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Mature

actively developed tools / projects with stable executables / sources and support


Protein Array Analyzer (PAA)

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.

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Protein Inference Algorithms (PIA)

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.


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CalibraCurve

Text in process

CalibraCurve was published in:
Kohl, M., Stepath, M., Bracht, T., Megger, D.A., Sitek, B., Marcus, K. and Eisenacher, M. (2020), CalibraCurve: A Tool for Calibration of Targeted MS‐based Measurements. Proteomics. Accepted Author Manuscript. doi:10.1002/pmic.201900143


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BIONDA

Die BIONDA Biomarker Datenbank bietet strukturierte Informationen zu allen Biomarkerkandidaten, die in PubMed-Artikeln veröffentlicht wurden. Es gibt keine Beschränkung auf irgendeine Art von Krankheiten. Zu diesem Zweck werden die Abstracts der PubMed-Artikel und renommierte Datenbanken wie UniProt und Human Disease Ontology als Quellen für die Datenbankeinträge von BIONDA verwendet. Diese werden mit Hilfe von Text-Mining-Methoden automatisch erfasst und regelmäßig aktualisiert. BIONDA ist über eine benutzerfreundliche Web-Schnittstelle frei verfügbar. Als spezifisches Merkmal werden die Datenbankeinträge von BIONDA durch einen Scoring-Ansatz bewertet, der die Zuverlässigkeit der Biomarker schätzt.