RNA-seq alignment – where methodological progress can still occur

Many tools are available for RNA-seq alignment and expression quantification, with comparative value being hard to establish. Benchmarking assessments often highlight methods’ good performance, but are focused on either model data or fail to explain variation in performance. This leaves us to ask, what is the most meaningful way to assess different alignment choices? And importantly, where is there room for progress? Researchers from the Cold Spring Harbor Laboratory explore the answers to these two questions by performing an exhaustive assessment of the STAR aligner. They assess STAR’s performance across a range of alignment parameters using common metrics, and then on biologically focused tasks. They find technical metrics such as fraction mapping or expression profile correlation to be uninformative, capturing properties unlikely to have any role in biological discovery. Surprisingly, the researchers find that changes in alignment parameters within a wide range have little impact on both technical and biological performance. Yet, when performance finally does break, it happens in difficult regions, such as X-Y paralogs and MHC genes. They believe improved reporting by developers will help establish where results are likely to be robust or fragile, providing a better baseline to establish where methodological progress can still occur.

Summarizing RNA-seq alignment tools

rna-seq

(A) RNA-seq alignment: typical experiment + sources of error. (B) Transcriptomics asks three broad questions which include transcript detection, differential gene expression and gene co-expression. (C) Growth and performance of RNA-seq tools – cumulative number of tools shows steady growth across all tool types.

Ballouz S, Dobin A, Gingeras TR, Gillis J. (2018) The fractured landscape of RNA-seq alignment: the default in our STARs. Nucleic Acids Res [Epub ahead of print]. [article]

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