Dataset Information

A computational approach for identifying pseudogenes in the ENCODE regions.

ABSTRACT: Pseudogenes are inheritable genetic elements showing sequence similarity to functional genes but with deleterious mutations. We describe a computational pipeline for identifying them, which in contrast to previous work explicitly uses intron-exon structure in parent genes to classify pseudogenes. We require alignments between duplicated pseudogenes and their parents to span intron-exon junctions, and this can be used to distinguish between true duplicated and processed pseudogenes (with insertions).Applying our approach to the ENCODE regions, we identify about 160 pseudogenes, 10% of which have clear 'intron-exon' structure and are thus likely generated from recent duplications.Detailed examination of our results and comparison of our annotation with the GENCODE reference annotation demonstrate that our computation pipeline provides a good balance between identifying all pseudogenes and delineating the precise structure of duplicated genes.

SUBMITTER: Zheng D

PROVIDER: S-EPMC1810550 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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A computational approach for identifying pseudogenes in the ENCODE regions.

Zheng Deyou D Gerstein Mark B MB

Genome biology 20060807

<h4>Background</h4>Pseudogenes are inheritable genetic elements showing sequence similarity to functional genes but with deleterious mutations. We describe a computational pipeline for identifying them, which in contrast to previous work explicitly uses intron-exon structure in parent genes to classify pseudogenes. We require alignments between duplicated pseudogenes and their parents to span intron-exon junctions, and this can be used to distinguish between true duplicated and processed pseudog ...[more]

PMID: 16925835

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Project description:BackgroundPathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs.ResultsWe present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. Of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs.ConclusionSupporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis.

Dataset Information

A computational approach for identifying pseudogenes in the ENCODE regions.

Publications

A computational approach for identifying pseudogenes in the ENCODE regions.

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