Top-down analysis of protein samples by de novo sequencing techniques

peer-reviewed · Bioinformatics · 2016

peer-reviewed · Bioinformatics · 2016. Kira Vyatkina et al. Motivation Recent technological advances have made high-resolution mass spectrometers affordable to many…
Date 2016-09-15
Type peer-reviewed
Venue Bioinformatics
Publisher Oxford University Press (OUP)
Contribution algorithm
DOI 10.1093/bioinformatics/btw307
Citations (OpenAlex) 20
Venue 2-year citedness 6.16

Abstract

Motivation Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data. Results We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns. Availability and implementation Freely available on the web at http://bioinf.spbau.ru/en/twister Contact or Supplementary information Supplementary data are available at Bioinformatics online.

Authors

  1. Kira Vyatkina · ITMO University, Saint Petersburg Academic University, Saint Petersburg National Research Academic University of the Russian Academy of Sciences, Saint Petersburg State Electrotechnical University, St Petersburg University, St. Petersburg State University
  2. Si Wu · Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, University of Oklahoma
  3. Lennard J. M. Dekker · Erasmus MC, Erasmus University Rotterdam
  4. Martijn M. VanDuijn · Erasmus MC, Erasmus University Rotterdam
  5. Xiaowen Liu · Indiana University Indianapolis, Indiana University School of Medicine, Indiana University-Purdue University Indianapolis, Tulane University, University of Waterloo
  6. Nikola Tolić · Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory
  7. Theo M. Luider · Erasmus MC, Erasmus University Rotterdam
  8. Ljiljana Paša-Tolić · Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory
  9. Pavel A. Pevzner · Max Planck Institute of Molecular Cell Biology and Genetics, Saint Petersburg Academic University, St Petersburg University, St. Petersburg State University, University of California San Diego, University of Southern California

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