Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data.
BACKGROUND
Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy.


RESULTS
We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development.


METHODS
Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2.


BACKGROUND
Supplementary data are available at Bioinformatics online.
University of Pennsylvania, Institute for Translational Medicine and Therapeutics, Philadelphia, PA 19104.
Katharina E
KE
Hayer
Scientific Computing at Amazon Web Services, Seattle, WA 98108.
Angel
A
Pizarro
Department of Pharmacology and.
Nicholas F
NF
Lahens
Department of Pharmacology and.
John B
JB
Hogenesch
University of Pennsylvania, Institute for Translational Medicine and Therapeutics, Philadelphia, PA 19104, Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Gregory R
GR
Grant
D000465:Algorithms; D017398:Alternative Splicing; D000818:Animals; D019985:Benchmarking; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D051379:Mice; D059368:RNA Isoforms / Q000032:analysis*; D012333:RNA, Messenger / Q000032:analysis; D017423:Sequence Analysis, RNA / Q000379:methods*
D059368:RNA Isoforms; D012333:RNA, Messenger