Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems

Marc Robinson-Rechavi (@marc_rr) tweeted about this great new paper in BMC Bioinformatics by Ruolin Liu, Ann Loraine, and Julie Dickerson. From the abstract:

The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals.

Like so many other areas of bioinformatics, there are many methods available for detecting alternative splicing, and it is far from clear which — if any —  is the best. This paper attempts to compare eight of them, and the abstract contains a sobering conclusion:

No single method performs the best in all situations

Figure 5 from the paper is especially depressing. It looks at the overlap of differentially spliced genes as detected by five different methods. There are zero differentially spliced genes that all methods agreed on.

Liu et al. BMC Bioinformatics 2014 15:364   doi:10.1186/s12859-014-0364-4