Current algorithmic solutions for peptide-based proteomics data generation and identification

Peptide-based proteomic data sets are ever increasing in size and complexity. These data sets provide computational challenges when attempting to quickly analyze spectra and obtain correct protein identifications. Database search and de novo algorithms must consider high-resolution MS/MS spectra and alternative fragmentation methods. Protein inference is a tricky problem when analyzing large data sets of degenerate peptide identifications. Combining multiple algorithms for improved peptide identification puts significant strain on computational systems when investigating large data sets. This review highlights some of the recent developments in peptide and protein identification algorithms for analyzing shotgun mass spectrometry data when encountering the aforementioned hurdles. Also explored are the roles that analytical pipelines, public spectral libraries, and cloud computing play in the evolution of peptide-based proteomics.

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► Peptide-based proteomics algorithms for ETD, HCD, and high-resolutions MS/MS spectra.
► Algorithms for identifying proteins in complex mixtures using shotgun proteomics.
► Spectral libraries and software pipelines for improved analytical throughput.
► Cloud computing to obtain results for large data sets.