Pan-Cancer Repository of Validated Natural and Cryptic mRNA Splicing Mutations

Shirley, Ben C.; Mucaki, Eliseos J.; Rogan, Peter K.

doi:10.1101/474452

Cited by 6 publications

(8 citation statements)

References 25 publications

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“…Exon-based expression microarrays and q-RT-PCR techniques were initially used to confirm the predicted impact of common and rare SNPs on splicing. Results were subsequently confirmed using RNAseq data for some of these SNPs Dorman et al 2014, Shirley et al 2019. However, exon skipping due to rs1893592 was not consistently seen in all carriers.…”

Section: Discussionmentioning

confidence: 81%

“…The extent to which SNP-related sequence variation accounts for the heterogeneity in mRNA transcript structures has been somewhat unappreciated, given the relatively high proportion of genes that exhibit tissue-specific alternative splicing Pan et al 2008;Baralle et al 2017). This and our previous study (Shirley et al 2019) raise questions regarding the degree to which apparent alternative splicing is the result of genomic polymorphism rather than splicing regulation alone. Because much of the information required for splice site recognition resides within neighboring introns, it would be prudent to consider contributions from intronic and exonic polymorphism that produce structural mRNA variation, since these changes might be associated with disease or predisposition.…”

Section: Discussionmentioning

confidence: 83%

“…The previous analyses were extended to include 24 additional, common SNPs for their potential influence on splicing. All SNVs present in ICGC (International Cancer Genome Consortium) patients (Shirley et al 2019) were evaluated by the Shannon Pipeline (SP; Shirley et al 2013) to identify those altering splice site strength. Common SNPs (average heterozygosity > 10% in dbSNP 150) predicted to decrease natural splice site strength by SP (where Δ R i < -1 bit) were selected.…”

Section: Pcr and Quantitative Rt-pcr M-mlv Reverse Transcriptase (Inmentioning

confidence: 99%

“…Nevertheless, splicing information changes resulting from SNPs corresponded to observed alternative and/or other novel splice isoforms. We then queried the ValidSpliceMut database for these SNPs, as abnormal splicing was only flagged in the database when the junction-read or read-abundance counts significantly exceeded corresponding evidence type in a large set of normal control samples (Shirley et al 2019).…”

Section: Pcr and Quantitative Rt-pcr M-mlv Reverse Transcriptase (Inmentioning

confidence: 99%

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Expression changes confirm genomic variants predicted to result in allele-specific, alternative mRNA splicing

Mucaki

2019

Preprint

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Splice isoform structure and abundance can be affected by either non-coding or masquerading coding variants that alter the structure or abundance of transcripts. When these variants are common in the population, these non-constitutive transcripts are sufficiently frequent so as to resemble naturally occurring, alternative mRNA splicing. Prediction of the effects of such variants has been shown to be accurate using information theory-based methods. Single nucleotide polymorphisms (SNPs) predicted to significantly alter natural and/or cryptic splice site strength were shown to affect gene expression. Splicing changes for known SNP genotypes were confirmed in HapMap lymphoblastoid cell lines with gene expression microarrays and custom designed q-RT-PCR or TaqMan assays. The majority of these SNPs (15 of 22) as well as an independent set of 24 variants were then subjected to RNAseq analysis using the ValidSpliceMut web beacon (http://validsplicemut.cytognomix.com), which is based on data from the Cancer Genome Atlas and International Cancer Genome Consortium. SNPs from different genes analyzed with gene expression microarray and q-RT-PCR exhibited significant changes in affected splice site use. Thirteen SNPs directly affected exon inclusion and 10 altered cryptic site use. Homozygous SNP genotypes resulting in stronger splice sites exhibited higher levels of processed mRNA than alleles associated with weaker sites. Four SNPs exhibited variable expression among individuals with the same genotypes, masking statistically significant expression differences between alleles. Genome-wide information theory and expression analyses (RNAseq) in tumour exomes and genomes confirmed splicing effects for 7 of the HapMap SNP and 14 SNPs identified from tumour genomes. q-RT-PCR resolved rare splice isoforms with read abundance too low for statistical significance in ValidSpliceMut.Nevertheless, the web-beacon provides evidence of unanticipated splicing outcomes, for example, intron retention due to compromised recognition of constitutive splice sites. Thus, ValidSpliceMut and q-RT-PCR represent complementary resources for identification of allelespecific, alternative splicing. KeywordsAllele-specific gene expression, mRNA splicing, RT-PCR, gene expression microarray, RNAseq, single nucleotide polymorphism, mutation, cryptic splicing; intron retention; alternative splicing, information theory Accurate and comprehensive methods are needed for predicting impact of non-coding mutations, in particular, mRNA splicing defects, which are prevalent in genetic disease (Krawczak et al. 1992;Teraoka et al. 1999;Ars et al. 2003;Spielmann and Mundlos, 2016;Gloss and Dinger, 2018). This class of mutations may account for as much as 62% of point mutations (López-Bigas et al. 2005). Large transcriptome studies have suggested that a large fraction of genome-wide association studies (GWAS) signals for disease and complex traits are due to SNPs affecting mRNA splicing (Park et al. 2018). ValidSpliceMut (Shirley et al. 2019) presents evidence of altere...

