Toward improved peptide feature detection in quantitative proteomics using stable isotope labeling.
Reliable detection of peptides in LC-MS data is a key algorithmic step in the analysis of quantitative proteomics experiments. While highly abundant peptides can be detected reliably by most modern software tools, there is much less agreement on medium and low-intensity peptides in a sample. The choice of software tools can have a big impact on the quantification of proteins, especially for proteins that appear in lower concentrations. However, in many experiments, it is precisely this region of less abundant but substantially regulated proteins that holds the biggest potential for discoveries. This is particularly true for discovery proteomics in the pharmacological sector with a specific interest in key regulatory proteins. In this viewpoint article, we discuss how the development of novel software algorithms allows us to study this region of the proteome with increased confidence. Reliable results are one of many aspects to be considered when deciding on a bioinformatics software platform. Deployment into existing IT infrastructures, compatibility with other software packages, scalability, automation, flexibility, and support need to be considered and are briefly addressed in this viewpoint article.
Institute of Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany.
Lars
L
Nilse
Institute of Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany.|Faculty of Biology, University of Freiburg, Freiburg, Germany.
Florian Christoph
FC
Sigloch
Institute of Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany.
Martin L
ML
Biniossek
Institute of Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany.|BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany.
Oliver
O
Schilling
D000818:Animals; D002853:Chromatography, Liquid; D007553:Isotope Labeling / Q000379:methods*; D013058:Mass Spectrometry; D051379:Mice; D010455:Peptides / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012867:Skin / Q000378:metabolism
D010455:Peptides; D020543:Proteome
Bioinformatics data processing; Feature finding; Quantitative proteomics; Technology