Identification of protein coding regions in RNA transcripts

TLDR: It is demonstrated that the GeneMarkS-T self-training is robust with respect to the presence of errors in assembled transcripts and accuracy of GeneMarkT in identification of protein-coding regions and, particularly, in prediction of gene starts compares favorably to other existing methods.

Abstract: Massive parallel sequencing of RNA transcripts by the next generation technology (RNA-Seq) is a powerful method of generating critically important data for discovery of structure and function of eukaryotic genes. The transcripts may or may not carry protein-coding regions. If protein coding region is present, it should be a continuous (spliced) open reading frame. Gene finding in transcripts can be done by statistical (alignment-free) as well as by alignment based methods. We describe a new tool, GeneMarkS-T, for ab initio identification of protein-coding regions, complete or incomplete, in RNA transcripts assembled from RNA-Seq reads. Important feature of GeneMarkS-T is unsupervised estimation of parameters of the algorithm that makes unnecessary several conventional steps used in the gene prediction protocols, most importantly the manually curated preparation of training sets. We demonstrate that i/the GeneMarkS-T self-training is robust with respect to the presence of errors in assembled transcripts and ii/accuracy of GeneMarkS-T in identification of protein-coding regions and, particularly, in prediction of gene starts compares favorably to other existing methods.