Motivation: Tandem mass spectrometry has become a standard tool for identifying post-translational modifications (PTMs) of proteins. Algorithmic searches for PTMs from tandem mass spectrum data (MS/MS) tend to be hampered by noisy data as well as by a combinatorial explosion of search space. This leads to high uncertainty and long search-execution times.
View Article and Find Full Text PDFProtein Pept Lett
February 2015
Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion.
View Article and Find Full Text PDFMotivation: Identification of proteins by mass spectrometry-based proteomics requires automated interpretation of peptide tandem mass spectrometry spectra. The effectiveness of peptide identification can be greatly improved by filtering out extraneous noise peaks before the subsequent database searching steps.
Results: Here we present a novel chemical rule-based filtering algorithm, termed CRF, which makes use of the predictable patterns (rules) of collision-induced peptide fragmentation.
Background: Microbial consortia are a major form of life; however their stability conditions are poorly understood and are often explained in terms of species-specific defence mechanisms (secretion of extracellular matrix, antimicrobial compounds, siderophores, etc.). Here we propose a hypothesis that the primarily local nature of intercellular signalling can be a general mechanism underlying the stability of many forms of microbial communities.
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