Effect of method of deduplication on estimation of differential gene expression using RNA-seq

Klepikova, Anna V.; Kasianov, Artem S.; Chesnokov, Mikhail S.; Лазаревич, Н. Л.; Penin, Aleksey A.; Logacheva, Maria D.

doi:10.7717/peerj.3091

Cited by 21 publications

(19 citation statements)

References 33 publications

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“…Fig.S6) showing that other sources of noise were major contributors to AI overdispersion. Deduplication can lead to loss of large amounts of legitimate data, and may have other undesirable impacts, such as distorting signal distribution in the biological sample [35][36][37] . Thus, from a practical standpoint, read deduplication has limited utility, and its impact on AI overdispersion is accounted for in the QCC analysis.…”

Section: Sources Of Ai Overdispersionmentioning

confidence: 99%

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Unexpected variability of allelic imbalance estimates from RNA sequencing

Mendelevich

Vinogradova

Gupta

et al. 2020

Preprint

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RNA sequencing and other experimental methods that produce large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques have not been fully understood. We assessed the reproducibility of allele-specific expression measurements by conducting replicate sequencing experiments from the same RNA sample. Surprisingly, variation in the estimates of allelic imbalance (AI) between technical replicates was up to 7-fold higher than expected from commonly applied noise models. We show that AI overdispersion varies substantially between replicates and between experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allelespecific gene expression RNA sequencing (RNA-seq) is a widely used technology for measuring RNA abundance across the whole transcriptome 1 . An especially informative approach to RNA-seq analysis in samples from humans and other diploid organisms is comparison of the activity of the parental alleles. Allele-specific analysis of gene expression can reveal epigenetic gene regulation associated with imprinting 2 , X-chromosome inactivation 3 , and autosomal monoallelic expression 4-8 . The maternal and paternal copies of a gene share the same cell nucleus and therefore are both influenced by the rest of the genome in the same way. Consequently, allelic imbalance (AI) in expression can be highly sensitive to cis-regulatory mechanisms 9,10 . Accordingly, AI analysis has been used to uncover gene regulatory effects in a growing number of studies [11][12][13][14] .Accurate estimation of AI is thus important for quantitative understanding of genetic and epigenetic mechanisms of gene regulation. Efforts to increase AI estimation accuracy have mostly focused on the data analysis 15-20 , based on the assumption that consistency in measuring total RNA abundance translates to accurate measurement of each of the alleles separately. Here, we show that this implicit assumption is incorrect.Based on analysis of newly generated datasets and publicly available data, we reveal a previously neglected, major source of technical variation in AI measurements in RNA-seq experiments. This indicates that analyses based on a single RNA-seq replicate and aiming to identify genes with significant AI can result in an unexpectedly large fraction of false positives. To correct for this issue, we devised a specific measure of AI overdispersion, Quality Correction Constant (QCC), derived from comparison of replicate RNA-seq libraries. QCC can be used to control for this unexpected source of variation, and its use results in more accurate AI estimates. Finally, we outline several use c...

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Section: Sources Of Ai Overdispersionmentioning

confidence: 99%

“…Detailed analysis of specific protocols is outside the scope of this work. However, one common concern in deep sequencing experiments is the impact of PCR amplification artifacts [35][36][37] .…”

Section: Sources Of Ai Overdispersionmentioning

confidence: 99%

Unexpected variability of allelic imbalance estimates from RNA sequencing

Mendelevich

Vinogradova

Gupta

et al. 2020

Preprint

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“…Duplicate reads (light green) can include technical artifacts as well as reads that represent true transcript abundance. Removal of duplicate reads reduces false positives but also causes false negatives (23). We include duplicate reads in gene expression quantification.…”

Section: Composition Of Rna-seq Datasetsmentioning

confidence: 99%

The case for using Mapped Exonic Non-Duplicate (MEND) read counts in RNA-Seq experiments: examples from pediatric cancer datasets

et al. 2019

Preprint

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BackgroundThe clinical value of identifying aberrant gene expression in tumors is becoming increasingly evident. In order for multi-gene expression analysis to achieve wider adoption and eventually be developed as a Clinical Laboratory Improvement Amendments (CLIA)-approved test, the input sample requirements, sensitivity, specificity and reference ranges must be quantified. MethodsWe analyzed paired-end Illumina RNA sequencing (RNA-Seq) data from 1088 tumor samples from 29 projects. We categorized reads based on where and how well they map to the genome, as well as their PCR duplicate status. We subsampled 5 deeply sequenced samples, identified exceptionally highly expressed genes and samples with similar gene expression profiles. Results 2We addressed variability in RNA-Seq dataset composition by defining reference ranges for four types of reads found in sequencing data: unmapped (0-13%); mapped duplicate (2-66%); mapped non exonic (0-26%) and mapped, exonic, non-duplicate (MEND,. With 20 million MEND reads, we detected over-expressed genes ("up-outlier" genes) with a median sensitivity of 96.1% and specificity of 99.8%; sample similarity had 96.6% sensitivity and 100.0% specificity. ConclusionsThis strategy for measuring RNA-Seq data content and identifying thresholds could be applied to a clinical test of a single sample, specifying minimum inputs and defining the sensitivity and specificity. We estimate that a sample sequenced to the depth of 70 million total reads will typically have sufficient data for accurate gene expression analysis.

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“…These methods include the use of exogenous molecular barcodes (unique molecular identifiers, UMIs), endogenous position-based method, sequencing technical replicates [20,[22][23][24][25][26] and background error modeling [20,26,27]. The UMI strategy is an effective way to remove stochastic sequencing errors [20,[28][29][30] and duplicates, which can improve the accuracy of lowfrequency variant detection and solve severe quantitative bias in RNA-seq [31]. However, UMIs' universal application is limited by their experimental design [32].…”

Section: Introductionmentioning

confidence: 99%

“…The endogenous position-based method is an alternative way to deal with duplicates and remove errors. Modules in popular tools such as SAMtools [33] and Picard (http:// broadinstitute.github.io/picard/) use this approach to mark duplicates, select a representative read and further improve calling results and RNA quantification [31]. However, these tools are based on 5′ prime position of a read and do not use full segment information.…”

Section: Introductionmentioning

confidence: 99%

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

Deng

Xu³

et al. 2020

BMC Bioinformatics

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Background: Hybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelicfraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based 'liquid biopsy'. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations. Results: Based on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted. Conclusions: Due to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.

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Effect of method of deduplication on estimation of differential gene expression using RNA-seq

Cited by 21 publications

References 33 publications

Unexpected variability of allelic imbalance estimates from RNA sequencing

Unexpected variability of allelic imbalance estimates from RNA sequencing

The case for using Mapped Exonic Non-Duplicate (MEND) read counts in RNA-Seq experiments: examples from pediatric cancer datasets

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

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