A differential k-mer analysis pipeline for comparing RNA-Seq transcriptome and meta-transcriptome datasets without a reference View Full Text


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Article Info

DATE

2019-03

AUTHORS

Chon-Kit Kenneth Chan, Nedeljka Rosic, Michał T. Lorenc, Paul Visendi, Meng Lin, Paulina Kaniewska, Brett J. Ferguson, Peter M. Gresshoff, Jacqueline Batley, David Edwards

ABSTRACT

Next-generation DNA sequencing technologies, such as RNA-Seq, currently dominate genome-wide gene expression studies. A standard approach to analyse this data requires mapping sequence reads to a reference and counting the number of reads which map to each gene. However, for many transcriptome studies, a suitable reference genome is unavailable, especially for meta-transcriptome studies which assay gene expression from mixed populations of organisms. Where a reference is unavailable, it is possible to generate a reference by the de novo assembly of the sequence reads. However, the high cost of generating high-coverage data for de novo assembly hinders this approach and more importantly the accurate assembly of such data is challenging, especially for meta-transcriptome data, and resulting assemblies frequently suffer from collapsed regions or chimeric sequences. As an alternative to the standard reference mapping approach, we have developed a k-mer-based analysis pipeline (DiffKAP) to identify differentially expressed reads between RNA-Seq datasets without the requirement for a reference. We compared the DiffKAP approach with the traditional Tophat/Cuffdiff method using RNA-Seq data from soybean, which has a suitable reference genome. We subsequently examined differential gene expression for a coral meta-transcriptome where no reference is available, and validated the results using qRT-PCR. We conclude that DiffKAP is an accurate method to study differential gene expression in complex meta-transcriptomes without the requirement of a reference genome. More... »

PAGES

363-371

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10142-018-0647-3

    DOI

    http://dx.doi.org/10.1007/s10142-018-0647-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110226645

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30483906


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