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 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 the command line, in the workflow environment KNIME or using a web-interface (which requires an installation of a web server, but feel free to test it using the test server).
For how to install and run PIA inside KNIME see the wiki about PIA in KNIME.
Download the latest released version here or on the top of the page.
For documentation please refer to the Wiki (https://github.com/mpc-bioinformatics/pia/wiki) on github.
If you have any problems with PIA or find bugs and other issues, please use the issue tracker of github (https://github.com/mpc-bioinformatics/pia/issues).
If you found PIA useful for your work, please cite the following publication:
We provide a test installation of the web interface on http://188.8.131.52:8080/pia/
In your own interest, don't sent any sensitive research data to this server!