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P-GRe : an efficient pipeline to maximised pseudogene prediction in plants/eucaryotes

Cabanac, S.; Mathe, C.; Dunand, C.

2023-12-06 bioinformatics
10.1101/2023.12.04.569967 bioRxiv
Show abstract

Formerly considered as part of "junk DNA", pseudogenes are nowadays known for their role in the post-transcriptional regulation of functional genes. In addition, their identification allows a better understanding of gene evolution in the frame of multigenic families. Despite this, there is, to our knowledge, no fully automatic user-friendly software allowing the annotation of pseudogenes on a whole genome. Here, we present Pseudo-Gene Retriever (P-GRe), a fully automated pseudogene prediction software requiring only a genome sequence and its corresponding GFF annotation file. P-GRe detects the sequences of the pseudogenes on a whole genome and returns to the user all their genomic sequences and their pseudo-coding sequences. The ability of P-GRe to finely reconstruct the structure of pseudogenes also allow to obtain a set of proteins virtually encoded by the predicted pseudogenes. We show here that in 70% of the cases, virtual proteins constructed by P-GRe from Arabidopsis thaliana proteome and genome aligned better to their parent protein than their annotated counterpart.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.