Recognizing short coding sequences of prokaryotic genome using a novel iteratively adaptive sparse partial least squares algorithm.

ABSTRACT:

Background

Significant efforts have been made to address the problem of identifying short genes in prokaryotic genomes. However, most known methods are not effective in detecting short genes. Because of the limited information contained in short DNA sequences, it is very difficult to accurately distinguish between protein coding and non-coding sequences in prokaryotic genomes. We have developed a new Iteratively Adaptive Sparse Partial Least Squares (IASPLS) algorithm as the classifier to improve the accuracy of the identification process.

Results

For testing, we chose the short coding and non-coding sequences from seven prokaryotic organisms. We used seven feature sets (including GC content, Z-curve, etc.) of short genes.In comparison with GeneMarkS, Metagene, Orphelia, and

SUBMITTER: Chen S 

PROVIDER: S-EPMC3852556 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature