Year: 2013
Type: article
Abstract: Accurate mapping of spliced RNA-Seq reads to genomic DNA has been known as a challenging problem. Despite significant efforts invested in developing efficient algorithms, with the human genome as a primary focus, the best solution is still not known. A recently introduced tool, TrueSight, has demonstrated better performance compared with earlier developed algorithms such as TopHat and MapSplice. To improve detection of splice junctions, TrueSight uses information on statistical patterns of nucleotide ordering in intronic and exonic DNA. This line of research led to yet another new algorithm, UnSplicer, designed for eukaryotic species with compact genomes where functional alternative splicing is likely to be dominated by splicing noise. Genome-specific parameters of the new algorithm are generated by GeneMark-ES, an ab initio gene prediction algorithm based on unsupervised training. UnSplicer shares several components with TrueSight; the difference lies in the training strategy and the classification algorithm. We tested UnSplicer on RNA-Seq data sets of Arabidopsis thaliana, Caenorhabditis elegans, Cryptococcus neoformans and Drosophila melanogaster. We have shown that splice junctions inferred by UnSplicer are in better agreement with knowledge accumulated on these well-studied genomes than predictions made by earlier developed tools.
Source: Nucleic Acids Research
DOI: 10.1093/nar/gkt1141
Authors: Paul D. Burns, Yang Li, Jian Ma, Mark Borodovsky
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,992
Awards: CAREER: Computational Methods for Analyzing Large-Scale Genomic Changes in Mammalian Genomes, G382648021, Gene Prediction by Markov Models and Complementary Methods, ABI Innovation: New Software Tools for Genome Comparisons of Non-Model Organisms, G6891423054, IMPROVE GENOME ANNOTATION USING MULTIPLE SEQUENCE ALIGNMENT RELIABILITY SCORES, G8148301435, G8920915315
OpenAlex ID: W2112849898