Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides.

Umer HM, Audain E, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca RM, Perez-Riverol Y

Bioinformatics 38 (5) 1470-1472 [2022-02-07; online 2021-12-15]

We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling including optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we have reanalyzed six public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to >5% of the total number of peptides identified. The software is freely available. pypgatk: https://github.com/bigbio/py-pgatk/ and pgdb: https://nf-co.re/pgdb. Supplementary data are available at Bioinformatics online.

Global Proteomics and Proteogenomics [Technology development]

PubMed 34904638

DOI 10.1093/bioinformatics/btab838

Crossref 10.1093/bioinformatics/btab838

pmc: PMC8825679
pii: 6460801