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Pool-seq driven proteogenomic database for Group G Streptococcus.

R G Weldatsadik¹, N Datta², C Kolmeder³

¹Research Programs Unit, Immunobiology, Univ. of Helsinki, Helsinki, Finland; Institute of Biotechnology, Molecular Systems Biology Research Group, HiLIFE, Univ. of Helsinki, Helsinki, Finland; Protein-Proteome Research Program, HiLIFE, Univ. of Helsinki, Helsinki, Finland.

Journal of Proteomics

|April 25, 2019

Summary

This summary is machine-generated.

Proteogenomic databases enhance peptide and protein identification in mass spectrometry. Creating variant databases from pooled sequencing data improves variant peptide discovery in diverse bacteria like Group G Streptococcus.

Keywords:

Group G Streptococcus Pool-seq Proteogenomics Variant protein database

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Area of Science:

Microbiology
Proteomics
Genomics

Background:

Proteogenomic databases integrate genomic and transcriptomic data for enhanced peptide and protein identification in mass spectrometry.
Discovering genetic variants and mutations is a key application of proteogenomic databases.
Bacterial species like Group G Streptococcus exhibit significant intra-species diversity.

Purpose of the Study:

To create and evaluate a proteogenomic database incorporating sequence variants from pooled sequencing experiments.
To assess the utility of this database for identifying variant peptides in Group G Streptococcus using tandem mass spectrometry (MS/MS).

Main Methods:

Generated a proteogenomic database with variants from 137 Group G Streptococcus strains sequenced in 3 pools.
Analyzed eight protein samples from randomly selected strains using MS/MS.
Compared identification results against conventional single-genome databases and databases with varying numbers of genomes/assemblies.

Main Results:

Identified 385 variant peptides using the proteogenomic variant database, which were undetectable with a conventional single-genome database.
Achieved identification rates of 71.2% and 93.5% for variant peptides using databases with 4 complete genomes and 26 assemblies, respectively.
Proteogenomic variant databases demonstrated comparable performance to conventional databases regarding Andromeda scores and posterior error probability (PEP).

Conclusions:

Proteogenomic variant databases derived from pooled sequencing experiments are effective for discovering peptides, especially in bacteria with high intra-species diversity like Group G Streptococcus.
This approach offers a cost-effective method to complement traditional databases and identify subtle strain-specific differences.
Simultaneous sequencing of multiple strains and subsequent proteogenomic database generation significantly improves peptide discovery in mass spectrometry analyses.