SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The software is freely available. pypgatk: https://github.com/bigbio/py-pgatk/ and pgdb: https://nf-co.re/pgdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

IEF LC-MS/MS is an analytical method that incorporates a two-step sample separation prior to MS identification of proteins. When analyzing complex samples this preparatory separation allows for higher analytical depth and improved quantification accuracy of proteins. However, cost and analysis time are greatly increased as each analyzed IEF fraction is separately profiled using LC-MS/MS. We propose an approach that selects a subset of IEF fractions for LC-MS/MS analysis that is highly informative in the context of a group of proteins of interest. Specifically, our method allows a significant reduction in cost and instrument time as compared to the standard protocol of running all fractions, with little compromise to coverage. We develop algorithmics to optimize the selection of the IEF fractions on which to run LC-MS/MS. We translate the fraction optimization task to Minimum Set Cover, a well-studied NP-hard problem. We develop heuristic solutions and compare them in terms of effectiveness and running times. We provide examples to demonstrate advantages and limitations of each algorithmic approach. Finally, we test our methodology by applying it to experimental data obtained from IEF LC-MS/MS analysis of yeast and human samples. We demonstrate the benefit of this approach for analyzing complex samples with a focus on different protein sets of interest.

IEF LC-MS/MS (Iso-Electric-Focusing Liquid-Chromatography Tandem-Mass-Spectrometry) is an analytical method that incorporates a two-step sample separation prior to MS identification of proteins. When analyzing complex samples this preparatory separation allows for higher analytical depth and improved quantification accuracy of proteins and PTMs. IEF fractionation builds upon isoelectric point (pI) differences between peptides or proteins. In standard IEF LC-MS/MS, each fraction is separately profiled using LC-MS/MS. The cost of the complete assay, therefore, is strongly dependent on the number of pI fractions being analyzed. Commonly, studies are focused on a specific group of proteins. We propose an approach that selects a subset of fractions for LC-MS/MS analysis that is highly informative in the context of the proteins of interest. Specifically, our method allows a significant reduction in cost and instrument time as compared to the standard protocol of running all fractions, with little compromise to coverage. We develop algorithmics to optimize the selection of the IEF fractions on which to run LC-MS/MS. We translate the fraction optimization task to Minimum Set Cover (MSC), a well-studied NP-hard problem. We develop heuristic solutions and compare them in terms of effectiveness and of practical running times. We provide examples to demonstrate advantages and limitations of each algorithmic approach. Finally, we test our methodology by applying it to experimental data obtained from IEF LC-MS/MS analysis of yeast and human samples. We demonstrate the benefit of this approach for analyzing complex samples with a focus on different protein sets of interest.