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

Paper ID Volume ID Publish Year Pages File Format Full-Text
15683 42468 2013 8 PDF Available
Title
Current algorithmic solutions for peptide-based proteomics data generation and identification
Abstract

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.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (216 K)Download as PowerPoint slideHighlights► 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.

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Current algorithmic solutions for peptide-based proteomics data generation and identification
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Publisher
Database: Elsevier - ScienceDirect
Journal: Current Opinion in Biotechnology - Volume 24, Issue 1, February 2013, Pages 31–38
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us