A benchmark for RNA-seq quantification pipelines

Teng, Mingxiang; Love, Michael I.; Davis, Carrie A.; Djebali, Sarah; Dobin, Alexander; Graveley, Brenton R.; Li, Sheng; Mason, Christopher E.; Olson, Sara; Pervouchine, Dmitri D.; Sloan, Cricket A.; Wei, Xintao; Zhan, Lijun; Irizarry, Rafael A.

doi:10.1186/s13059-016-0940-1

Cited by 172 publications

(144 citation statements)

References 41 publications

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“…The discovered novel transcripts can then be used or added to existing annotations for quantification. However, recent reviews indicate that the quantification algorithms provided by these tools are not on par with tools from the previous category [20,53]. This is because identification of isoforms from RNA-seq data is far from being solved and is still challenging, due in particular to the incomplete nature of RNA-seq reads and the fact that the number of potential candidate isoforms is very large, growing almost exponentially with the number of exons.…”

Section: Isoform Quantification In Conjunction With Novel Transcriptmentioning

confidence: 93%

Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

Zhang¹,

Zhang²,

Vincent³

et al. 2016

Next Generat Sequenc & Applic

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In recent years, RNA-seq has emerged as a powerful technology in estimation of gene or transcript expression. 'Union-exon' and transcript based approaches are widely used in gene quantification. The 'Union-exon' based approach is simple, but it does not distinguish between isoforms when multiple alternatively spliced transcripts are expressed from the same gene. Because a gene is expressed in one or more transcript isoforms, the transcript based approach is more biologically meaningful than the 'union exon'-based approach. However, estimating the expression of individual isoform is intrinsically challenging because different isoforms of a gene usually have a high proportion of genomic overlap. Recently, a number of tools have been developed for RNA-seq isoform quantification. We review those methods and their main features to serve as guidance for users to choose suitable tools for their specific projects.

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Section: Isoform Quantification In Conjunction With Novel Transcriptmentioning

confidence: 93%

Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

Zhang¹,

Zhang²,

Vincent³

et al. 2016

Next Generat Sequenc & Applic

View full text Add to dashboard Cite

show abstract

“…The problem has already been addressed in the literature, and R packages have been produced that combine several RNA-seq analysis methods, such as sRAP [13], metaseqR [14], rnaseqcomp [15] or SARTools2 [16].…”

Section: Introductionmentioning

confidence: 99%

DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

Gayte¹,

Moreno²,

Zonjic³

et al. 2017

Genomics Comput Biol

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Differential gene expression based on RNA-seq is widely used.Bioinformatics skills are required since no algorithm is appropriate for all experimental designs.Moreover, when working with organisms without reference genome, functional analysis is less than straightforward in most situations. DEgenes Hunter, an attempt to automate the process, is based on two independent scripts, one for differential expression and one for functional interpretation. Based on replicates, the R script decides which of the edgeR, DEseq2, NOISeq and limma algorithms are appropriate. It performs quality control calculations and provides the prevalent, most reliable, set of differentially expressed genes, and lists all other possible candidates for further functional interpretation. It also provides a combined P-value that allows differentially expressed genes ranking. It has been tested with synthetic and real-world datasets, showing in both cases ease of use and reliable results. With real data, DEgenes Hunter offers straightforward functional interpretation.

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“…The performances are found to be very similar for all algorithms. Teng et al [65] described several evaluation metrics and compared 7 quantiication algorithms and reported that Flux Capacitor and eXpress underperformed, while RSEM outperformed other methods. We believe that RSEMs accuracy may result from its ability to properly handling short transcripts, poly (A) tails and the reads that map to multiple genes.…”

Section: Current Approaches For Transcript Quantiication From Rna-seqmentioning

confidence: 99%

Transcriptome Analysis for Non-Model Organism: Current Status and Best-Practices

Eldem¹,

Zararsız²,

Taşçi³

et al. 2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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Since transcriptome analysis provides genome-wide sequence and gene expression information, transcript reconstruction using RNA-Seq sequence reads has become popular during recent years. For non-model organism, as distinct from the reference genome-based mapping, sequence reads are processed via de novo transcriptome assembly approaches to produce large numbers of contigs corresponding to coding or non-coding, but expressed, part of genome. In spite of immense potential of RNA-Seq-based methods, particularly in recovering full-length transcripts and spliced isoforms from short-reads, the accurate results can be only obtained by the procedures to be taken in a step-by-step manner. In this chapter, we aim to provide an overview of the state-of-the-art methods including (i) quality check and pre-processing of raw reads, (ii) the pros and cons of de novo transcriptome assemblers, (iii) generating non-redundant transcript data, (iv) current quality assessment tools for de novo transcriptome assemblies, (v) approaches for transcript abundance and diferential expression estimations and inally (vi) further mining of transcriptomic data for particular biological questions. Our intention is to provide an overview and practical guidance for choosing the appropriate approaches to best meet the needs of researchers in this area and also outline the strategies to improve on-going projects.

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A benchmark for RNA-seq quantification pipelines

Cited by 172 publications

References 41 publications

Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

Transcriptome Analysis for Non-Model Organism: Current Status and Best-Practices

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