Abstract
Human gene catalogs are fundamental to the study of human biology and medicine. But they are all based on open reading frames (ORFs) in a reference genome sequence (with allowance for introns). Individual genomes, however, are polymorphic: their sequences are not identical. There has been much research on how polymorphism affects previously-identified genes, but no research has been done on how it affects gene identification itself. We computationally predict protein-coding genes in a straightforward manner, by finding long ORFs in mRNA sequences aligned to the reference genome. We systematically test the effect of known polymorphisms with this procedure. Polymorphisms can not only disrupt ORFs, they can also create long ORFs that do not exist in the reference sequence. We found 5,737 putative protein-coding genes that do not exist in the reference, whose protein-coding status is supported by homology to known proteins. On average 10% of these genes are located in the genomic regions devoid of annotated genes in 12 other catalogs. Our statistical analysis showed that these ORFs are unlikely to occur by chance.
Highlights
Compilation of an accurate catalog of protein-coding genes encoded in human genomes is a critical step to fully understand the functional elements in human genomes
We identified 5,737 putative protein-coding genes that result from mRNA modified by human polymorphisms and have significant homology to known proteins
There we indicate the genomic positions of the longest open reading frames (ORFs), the polymorphism that causes the modification, the length of 59 untranslated region (UTR) after modification, the E-value of homology to known proteins and the sequence orientation
Summary
Compilation of an accurate catalog of protein-coding genes encoded in human genomes is a critical step to fully understand the functional elements in human genomes. Many annotations of protein-coding genes have been published [1] and a plethora of gene finding software has been introduced [2]. As stated by Brent [4] the difficulty lies in the limitations of sequencing protocols, ways to combine the predicted genes and the limitations of human curators. In response to these findings we believe that there is an increasing amount of genomic evidence that may affect proteincoding gene detection, which has not been taken into account. It has been suggested that such polymorphism affects protein-coding genes, and that they are responsible for various human diseases [6,7,8]
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