Fast and accurate approximate inference of transcript expression from RNA-seq data

AbstractMotivationThe biological importance of changes in gene and transcript expression is well recognised and is reflected by the wide variety of tools available to characterise these changes. Regulation via Differential Transcript Usage (DTU) is emerging as an important phenomenon. Several tools exist for the detection of DTU from read alignment or assembly data, but options for detection of DTU from alignment-free quantifications are limited.ResultsWe present an R package named RATs – (Relative Abundance of Transcripts) – that identifies DTU transcriptome-wide directly from transcript abundance estimations. RATs is agnostic to quantification methods and exploits bootstrapped quantifications, if available, to inform the significance of detected DTU events. RATs contextualises the DTU results and shows good False Discovery performance (median FDR ≤0.05) at all replication levels. We applied RATs to a human RNA-seq dataset associated with idiopathic pulmonary fibrosis with three DTU events validated by qRT-PCR. RATs found all three genes exhibited statistically significant changes in isoform proportions based on Ensembl v60 annotations, but the DTU for two were not reliably reproduced across bootstrapped quantifications. RATs also identified 500 novel DTU events that are enriched for eleven GO terms related to regulation of the response to stimulus, regulation of immune system processes, and symbiosis/parasitism. Repeating this analysis with the Ensembl v87 annotation showed the isoform abundance profiles of two of the three validated DTU genes changed radically. RATs identified 414 novel DTU events that are enriched for five GO terms, none of which are in common with those previously identified. Only 141 of the DTU evens are common between the two analyses, and only 8 are among the 248 reported by the original study. Furthermore, the original qRT-PCR probes no longer match uniquely to their original transcripts, calling into question the interpretation of these data. We suggest parallel full-length isoform sequencing, annotation pre-filtering and sequencing of the transcripts captured by qRT-PCR primers as possible ways to improve the validation of RNA-seq results in future experiments.AvailabilityThe package is available through Github at https://github.com/bartongroup/Rats.

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A Novel Combinatorial Method for Estimating Transcript Expression with RNA-Seq: Bounding the Number of Paths

Lecture Notes in Computer Science - Algorithms in Bioinformatics ◽

10.1007/978-3-642-40453-5_8 ◽

2013 ◽

pp. 85-98 ◽

Cited By ~ 2

Author(s):

Alexandru I. Tomescu ◽

Anna Kuosmanen ◽

Romeo Rizzi ◽

Veli Mäkinen

Keyword(s):

Transcript Expression ◽

Rna Seq ◽

Combinatorial Method

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HCV Activates Somatic L1 Retrotransposition—A Potential Hepatocarcinogenesis Pathway

Cancers ◽

10.3390/cancers13205079 ◽

2021 ◽

Vol 13 (20) ◽

pp. 5079

Author(s):

Praveen D. Sudhindar ◽

Daniel Wainwright ◽

Santu Saha ◽

Rachel Howarth ◽

Misti McCain ◽

...

Keyword(s):

Viral Clearance ◽

Hcv Rna ◽

Transcript Expression ◽

Healthy Controls ◽

Rna Seq ◽

Common Cause ◽

Damage Repair ◽

Huh7 Cells ◽

Chronic Hcv

Hepatitis C virus (HCV) is a common cause of hepatocellular carcinoma (HCC). The activation and mutagenic consequences of L1 retrotransposons in virus-associated-HCC have been documented. However, the direct influence of HCV upon L1 elements is unclear, and is the focus of the present study. L1 transcript expression was evaluated in a publicly available liver tissue RNA-seq dataset from patients with chronic HCV hepatitis (CHC), as well as healthy controls. L1 transcript expression was significantly higher in CHC than in controls. L1orf1p (a L1 encoded protein) expression was observed in six out of 11 CHC livers by immunohistochemistry. To evaluate the influence of HCV on retrotransposition efficiency, in vitro engineered-L1 retrotransposition assays were employed in Huh7 cells in the presence and absence of an HCV replicon. An increased retrotransposition rate was observed in the presence of replicating HCV RNA, and persisted in cells after viral clearance due to sofosbuvir (PSI7977) treatment. Increased retrotransposition could be due to dysregulation of the DNA-damage repair response, including homologous recombination, due to HCV infection. Altogether these data suggest that L1 expression can be activated before oncogenic transformation in CHC patients, with HCV-upregulated retrotransposition potentially contributing to HCC genomic instability and a risk of transformation that persists post-viral clearance.

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PM-Seq: Using Finite Poisson Mixture Models for RNA-Seq Data Analysis and Transcript Expression Level Quantification

Statistics in Biosciences ◽

10.1007/s12561-012-9070-9 ◽

2012 ◽

Vol 5 (1) ◽

pp. 71-87

Author(s):

Han Wu ◽

Zhaohui Qin ◽

Yu Zhu

Keyword(s):

Data Analysis ◽

Mixture Models ◽

Expression Level ◽

Transcript Expression ◽

Rna Seq ◽

Poisson Mixture ◽

Transcript Expression Level

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Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq

Advances in Statistical Bioinformatics ◽

10.1017/cbo9781139226448.006 ◽

2013 ◽

pp. 105-125

Author(s):

Zhaonan Sun ◽

Han Wu ◽

Zhaohui Qin ◽

Yu Zhu

Keyword(s):

Expression Level ◽

Transcript Expression ◽

Rna Seq ◽

Model Based ◽

Transcript Expression Level

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Multitask regression for condition-specific prioritization of miRNA targets in transcripts

10.7287/peerj.preprints.2377v1 ◽

2016 ◽

Author(s):

Azim Dehghani Amirabad ◽

Marcel H Schulz

Keyword(s):

Mirna Target ◽

Small Sample Size ◽

Mirna Gene ◽

Simulated Data ◽

Target Prediction ◽

Small Sample ◽

Specific Information ◽

Transcript Expression ◽

Rna Seq ◽

Mirna Targets

Deregulation of miRNAs is implicated in many diseases in particular cancer, where miRNAs can act as tumour suppressors or oncogenes. As sequence-based miRNA target predictions do not provide condition-specific information, many algorithms combine expression data for miRNAs and genes for prioritization of miRNA targets. However, common strategies prioritize miRNA-gene as- sociations, although a miRNA may only target a subset of the alternative transcripts produced by a gene. Thus, current approaches are suboptimal. Here we address the problem of transcript and not gene based miRNA target prioritization. We show how to leverage methods that were developed for gene expression based miRNA-target prioritization for transcripts. In addition, we introduce a new multitasking based learning (MTL) method that uses structured-sparsity inducing regularization to improve accuracy of the learning. The new MTL approach performs especially favorable in small sample size settings, for genes with many transcripts and with noisy transcript expression level es- timates as shown with simulated data. In an analysis of real liver cancer RNA-seq data we show that the MTL approach better predicts transcript expression and outperforms simpler approaches for miRNA-target prediction.

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