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How antisense transcripts can evolve to encode novel proteins

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Abstract Protein coding features can emerge de novo in non coding transcripts, resulting in emer- gence of new protein coding genes. Studies across many species show that a large frac- tion large fraction of evolutionarily novel non-coding RNAs have an antisense overlap with protein coding genes. The open reading frames (ORFs) in these antisense RNAs could also overlap with existing ORFs. In this study, we investigate how the evolution an ORF could be constrained by its overlap with an existing ORF in three different read- ing frames. Using a combination of mathematical modeling and genome/transcriptome data analysis in two different model organisms, we show that antisense overlap can increase the likelihood of ORF emergence and reduce the likelihood of ORF loss, es- pecially in one of the three reading frames. In addition to rationalising the repeatedly reported prevalence of de novo emerged genes in antisense transcripts, our work also provides a generic modeling and an analytical framework that can be used to under- stand evolution of antisense genes.
Title: How antisense transcripts can evolve to encode novel proteins
Description:
Abstract Protein coding features can emerge de novo in non coding transcripts, resulting in emer- gence of new protein coding genes.
Studies across many species show that a large frac- tion large fraction of evolutionarily novel non-coding RNAs have an antisense overlap with protein coding genes.
The open reading frames (ORFs) in these antisense RNAs could also overlap with existing ORFs.
In this study, we investigate how the evolution an ORF could be constrained by its overlap with an existing ORF in three different read- ing frames.
Using a combination of mathematical modeling and genome/transcriptome data analysis in two different model organisms, we show that antisense overlap can increase the likelihood of ORF emergence and reduce the likelihood of ORF loss, es- pecially in one of the three reading frames.
In addition to rationalising the repeatedly reported prevalence of de novo emerged genes in antisense transcripts, our work also provides a generic modeling and an analytical framework that can be used to under- stand evolution of antisense genes.

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