Identifying differential isoform abundance with RATs: a universal tool and a warning

Froussios, Kimon; Mourão, Kira; Schurch, Nicholas J.; Barton, Geoffrey J.

doi:10.1101/132761

Cited by 10 publications

(7 citation statements)

References 41 publications

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“…We assessed two other methods for DTU, SUPPA2 38 and RATs 39 , both of which can take Salmon quantifications as input. For statistical testing of DTU, SUPPA2 computes, for a given transcript, the difference in proportion across condition and the differences in proportion seen between biological replicates.…”

Section: Discussionmentioning

confidence: 99%

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

2018

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

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Section: Discussionmentioning

confidence: 99%

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

2018

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“…Instead of more time consuming read alignments, it uses a k -mer approach for quantifying the abundance of transcripts in RNA-seq experiments [ 18 ]. More recently, two R packages, sleuth and RATs (Relative Abundance of Transcripts), were developed that exploit the bootstrap estimates from kallisto to identify events of differential transcript expression and differential transcript usage, respectively [ 19 , 20 ]. Differential transcript expression (DTE) is any change in the relative abundance of a transcript between two conditions.…”

Section: Introductionmentioning

confidence: 99%

Global isoform-specific transcript alterations and deregulated networks in clear cell renal cell carcinoma

2018

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Extensive genome-wide analyses of deregulated gene expression have now been performed for many types of cancer. However, most studies have focused on deregulation at the gene-level, which may overlook the alterations of specific transcripts for a given gene. Clear cell renal cell carcinoma (ccRCC) is one of the best-characterized and most pervasive renal cancers, and ccRCCs are well-documented to have aberrant RNA processing. In the present study, we examine the extent of aberrant isoform-specific RNA expression by reporting a comprehensive transcript-level analysis, using the new kallisto-sleuth-RATs pipeline, investigating coding and non-coding differential transcript expression in ccRCC. We analyzed 50 ccRCC tumors and their matched normal samples from The Cancer Genome Altas datasets. We identified 7,339 differentially expressed transcripts and 94 genes exhibiting differential transcript isoform usage in ccRCC. Additionally, transcript-level coexpression network analyses identified vasculature development and the tricarboxylic acid cycle as the most significantly deregulated networks correlating with ccRCC progression. These analyses uncovered several uncharacterized transcripts, including lncRNAs FGD5-AS1 and AL035661.1, as potential regulators of the tricarboxylic acid cycle associated with ccRCC progression. As ccRCC still presents treatment challenges, our results provide a new resource of potential therapeutics targets and highlight the importance of exploring alternative methodologies in transcriptome-wide studies.

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“…Note that estimating gene abundances by summing counts from the underlying transcripts can be problematic, as discussed in [18]. RATs [19] on the other hand is among those methods that target to capture such behavior and tests for differential transcript usage (DTU). Regardless of the testing objective, both tests entirely depend on the transcript abundances that were obtained from algorithms like EM during the quantification step to resolve the ambiguity of the multimapped reads, which adherently requires some bias-correction modeling ( [8]) adding another layer of complexity to achieve the final goal of gene analysis.…”

Section: Segment-based Gene Expression Analysismentioning

confidence: 99%

Fast and interpretable alternative splicing and differential gene-level expression analysis using transcriptome segmentation with Yanagi

Mount

2018

Preprint

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Introduction: Analysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, besides, the annotated transcripts are often a small subset of the possible transcripts of a gene. Here we describe Yanagi, a tool for segmenting transcriptome to create a library of maximal L-disjoint segments from a complete transcriptome annotation. That segment library preserves all transcriptome substrings of length L and transcripts structural relationships while eliminating unnecessary sequence duplications. Contributions: In this paper, we formalize the concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens and provide gene-level visualization of the segments for better interpretability. Then we demonstrate the use of segments-level quantification into gene expression and alternative splicing analysis. The notion of transcript segmentation as introduced here and implemented in Yanagi opens the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of RNA-seq analyses. Conclusion: Using segment library rather than the standard transcriptome succeeds in significantly reducing ambigious alignments where reads are multimapped to several sequences in the reference. That allowed avoiding the quantification step required by standard kmer-based pipelines for gene expression analysis. Moreover, using segment counts as statistics for alternative splicing analysis enables achieving comparable performance to counting-based approaches (e.g. rMATS) while rather using fast and lighthweight pseudo alignment.

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Identifying differential isoform abundance with RATs: a universal tool and a warning

Cited by 10 publications

References 41 publications

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

Global isoform-specific transcript alterations and deregulated networks in clear cell renal cell carcinoma

Fast and interpretable alternative splicing and differential gene-level expression analysis using transcriptome segmentation with Yanagi

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