning to the alternative exon with the number of junctional reads linking it to neighboring exons, with the numbers of junctional reads excluding it, and with the number of reads in the immediately neighboring exons (Katz et al, 2010). SpliceTrap generates an exon-trio database (all the possible 3 consecutive exons in the annotation), generates two isoforms for each trio (with or without the middle exon) and quantifies the relative abundances of the two isoforms to infer middle exon usage (Wu et al, 2011).…”