Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data

Quinn, Emma M.; Cormican, Paul; Kenny, Elaine; Hill, Matthew; Anney, Richard; Gill, Michael; Corvin, Aiden; Morris, Derek W.

doi:10.1371/journal.pone.0058815

Cited by 120 publications

(106 citation statements)

References 44 publications

Supporting

Mentioning

104

Contrasting

Unclassified

Order By: Relevance

“…To increase the confidence of somatic mutation calling, a minimum base Phred quality score of 20 [20] is used to filter out low quality reads and bases. A recent report suggests that only >10X coverage is required to ensure 89% accuracy and 92% sensitivity for single nucleotide variations (SNVs) [21]. Thus, a minimum depth of 10 from both tumor data and normal data is required for GLMVC to consider the candidate base.…”

Section: Methodsmentioning

confidence: 99%

Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Sheng

Zhao

et al. 2016

Genomics

View full text Add to dashboard Cite

Traditionally, somatic mutations are detected by examining DNA sequence. The maturity of sequencing technology has allowed researchers to screen for somatic mutations in the whole genome. Increasingly, researchers have become interested in identifying somatic mutations through RNAseq data. With this motivation, we evaluated the practicability of detecting somatic mutations from RNAseq data. Current somatic mutation calling tools were designed for DNA sequencing data. To increase performance on RNAseq data, we developed a somatic mutation caller GLMVC based on bias reduced generalized linear model for both DNA and RNA sequencing data. Through comparison with MuTect and Varscan we showed that GLMVC performed better for somatic mutation detection using exome sequencing or RNAseq data. GLMVC is freely available for download at the following website: https://github.com/shengqh/GLMVC/wiki.

show abstract

Section: Methodsmentioning

confidence: 99%

Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Sheng

Zhao

et al. 2016

Genomics

View full text Add to dashboard Cite

show abstract

“…These QC steps have been shown to reduce the likelihood of false positives and biases in observed mutant allele frequencies during mutation calling using RNA-seq data. 15 We calculated allelic counts at each single-nucleotide variant position in each sample using the SAMtools mpileup function with default parameters, except for the "-A" option and the "-l" option to specify the list of mutation coordinates, which we took, for each sample, from the list of validated mutations in our prior study. 9 We recorded the number of overlapping sequences containing the mutant and wild-type (WT) allele, as identified in the previous study for each position and sample.…”

mentioning

confidence: 99%

Differential and limited expression of mutant alleles in multiple myeloma

Rashid

Sperling

Bolli

et al. 2014

Blood

View full text Add to dashboard Cite

Key Points• The majority of mutations are found in genes that have low or no detectable biological expression.• Mutated genes often show differential allelic expression in multiple myeloma patient samples.Recent work has delineated mutational profiles in multiple myeloma and reported a median of 52 mutations per patient, as well as a set of commonly mutated genes across multiple patients. In this study, we have used deep sequencing of RNA from a subset of these patients to evaluate the proportion of expressed mutations. We find that the majority of previously identified mutations occur within genes with very low or no detectable expression. On average, 27% (range, 11% to 47%) of mutated alleles are found to be expressed, and among mutated genes that are expressed, there often is allele-specific expression where either the mutant or wild-type allele is suppressed. Even in the absence of an overall change in gene expression, the presence of differential allelic expression within malignant cells highlights the important contribution of RNA-sequencing in identifying clinically significant mutational changes relevant to our understanding of myeloma biology and also for therapeutic applications. (Blood. 2014;124(20):3110-3117) IntroductionMultiple myeloma (MM) is an incurable neoplastic disease involving the proliferation of monoclonal antibody producing plasma cells. 1MM is a heterogeneous disease but the hallmark genetic changes include several genomic rearrangements, such as translocations involving the IgH locus or hyperdiploidy. 2 The pathogenic molecular changes and the processes that drive genomic instability during the development and evolution of the disease are complex and incompletely understood. Elucidating the precise genetic changes that drive malignant transformation, affect the phenotypic behavior of the disease, and alter treatment response is an essential component of our drive toward individualized therapy.3 Recent studies have focused on attempts to identify individual driver mutations that might provide both prognostic information and unique therapeutic targets. Whole genome and whole exome sequencing of increasingly large numbers of patient samples have identified a number of commonly mutated genes in MM patients, such as CCND1, NRAS, KRAS, BRAF, TP53, and FAM46C. [4][5][6][7][8] However, none of these mutations are found in more than one quarter of patients, and most are found in less than 10% of samples sequenced.We recently reported a large cohort of MM exome sequences involving 84 samples from 67 patients. Of these patients, 15 contributed samples from multiple time points during disease evolution. 9 We again identified a diverse set of gene mutations with significant heterogeneity across our cohort, with a median of 52 (range, 21-488) mutations per sample. Computational approaches can be used to prioritize mutations that are expected to alter protein structure and function, or to lie in likely driver genes. It is more challenging to determine which mutations are likely to be clinically meani...

show abstract

“…Preservation of the reading frame ensures correct calling of gene structure (16–18) and SNPs, whether in, for example, the realignment phase of an exome sequencing pipeline (19), single-nucleotide polymorphism (SNP) calling from existing RNA-seq data (20,21) or amplicon-based analyses such as human immunodeficiency virus (HIV) drug resistance genotyping (22). When aligning coding DNA generated using high-throughput sequencing platforms, it is critical that codons present in the open reading frame remain intact and that codon-sized insertions and deletions are recognized and called correctly, as distinct from both genuine frameshifts and single indels created through sequencing error.…”

Section: Introductionmentioning

confidence: 99%

RAMICS: trainable, high-speed and biologically relevant alignment of high-throughput sequencing reads to coding DNA

Wright

Travers

2014

Nucleic Acids Res

View full text Add to dashboard Cite

The challenge presented by high-throughput sequencing necessitates the development of novel tools for accurate alignment of reads to reference sequences. Current approaches focus on using heuristics to map reads quickly to large genomes, rather than generating highly accurate alignments in coding regions. Such approaches are, thus, unsuited for applications such as amplicon-based analysis and the realignment phase of exome sequencing and RNA-seq, where accurate and biologically relevant alignment of coding regions is critical. To facilitate such analyses, we have developed a novel tool, RAMICS, that is tailored to mapping large numbers of sequence reads to short lengths (<10 000 bp) of coding DNA. RAMICS utilizes profile hidden Markov models to discover the open reading frame of each sequence and aligns to the reference sequence in a biologically relevant manner, distinguishing between genuine codon-sized indels and frameshift mutations. This approach facilitates the generation of highly accurate alignments, accounting for the error biases of the sequencing machine used to generate reads, particularly at homopolymer regions. Performance improvements are gained through the use of graphics processing units, which increase the speed of mapping through parallelization. RAMICS substantially outperforms all other mapping approaches tested in terms of alignment quality while maintaining highly competitive speed performance.

show abstract

Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data

Cited by 120 publications

References 44 publications

Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Differential and limited expression of mutant alleles in multiple myeloma

RAMICS: trainable, high-speed and biologically relevant alignment of high-throughput sequencing reads to coding DNA

Contact Info

Product

Resources

About