SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines

Audoux, Jérôme; Salson, Mikaël; Grosset, Christophe; Beaumeunier, Sacha; Holder, Jean-Marc; Commes, Thérèse; Naveilhan, Philippe

doi:10.1186/s12859-017-1831-5

Cited by 11 publications

(9 citation statements)

References 34 publications

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“…The correctness of alignment programs are crucial to the accuracy of the downstream analyses. Unfortunately, previous studies have shown that while these tools have low false positive rates, they do not necessarily have low false negative rates [3,1]. This means that while many of the reads were likely to be correctly aligned, there are still many incorrectly unaligned reads which should have been aligned.…”

Section: Introductionmentioning

confidence: 99%

“…This means that while many of the reads were likely to be correctly aligned, there are still many incorrectly unaligned reads which should have been aligned. These incorrectly unaligned reads, or false negative non-alignments, adversely affect the accuracy of the alignment produced and can also affect the result of downstream analyses, such as variant calling, indel (insertion-deletion) detection and gene fusion detection [1].…”

Section: Introductionmentioning

confidence: 99%

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Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

Yang

Tang

Troup

et al. 2018

Preprint

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Motivation: Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for further downstream analysis. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align reads which should have been aligned, a problem we termed as the false-negative non-alignment problem.Results: We have developed Scavenger, a pipeline for recovering unaligned reads using a novel mechanism which utilises information from aligned reads. Scavenger performs recovery of unaligned reads by re-aligning unaligned reads against a putative location derived from aligned reads with sequence similarity against unaligned reads. We show that Scavenger can successfully recover unaligned reads in both simulated and real RNA-seq datasets, including single-cell RNA-seq data. The reads recovered contain more genetic variants compared to previously aligned reads, indicating that divergence between personal and reference genome plays a role in the false-negative non-alignment problem. We also explored the impact of read recovery on downstream analysis, in particular gene expression analysis, and showed that Scavenger is able to both recover genes which were previously non-expressed and also increase gene expression, with lowly expressed genes having the most impact from the addition of recovered reads. We also found that the majority of genes with >1 fold change in expression after recovery are of pseudogenes category, indicating that pseudogenes expression can be substantially affected by the false-negative non-alignment problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

Yang

Tang

Troup

et al. 2018

Preprint

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“…To the best of our knowledge, the programs described for such a simulation of mutations are SIM-CT ( Audoux et al , 2017 ), SVsim ( https://github.com/GregoryFaust/SVsim ), shuffleseq ( http://emboss.sourceforge.net/apps/release/6.2/emboss/apps/shuffleseq.html ), simuG ( Yue and Liti, 2019 ) and Simulome ( Price and Gibas, 2017 ). Compared to Mutation-Simulator, these tools are either limited in the type of genomes they can operate on (Simulome), limited in their field of use (e.g.…”

Section: Introductionmentioning

confidence: 99%

Mutation-Simulator: fine-grained simulation of random mutations in any genome

2020

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Summary Mutation-Simulator allows the introduction of various types of sequence alterations in reference sequences, with reasonable compute-time even for large eukaryotic genomes. Its intuitive system for fine-grained control over mutation rates along the sequence enables the mimicking of natural mutation patterns. Using standard file formats for input and output data, it can easily be integrated into any development and benchmarking workflow for high-throughput sequencing applications. Availability and implementation Mutation-Simulator is written in Python 3 and the source code, documentation, help and use cases are available on the Github page at https://github.com/mkpython3/Mutation-Simulator. It is free for use under the GPL 3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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“…The correctness of alignment programs are crucial to the accuracy of the downstream analyses. Unfortunately, previous studies have shown that while these tools have low false positive rates, they do not necessarily have low false negative rates 8,9 . This means that while many of the reads were likely to be correctly aligned, there are still many incorrectly unaligned reads which should have been aligned.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

et al. 2019

View full text Add to dashboard Cite

Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align all the reads which should have been aligned, a problem we termed as the false-negative non-alignment problem. Here we present Scavenger, a python-based bioinformatics pipeline for recovering unaligned reads using a novel mechanism in which a putative alignment location is discovered based on sequence similarity between aligned and unaligned reads. We showed that Scavenger could recover unaligned reads in a range of simulated and real RNA-seq datasets, including single-cell RNA-seq data. We found that recovered reads tend to contain more genetic variants with respect to the reference genome compared to previously aligned reads, indicating that divergence between personal and reference genomes plays a role in the false-negative non-alignment problem. Even when the number of recovered reads is relatively small compared to the total number of reads, the addition of these recovered reads can impact downstream analyses, especially in terms of estimating the expression and differential expression of lowly expressed genes, such as pseudogenes.

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SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines

Cited by 11 publications

References 34 publications

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

Mutation-Simulator: fine-grained simulation of random mutations in any genome

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

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