Identifiability of isoform deconvolution from junction arrays and RNA-Seq.
BACKGROUND
Splice junction microarrays and RNA-seq are two popular ways of quantifying splice variants within a cell. Unfortunately, isoform expressions cannot always be determined from the expressions of individual exons and splice junctions. While this issue has been noted before, the extent of the problem on various platforms has not yet been explored, nor have potential remedies been presented.


RESULTS
We propose criteria that will guarantee identifiability of an isoform deconvolution model on exon and splice junction arrays and in RNA-Seq. We show that up to 97% of 2256 alternatively spliced human genes selected from the RefSeq database lead to identifiable gene models in RNA-seq, with similar results in mouse. However, in the Human Exon array only 26% of these genes lead to identifiable models, and even in the most comprehensive splice junction array only 69% lead to identifiable models.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Hiller
David
D
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
Jiang
Hui
H
Xu
Weihong
W
Wong
Wing Hung
WH
D017398:Alternative Splicing; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020033:Protein Isoforms / Q000235:genetics* / Q000378:metabolism; D012313:RNA / Q000737:chemistry* / Q000378:metabolism
D020033:Protein Isoforms; D012313:RNA