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UniNovo : A Universal Tool for de Novo Peptide Sequencing

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7821))

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Abstract

Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. While existing de novo sequencing tools perform well on certain types of spectra (e.g., Collision Induced Dissociation (CID) spectra of tryptic peptides), their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra, or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g., CID/ETD spectral pairs). The performance of UniNovo is compared with PepNovo+, PEAKS, and pNovo using various types of spectra. The results show that the performance of UniNovo is superior to other tools for ETD spectra and superior or comparable to others for CID and HCD spectra. UniNovo also estimates the probability that each reported reconstruction is correct, using simple statistics that are readily obtained from a small training dataset. We demonstrate that the estimation is accurate for all tested types of spectra (including CID, HCD, ETD, CID/ETD, and HCD/ETD spectra of trypsin, LysC, or AspN digested peptides). The appendix is available online at http://proteomics.ucsd.edu/Software/UniNovo.html

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA

    Kyowon Jeong

  2. Department of Computer Science and Engineering, University of California, San Diego, CA, USA

    Sangtae Kim & Pavel A. Pevzner

Authors
  1. Kyowon Jeong
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  2. Sangtae Kim
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  3. Pavel A. Pevzner
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Editor information

Editors and Affiliations

  1. School of Mathematics, Peking University, Beijing, P.R. China

    Minghua Deng

  2. Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, 100084, Beijing, P.R. China

    Rui Jiang

  3. Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA

    Fengzhu Sun

  4. Department of Automation, Tsinghua University, P.O. Box, 100084, Beijing, China

    Xuegong Zhang

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© 2013 Springer-Verlag Berlin Heidelberg

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Jeong, K., Kim, S., Pevzner, P.A. (2013). UniNovo : A Universal Tool for de Novo Peptide Sequencing. In: Deng, M., Jiang, R., Sun, F., Zhang, X. (eds) Research in Computational Molecular Biology. RECOMB 2013. Lecture Notes in Computer Science(), vol 7821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37195-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-37195-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37194-3

  • Online ISBN: 978-3-642-37195-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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