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Welcome to the PIA-Wiki.
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 semlessly 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 a fully parametrisable web-interface (using JavaServer Faces), which combines PSMs from different experiments and/or search engines, and reports consistent and thus comparable results. None of the parameters for the inference, 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 KNIME nodes and the the command line (also using Docker).
We created a a tutorial for a basic PIA analysis, mainly inside KNIME.
The tutorial as PDF can be downloaded here, the required data are available here and the workflows here (all are in the tutorial repository at https://github.com/julianu/pia-tutorial/).
A PIA analysis can be run either using KNIME or the command line. The web-interface is no longer maintained.