Rapidly accumulating genome sequence data from multiple species offer powerful opportunities for the detection of DNA sequence evolution. Phylogenetic tree construction and codon-based tests for natural selection are the prevailing tools used to detect functionally important evolutionary change in protein coding sequences. These analyses often require multiple DNA sequence alignments that maintain the correct reading frame for each collection of putative orthologous sequences. Since this feature is not available in most alignment tools, codon reading frames often must be checked manually before evolutionary analyses can commence.
Here we report an online codon-preserved alignment tool (OCPAT) that generates multiple sequence alignments automatically from the coding sequences of any list of human gene IDs and their putative orthologs from genomes of other vertebrate tetrapods. OCPAT is programmed to extract putative orthologous genes from genomes and to align the orthologs with the reading frame maintained in all species. OCPAT also optimizes the alignment by trimming the most variable alignment regions at the 5' and 3' ends of each gene. The resulting output of alignments is returned in several formats, which facilitates further molecular evolutionary analyses by appropriate available software. Alignments are generally robust and reliable, retaining the correct reading frame. The tool can serve as the first step for comparative genomic analyses of protein-coding gene sequences including phylogenetic tree reconstruction and detection of natural selection. We aligned 20,658 human RefSeq mRNAs using OCPAT. Most alignments are missing sequence(s) from at least one species; however, functional annotation clustering of the ~1700 transcripts that were alignable to all species shows that genes involved in multi-subunit protein complexes are highly conserved.
The OCPAT program facilitates large-scale evolutionary and phylogenetic analyses of entire biological processes, pathways, and diseases.
Evolution | Genetics and Genomics | Health Information Technology
Liu et al. Source Code for Biology and Medicine 2007, 2:5