Motivation: Pseudogenes are the remnants of genomic sequences of genes which are no longer functional. They are frequent in most eukaryotic genomes, and an important resource for comparative genomics. However, pseudogenes are often mis-annotated as functional genes in sequence databases. Current methods for identifying pseudogenes include methods which rely on the presence of stop codons and frameshifts, as well as methods based on the ratio of non-silent to silent nucleotide substitution rates (dN/dS). A recent survey concluded that 50% of human pseudogenes have no detectable truncation in their pseudo-coding regions, indicating that the former methods lack sensitivity. The latter methods have been used to find sets of genes enriched for pseudogenes, but are not specific enough to accurately separate pseudogenes from expressed genes.
Results: We introduce a program called pseudogene inference from loss of constraint (PSILC) which incorporates novel methods for separating pseudogenes from functional genes. The methods calculate the log-odds score that evolution along the final branch of the gene tree to the query gene has been according to the following constraints: A neutral nucleotide model compared to a Pfam domain encoding model (PSILC(nuc/dom)); A protein coding model compared to a Pfam domain encoding model (PSILC(prot/dom)). Using the manual annotation of human chromosome 6, we show that both these methods result in a more accurate classification of pseudogenes than dN/dS when a Pfam domain alignment is available.
Availability: PSILC is available from http://www.sanger.ac.uk/Software/PSILC