actively developed tools / projects with stable executables / sources and support
The R/Bioconductor package Protein Array Analyzer (PAA) facilitates a flexible analysis of protein microarrays for biomarker discovery (esp., ProtoArrays). It provides a complete data analysis workflow including preprocessing and quality control, uni- and multivariate feature selection as well as several different plots and results tables to outline and evaluate the analysis results. As a main feature, PAA's multivariate feature selection methods are based on recursive feature elimination (e.g. SVM-recursive feature elimination, SVM-RFE) with stability ensuring strategies such as ensemble feature selection. This enables PAA to detect stable and reliable biomarker candidate panels.
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.
PIA in a nutshell
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.
A highly useful and flexible tool for calibration of targeted MS‐based measurements. CalibraCurve enables an automated batch-mode determination of dynamic linear ranges and quantification limits for both targeted proteomics and similar assays. The software uses a variety of measures to assess the accuracy of the calibration and provides intuitive visualizations.
The BIONDA biomarker database provides structured information on all biomarker candidates published in PubMed articles. There is no limitation to any kind of diseases. To this end, PubMed article abstracts and renowned databases such as UniProt and Human Disease Ontology are used as sources for BIONDA’s database entries. These are acquired automatically and updated regularly using text mining methods. BIONDA is available freely via a user-friendly web interface. As a specific characteristic, BIONDA’s database entries are rated by a scoring approach estimating biomarker reliability.