Abstract
An original bioinformatics technique is developed to identify the protein-coding genes in rodents, lagomorphs and nonhuman primates that are pseudogenized in humans. The method is based on per-gene verification of local synteny, similarity of exon-intronic structures and orthology in a set of genomes. It is applicable to any genome set, even with the number of genomes exceeding 100, and efficiently implemented using fast computer software. Only 50 evolutionary recent human pseudogenes were predicted. Their functional homologs in model species are often associated with the immune system or digestion and mainly express in the testes. According to current evidence, knockout of most of these genes leads to an abnormal phenotype. Some genes were pseudogenized or lost independently in human and nonhuman hominoids.
Highlights
Pseudogenes were for long relegated to the “junk” portion of DNA, but nowadays they attract an increasing attention for bearing important biological functions [1,2], e.g., in gene expression regulation and development of human diseases
With many pseudogenes already known from human and model species [8], research continues towards an effective computer technique for large-scale prediction of pseudogenes with common protein-coding homologs in a wide set of species [9]
The challenge is to detect pseudogenes in a species of interest or a set of species based on ancestry patterns of protein-coding homologs in a query species set
Summary
Pseudogenes were for long relegated to the “junk” portion of DNA, but nowadays they attract an increasing attention for bearing important biological functions [1,2], e.g., in gene expression regulation and development of human diseases. Association of pseudogenes with human disorders has been approached in many works. With many pseudogenes already known from human and model species [8], research continues towards an effective computer technique for large-scale prediction of pseudogenes with common protein-coding homologs in a wide set of species [9]. The challenge is to detect pseudogenes in a species of interest (in our case, humans) or a set of species based on ancestry patterns of protein-coding homologs in a query species set (here, the Euarchontoglires group) Among numerous examples are interfaces in cancer [3,4,5,6], type 2 diabetes [7], pulmonary fibrosis, adrenal hyperplasia, chronic pancreatitis, AIDS and others [1].
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