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The Bacterial Proteogenomic Pipeline consists of several modules, which assist in a proteogenomics analysis. Each module can either be called by the command line or via a Java Swing GUI.

For an exampel walkthrough, please see the wiki page (


Download the latest released version here

Publication / How to cite it:

The bacterial proteogenomic pipeline. Julian Uszkoreit, Nicole Plohnke, Sascha Rexroth, Katrin Marcus and Martin Eisenacher. BMC Genomics 2014, 15(Suppl 9):S19. doi:10.1186/1471-2164-15-S9-S19

The Pipeline

Parse Protein Information

Reads in a protein database in FASTA format, which should contain the reading frame positions of the proteins in the header. Alternatively a TSV/CSV file may be used, which holds all information for the annotated genes/proteins. With this information, a GFF3 file for the "known proteins" is created.

Compare And Combine (optional)

This class helps to get further protein information from another FASTA file. To do this, take the information from the GFF file created by ParseProteinInformationToGFF with the corresponding FASTA file as target and another FASTA database as reference set. The enriched information will be written into a new GFF and FASTA file.

Genome Parser

Reads in a FASTA containing the genome of a specific species in all six reading frames and translates it into all possible "pseudoproteins". Only the translation code for bacteria is used at the moment. In Bacteria, there are codons which signal a start codon but are usually NOT coding for methionine. These are still translated into M, if they start the protein. A pseudo protein marked as ORF is the first (i.e. automatically longest) sub-sequence of a pseudo protein with a start codon as sequence start.

If a GFF file for the known proteins (from ParseProteinInformationToGFF and/or CompareAndCombineProteinList) is given, pseudoproteins with exactly the same translation start and end sites as a known protein will not be reported.

Reported is a FASTA file containing all the pseudo proteins and for each frame (0-5) a GFF file with the corresponding information.

Create Decoy DB (optional)

Generate combined and decoy database and search by search engines.

Combine Identifications

In this step, the search results of an external search are combined. The identified PSM may be validated and FDR-filtered. The module takes mzTab files as input.


The final step allows analysis of the identified peptides and visualizes the number of distinctly identified PSMs for each peptide.


📚 🔬 The Bacterial Proteogenomic Pipeline




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