A posteriori quality control for the curation and reuse of public proteomics data

Proteomics - Tập 11 Số 11 - Trang 2182-2194 - 2011
Joseph Foster1, Sven Degroeve2,3, Laurent Gatto4, Matthieu Visser5, Rui Wang1, Johannes Griss6, Rolf Apweiler1, Lennart Martens2,3
1EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
2Department of Biochemistry, Ghent University, Ghent, Belgium
3Department of Medical Protein Research, VIB, Ghent, Belgium
4Cambridge Centre for Proteomics, Cambridge Systems Biology Centre, Department of Biochemistry, University of Cambridge, Cambridge, UK
5Philips Research Laboratories, Cambridge Science Park, Cambridge, UK
6Department of Medicine, Vienna General Hospital, Medical University Vienna, Vienna, Austria

Tóm tắt

AbstractProteomics is a rapidly expanding field encompassing a multitude of complex techniques and data types. To date much effort has been devoted to achieving the highest possible coverage of proteomes with the aim to inform future developments in basic biology as well as in clinical settings. As a result, growing amounts of data have been deposited in publicly available proteomics databases. These data are in turn increasingly reused for orthogonal downstream purposes such as data mining and machine learning. These downstream uses however, need ways to a posteriori validate whether a particular data set is suitable for the envisioned purpose. Furthermore, the (semi‐)automatic curation of repository data is dependent on analyses that can highlight misannotation and edge conditions for data sets. Such curation is an important prerequisite for efficient proteomics data reuse in the life sciences in general. We therefore present here a selection of quality control metrics and approaches for the a posteriori detection of potential issues encountered in typical proteomics data sets. We illustrate our metrics by relying on publicly available data from the Proteomics Identifications Database (PRIDE), and simultaneously show the usefulness of the large body of PRIDE data as a means to derive empirical background distributions for relevant metrics.

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Proteomics

Tập 11 Số 11

2182-2194

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