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 83%

Section: Pcr and Quantitative Rt-pcr M-mlv Reverse Transcriptase (Inmentioning

confidence: 99%

Section: Pcr and Quantitative Rt-pcr M-mlv Reverse Transcriptase (Inmentioning

confidence: 99%

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Expression changes confirm genomic variants predicted to result in allele-specific, alternative mRNA splicing

Mucaki

2019

Preprint

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“…42 Shirley et al showed that 341 486 variants had a significant impact on mRNA splicing, and approximately 70% of these variants were not registered in the dbSNP database. 43,44 Furthermore, these studies could analyze all genes and identify novel splicing variants because of integrated analyses using raw sequence data such as FASTQ or BAM files. In studies using TCGA datasets, one of the things to be considered is how to handle data from normal samples because tumor matched normal samples are a lot fewer than only tumor samples in TCGA.…”

Section: Discussionmentioning

confidence: 99%

Massive computational identification of somatic variants in exonic splicing enhancers using The Cancer Genome Atlas

2019

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Owing to the development of next‐generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine‐rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open‐access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA‐seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE‐disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE‐disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE‐disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE‐disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE‐disrupting nonsense variants tended to escape nonsense‐mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE‐disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon‐junction read counts from the tumor samples.

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RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Kc¹,

Feng²,

Ramu³

et al. 2018

Preprint

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The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, this analysis strategy excludes somatic mutations in non-coding regions of the genome and even exonic mutations may have unidentified non-coding consequences. Here we present RegTools (www.regtools.org), a free, open-source software package designed to integrate analysis of somatic variant calls from genomic data with splice junctions extracted from transcriptomic data in order to efficiently identify variants that may cause aberrant splicing in tumors.ContributionsY.-Y.F. was involved in all aspects of this study, including designing methodology, developing and testing the tool software, analyzing and interpreting data, and writing the manuscript, with input from A.R., K.C.C, Z.L.S., J.K., D.F.C., O.L.G., and M.G. A.R. designed the tool and led software development efforts. Y.L., W.C.C., R.U., and R.G. provided unpublished tumor datasets and provided critical feedback on the manuscript. O.G. and M.G. supervised the study. All authors read and approved the final manuscript

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Pan-Cancer Repository of Validated Natural and Cryptic mRNA Splicing Mutations

Cited by 6 publications

References 25 publications

Expression changes confirm genomic variants predicted to result in allele-specific, alternative mRNA splicing

Expression changes confirm genomic variants predicted to result in allele-specific, alternative mRNA splicing

Massive computational identification of somatic variants in exonic splicing enhancers using The Cancer Genome Atlas

RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

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