Accounting for fragments of unexpected origin improves transcript quantification in RNA-seq simulations focused on increased realism

AbstractTranscript and gene quantification is the first step in many RNA-seq analyses. While many factors and properties of experimental RNA-seq data likely contribute to differences in accuracy between various approaches to quantification, it has been demonstrated (1) that quantification accuracy generally benefits from considering, during alignment, potential genomic origins for sequenced fragments that reside outside of the annotated transcriptome.Recently, Varabyou et al. (2) demonstrated that the presence of transcriptional noise leads to systematic errors in the ability of tools — particularly annotation-based ones — to accurately estimate transcript expression. Here, we confirm the findings of Varabyou et al. (2) using the simulation framework they have provided. Using the same data, we also examine the methodology of Srivastava et al.(1) as implemented in recent versions of salmon (3), and show that it substantially enhances the accuracy of annotation-based transcript quantification in these data.

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Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples

GigaScience ◽

10.1093/gigascience/giz145 ◽

2019 ◽

Vol 8 (12) ◽

Cited By ~ 6

Author(s):

Hong Zheng ◽

Kevin Brennan ◽

Mikel Hernaez ◽

Olivier Gevaert

Keyword(s):

Rna Sequencing ◽

Ground Truth ◽

The Cancer Genome Atlas ◽

Rna Seq ◽

Optimal Method ◽

Lncrna Expression ◽

Transcript Quantification ◽

Gene Quantification ◽

Non Coding Rnas ◽

Expression Quantification

Abstract Background Long non-coding RNAs (lncRNAs) are emerging as important regulators of various biological processes. While many studies have exploited public resources such as RNA sequencing (RNA-Seq) data in The Cancer Genome Atlas to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification. Results In this study, we compared the performance of pseudoalignment methods Kallisto and Salmon, alignment-based transcript quantification method RSEM, and alignment-based gene quantification methods HTSeq and featureCounts, in combination with read aligners STAR, Subread, and HISAT2, in lncRNA quantification, by applying them to both un-stranded and stranded RNA-Seq datasets. Full transcriptome annotation, including protein-coding and non-coding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment methods and RSEM outperform HTSeq and featureCounts for lncRNA quantification at both sample- and gene-level comparison, regardless of RNA-Seq protocol type, choice of aligners, and transcriptome annotation. Pseudoalignment methods and RSEM detect more lncRNAs and correlate highly with simulated ground truth. On the contrary, HTSeq and featureCounts often underestimate lncRNA expression. Antisense lncRNAs are poorly quantified by alignment-based gene quantification methods, which can be improved using stranded protocols and pseudoalignment methods. Conclusions Considering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.

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Faculty Opinions recommendation of Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.14267340.15779565 ◽

2012 ◽

Author(s):

Marylyn Ritchie ◽

Stephen Turner

Keyword(s):

Expression Analysis ◽

Transcript Expression ◽

Rna Seq ◽

Differential Gene

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Union Exon Based Approach for RNA-Seq Gene Quantification: To Be or Not to Be?

PLoS ONE ◽

10.1371/journal.pone.0141910 ◽

2015 ◽

Vol 10 (11) ◽

pp. e0141910 ◽

Cited By ~ 16

Author(s):

Shanrong Zhao ◽

Li Xi ◽

Baohong Zhang

Keyword(s):

Rna Seq ◽

Gene Quantification

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Alignment and mapping methodology influence transcript abundance estimation

10.1101/657874 ◽

2019 ◽

Cited By ~ 6

Author(s):

Avi Srivastava ◽

Laraib Malik ◽

Hirak Sarkar ◽

Mohsen Zakeri ◽

Fatemeh Almodaresi ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Computational Cost ◽

Simulated Data ◽

Transcript Abundance ◽

Mapping Method ◽

Rna Seq ◽

Transcript Quantification ◽

Quantification Model

AbstractBackgroundThe accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.ResultsWe investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large, and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally-acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.ConclusionWe observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification.

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A novel min-cost flow method for estimating transcript expression with RNA-Seq

BMC Bioinformatics ◽

10.1186/1471-2105-14-s5-s15 ◽

2013 ◽

Vol 14 (S5) ◽

Cited By ~ 33

Author(s):

Alexandru I Tomescu ◽

Anna Kuosmanen ◽

Romeo Rizzi ◽

Veli Mäkinen

Keyword(s):

Transcript Expression ◽

Rna Seq ◽

Flow Method ◽

Cost Flow ◽

Min Cost Flow

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BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

BMC Genomics ◽

10.1186/s12864-019-6243-7 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 8

Author(s):

Paulo Rapazote-Flores ◽

Micha Bayer ◽

Linda Milne ◽

Claus-Dieter Mayer ◽

John Fuller ◽

...

Keyword(s):

Gene Expression ◽

Splice Junction ◽

Rna Seq ◽

Rt Pcr ◽

High Quality ◽

Transcript Quantification ◽

Reference Transcript ◽

Alternatively Spliced ◽

Time Required ◽

Comprehensive Reference

Abstract Background The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants. Results A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427–433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20–28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5′ and 3′ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage. Conclusion A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

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Platform-integrated mRNA isoform quantification

Bioinformatics ◽

10.1093/bioinformatics/btz932 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2466-2473 ◽

Cited By ~ 2

Author(s):

Jiao Sun ◽

Jae-Woong Chang ◽

Teng Zhang ◽

Jeongsik Yong ◽

Rui Kuang ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Large Scale ◽

Cancer Cell Lines ◽

Supplementary Information ◽

Rna Seq ◽

Mrna Quantification ◽

Transcript Quantification ◽

Isoform Expression ◽

Isoform Quantification

Abstract Motivation Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. Results In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary information Supplementary data are available at Bioinformatics online.

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CoCo: RNA-seq read assignment correction for nested genes and multimapped reads

Bioinformatics ◽

10.1093/bioinformatics/btz433 ◽

2019 ◽

Vol 35 (23) ◽

pp. 5039-5047 ◽

Cited By ~ 6

Author(s):

Gabrielle Deschamps-Francoeur ◽

Vincent Boivin ◽

Sherif Abou Elela ◽

Michelle S Scott

Keyword(s):

Supplementary Information ◽

Rna Seq ◽

Non Coding Rna ◽

Abundance Estimates ◽

Gene Coverage ◽

Nested Genes ◽

Quantification Accuracy ◽

Whole Transcriptome Analysis ◽

Whole Transcriptome ◽

Generation Sequencing

Abstract Motivation Next-generation sequencing techniques revolutionized the study of RNA expression by permitting whole transcriptome analysis. However, sequencing reads generated from nested and multi-copy genes are often either misassigned or discarded, which greatly reduces both quantification accuracy and gene coverage. Results Here we present count corrector (CoCo), a read assignment pipeline that takes into account the multitude of overlapping and repetitive genes in the transcriptome of higher eukaryotes. CoCo uses a modified annotation file that highlights nested genes and proportionally distributes multimapped reads between repeated sequences. CoCo salvages over 15% of discarded aligned RNA-seq reads and significantly changes the abundance estimates for both coding and non-coding RNA as validated by PCR and bedgraph comparisons. Availability and implementation The CoCo software is an open source package written in Python and available from http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco. Supplementary information Supplementary data are available at Bioinformatics online.

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