RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data

Movassagh, Mercedeh; Alomran, Nawaf; Mudvari, Prakriti; Dede, Merve; Dede, Cem; Kowsari, Kamran; Restrepo, Paula; Cauley, Edmund; Bahl, Sonali; Li, Muzi; Waterhouse, Wesley; Tsaneva-Atanasova, Krasimira; Edwards, Nathan; Horvath, Anélia

doi:10.1093/nar/gkw757

Cited by 11 publications

(18 citation statements)

References 49 publications

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“…The gene counts were then used to remove cells with low quality data, defined as less than 3,000 detected genes and/or mitochondrial genes' expression higher than 6% of the total gene expression. We estimated VAFRNA on the individual alignments from the cells with high quality data using ReadCounts [26] with three different thresholds for minimum required number of reads (minR): minR = 10, minR = 5, and minR = 3.…”

Section: Scrna-seq Data Processingmentioning

confidence: 99%

“…Second, in contrast to the (static) genotypes, VAFRNA is dynamic and reflects the actual allele content in the system at any particular time, which allows for the assessment of dynamic and progressive processes [23][24][25]. Importantly, through primarily reflecting genetic variation, VAFRNA is an essential component of the genomic interactome and plays a major role in phenotype formation [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

Liu

Bousounis

et al. 2019

Preprint

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With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono-and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show biallelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3'-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

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Section: Scrna-seq Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

Liu

Bousounis

et al. 2019

Preprint

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“…The human genome reference (hg38)-aligned sequencing reads (Binary Alignment Maps, .bams) were downloaded from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/) and processed downstream through an in-house pipeline. After variant call (Li, 2011), the RNA and DNA alignments, together with the variant lists were processed through the read count module of the package RNA2DNAlign (Movassagh, et al, 2016), to produce variant and reference sequencing read counts for all the variant positions in all four sequencing signals (normal exome, normal transcriptome, tumour exome and tumour transcriptome). Selected read count assessments were visually examined using Integrative Genomics Viewer (Thorvaldsdóttir, et al, 2013).…”

Section: Data Processingmentioning

confidence: 99%

“…In diploid organisms, an important measure of genetic variation is the allele frequency, which can be measured from both genome (DNA) and transcriptome (RNA) sequencing data (encoded and expressed allele frequency, respectively). Differential DNA-RNA allele frequencies are associated with a variety of biological processes, such as genome admixture or allele-specific transcriptional regulation (Ferreira, et al, 2016;Ha, et al, 2012;Han, et al, 2015;Movassagh, et al, 2016;Shah, et al, 2012).…”

mentioning

confidence: 99%

“…Most of the RNA-DNA allele comparisons from sequencing have been approached at nucleotide level, where it has proven to be highly informative for determining the alleles' functionality (Ferreira, et al, 2016;Ha, et al, 2012;Han, et al, 2015;Macaulay, et al, 2016;Morin, et al, 2013;Movassagh, et al, 2016;Reuter, et al, 2016;Shah, et al, 2012;Shi, et al, 2016;Shlien, et al, 2016;Yang, et al, 2016). Comparatively, integration of allele signals at the molecular level, as derived from linear DNA and RNA carriers, is less explored due to challenges presented by short sequencing length.…”

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confidence: 99%

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GeTallele: a method for integrative analysis and visualization of DNA and RNA allele frequencies

Słowiński

Restrepo

et al. 2018

Preprint

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Background: Asymmetric allele expression typically indicates functional and/or structural features associated with the underlying genetic variants. When integrated, RNA and DNA allele frequencies can reveal patterns characteristic of a wide-range of biological traits, including ploidy changes, genome admixture, allele-specific expression and gene-dosage transcriptional response. Results:To assess RNA and DNA allele frequencies from matched sequencing datasets, we introduce GeTallele: a mathematical model and a toolbox that provides a suit of functions for integrative analysis, statistical assessment and visualization of Genome and Transcriptome allele frequencies. The toolbox introduces a method for generating model distributions of variant allele frequencies (VAF) with a given variant read probability. Using model VAF probabilities, GeTallele allows estimation and comparison of variant read probabilities (VAF distributions) in a sequencing data sets. We demonstrate this functionality across cancer DNA and RNA sequencing data sets. Conclusion:Based on our evaluation, variant read probabilities can serve as a dependable indicator to assess gene and chromosomal allele asymmetries and to aid calls of genomic events in matched sequencing RNA and DNA datasets. Contact: P.M.Slowinski@exeter.ac.uk positions of interest, mathematically and statistically compares VAFRNA and VAFDNA distributions. Furthermore, GeTallele supports visualization of the allele distribution at a desired resolution-from nucleotide to genome. In contrast to other CNA-modelling methods based on statistical models, GeTallele is based on a mechanistic model of VAF distributions.

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Overexpressed somatic alleles are enriched in functional elements in Breast Cancer

Restrepo

Movassagh

Alomran

et al. 2017

Sci Rep

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Asymmetric allele content in the transcriptome can be indicative of functional and selective features of the underlying genetic variants. Yet, imbalanced alleles, especially from diploid genome regions, are poorly explored in cancer. Here we systematically quantify and integrate the variant allele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired through The Cancer Genome Atlas (TCGA). We test for correlation between allele prevalence and functionality in known cancer-implicated genes from the Cancer Gene Census (CGC). We document significant allele-preferential expression of functional variants in CGC genes and across the entire dataset. Notably, we find frequent allele-specific overexpression of variants in tumor-suppressor genes. We also report a list of over-expressed variants from non-CGC genes. Overall, our analysis presents an integrated set of features of somatic allele expression and points to the vast information content of the asymmetric alleles in the cancer transcriptome.

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RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data

Cited by 11 publications

References 49 publications

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

GeTallele: a method for integrative analysis and visualization of DNA and RNA allele frequencies

Overexpressed somatic alleles are enriched in functional elements in Breast Cancer

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