democratization of proteomics data lennart martens lennart.martens@vib-ugent.be computational omics and systems biology group VIB / Ghent University, Ghent, Belgium
We should never forget The land of opportunity What about our quali(ty|fications)?
We should never forget The land of opportunity What about our quali(ty|fications)?
J.R.R. Tolkien, A Conversation with Smaug
Due to the large volume of data, LIMS systems are required to manage data locally Helsens, Proteomics, 2010 Review in Stephan, Proteomics, 2010
A little light searching in ms_lims
We should never forget The land of opportunity What about our quali(ty|fications)?
(Public) proteomics data can be used in different ways, opening up many opportunities Vaudel, Proteomics, revision submitted
Ideally, the data ecosystem will even allow in silico proteomics experiments Vaudel, Proteomics, revision submitted
We should never forget The land of opportunity What about our quali(ty|fications)?
Target: peppermint icicles Result: peppermint ... eh … erm…
The big quality control poll ( n=86, 8 days ) How would you describe yourself? With Bas van Breukelen, Utrecht
The big quality control poll ( n=86, 8 days ) Do you already use quality control tools? With Bas van Breukelen, Utrecht
The big quality control poll ( n=86, 8 days ) Is it easy to obtain quality control software? With Bas van Breukelen, Utrecht
The big quality control poll ( n=86, 8 days ) How important is quality control for … With Bas van Breukelen, Utrecht
The big quality control poll ( n=86, 8 days ) How easy is it for you to … With Bas van Breukelen, Utrecht
The NIST/NCI CPTAC panel of metrics Rudnick, MCP, 2010
NIST metrics in actual use Rudnick, MCP, 2010
Tabb lab implementation: QuaMeter Ma, Anal. Chem., 2012
OpenMS supports TOPPAS and KNIME quality control pipelines TOPPAS KNIME Junker, Journal of Proteome Research, 2012 Walzer, MCP, 2014
qcML is intended to be transparent, acting as the relay format of QC metrics Walzer, MCP, 2014
Ideally, longitudinal analysis of QC metrics allows the easy detection of outlying data Walzer, MCP, 2014
However, different types of data sets will exhibit different ranges of QC metrics Walzer, MCP, 2014
If you get down and dirty, the instrument logs are very interesting fodder too Bittremieux, Journal of Proteome Research, 2015
The journey from quality control to accreditation can be taken in steps Given the right software: • Routine local QC on a local standard • Routine local QC on all runs • Longitudinal QC data on standard and runs • Given data standards and infrastructure: • Routine deposition of QC metrics in public domain • Mandatory deposition of QC metrics in public domain • Given reference samples • Voluntary, non-binding performance tests • Voluntary accreditation • Official standards for accreditation •
www.compomics.com @compomics
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