Abstract
PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.
Highlights
In the field of discovery proteomics, the aim of an experiment is to take a series of two or more biological samples and quantify the proteins within each
One way to achieve this goal is the use of label-free quantitative shotgun proteomics methodologies
Spectral counting has become a widely used option when analysing label-free data [2], and produces high-quality results, as demonstrated in a recent comprehensive comparative analysis between labelling with isobaric tags for relative and absolute quantification, exponentially modified protein abundance index, area under the curve (AUC) and spectral counts (SpCs) identification workflows [3]. Since it does not involve expensive labelling reagents, the use of SpCs in proteomics workflows is a desirable option for researchers interested in holistic approaches to discovery proteomics
Summary
In the field of discovery proteomics, the aim of an experiment is to take a series of two or more biological samples and quantify the proteins within each. Spectral counting has become a widely used option when analysing label-free data [2], and produces high-quality results, as demonstrated in a recent comprehensive comparative analysis between labelling with isobaric tags for relative and absolute quantification (iTRAQ), exponentially modified protein abundance index (emPAI), AUC and SpC identification workflows [3]. Since it does not involve expensive labelling reagents, the use of SpCs in proteomics workflows is a desirable option for researchers interested in holistic approaches to discovery proteomics
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