Last year I dedicated three blogposts to mass spectrometry-based proteomics (MS-proteomics) data analysis: PEPTIDE SEARCH ENGINES THE PEPTIDE-SPECTRUM MATCH DATA LOSS AND WAYS TO CONTROL IT To continue with this subject, I now discuss how using deep learning algorithms to predict fragment (MS2) ion mass intensities increases the number of peptide sequences identified by a peptide search engine. From the handful of deep learning-based MS2 ion intensity predictors reported so f

This blogpost is a postscript  to the previous one — “THE PEPTIDE-SPECTRUM MATCH” — ,  in which I wrote about the peptide-spectrum match...

In my previous blogpost “PROTEOMICS SEARCH ENGINES”   I wrote about peptide search engines in the context of the data-dependent acquisition (DDA) approach used in shotgun proteomics. In this blogpost I discuss the strategies used by peptide search engines to mitigate the false discovery rate when analyzing DDA data. For those not familiar with proteomics jargon, the abbreviations used throughout the text are: Data-dependent acquisition ( DDA ) False discovery rate ( FDR ) Fra

In this blogpost I talk about peptide search engines—the algorithms used in MS-proteomics to assign peptide sequences to raw mass spectra. For those not familiar with proteomics jargon, the abbreviations used throughout the text are: Data-dependent acquisition ( DDA ) Data-independent acquisition ( DIA ) Mass spectrometry-based proteomics ( MS-proteomics ), AKA shotgun proteomics Peptide fragmentation spectra (MS2) Precursor ion spectra (MS1) Peptide-spectrum match ( PSM )

But mass spectrometry-based proteomics (MS-proteomics), as we know it, was not born in 1995, which is when the term proteome first...