This fork is no longer the actively developed version of PIA and it might fade out of date! We keep this repo, as there are several publications linking to this place.

The new main repository is now located at https://github.com/medbioinf/pia

PIA is a toolbox for MS based protein inference and identification analysis.

PIA allows you to inspect the results of common proteomics spectrum identification search engines, combine them seamlessly and conduct statistical analyses. The main focus of PIA lays on the integrated inference algorithms, i.e. concluding the proteins from a set of identified spectra. But it also allows you to inspect your peptide spectrum matches, calculate FDR values across different search engine results and visualize the correspondence between PSMs, peptides and proteins.

Most search engines for protein identification in MS/MS experiments return protein lists, although the actual search yields a set of peptide spectrum matches (PSMs). The step from PSMs to proteins is called "protein inference". If a set of identified PSMs supports the detection of more than one protein in the searched database ("protein ambiguity"), usually only one representative accession is reported. These representatives may differ according to the used search engine and settings. Thus the protein lists of different search engines generally cannot be compared with one another. PSMs of complementary search engines are often combined to enhance the number of reported proteins or to verify the evidence of a peptide, which is improved by detection with distinct algorithms.

We developed an algorithm suite written in Java, including fully parametrisable KNIME nodes, which combine PSMs from different experiments and/or search engines, and reports consistent and thus comparable results. None of the parameters, like filtering or scoring, are fixed as in prior approaches, but held as flexible as possible, to allow for any adjustments needed by the user.

PIA can be called via the command line (also in Docker containers) or in the workflow environment KNIME, which allows a seamless integration into OpenMS workflows.