Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Assessing the significance of peptide spectrum match scores. / Abramova, Anastasiia; Korobeynikov, Anton.
17th International Workshop on Algorithms in Bioinformatics, WABI 2017. Том 88 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2017. 14 (Leibniz International Proceedings in Informatics, LIPIcs; Том 88).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Assessing the significance of peptide spectrum match scores
AU - Abramova, Anastasiia
AU - Korobeynikov, Anton
N1 - Conference code: 17
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Peptidic Natural Products (PNPs) are highly sought after bioactive compounds that include many antibiotic, antiviral and antitumor agents, immunosuppressors and toxins. Even though recent advancements in mass-spectrometry have led to the development of accurate sequencing methods for nonlinear (cyclic and branch-cyclic) peptides, requiring only picograms of input material, the identification of PNPs via a database search of mass spectra remains problematic. This holds particularly true when trying to evaluate the statistical significance of Peptide Spectrum Matches (PSM) especially when working with non-linear peptides that often contain non-standard amino acids, modifications and have an overall complex structure. In this paper we describe a new way of estimating the statistical significance of a PSM, defined by any peptide (including linear and non-linear), by using state-of-the-art Markov Chain Monte Carlo methods. In addition to the estimate itself our method also provides an uncertainty estimate in the form of confidence bounds, as well as an automatic simulation stopping rule that ensures that the sample size is sufficient to achieve the desired level of result accuracy.
AB - Peptidic Natural Products (PNPs) are highly sought after bioactive compounds that include many antibiotic, antiviral and antitumor agents, immunosuppressors and toxins. Even though recent advancements in mass-spectrometry have led to the development of accurate sequencing methods for nonlinear (cyclic and branch-cyclic) peptides, requiring only picograms of input material, the identification of PNPs via a database search of mass spectra remains problematic. This holds particularly true when trying to evaluate the statistical significance of Peptide Spectrum Matches (PSM) especially when working with non-linear peptides that often contain non-standard amino acids, modifications and have an overall complex structure. In this paper we describe a new way of estimating the statistical significance of a PSM, defined by any peptide (including linear and non-linear), by using state-of-the-art Markov Chain Monte Carlo methods. In addition to the estimate itself our method also provides an uncertainty estimate in the form of confidence bounds, as well as an automatic simulation stopping rule that ensures that the sample size is sufficient to achieve the desired level of result accuracy.
KW - Mass spectrometry
KW - Natural products
KW - Peptide spectrum matches
KW - Statistical significance
UR - http://www.scopus.com/inward/record.url?scp=85028747220&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.WABI.2017.14
DO - 10.4230/LIPIcs.WABI.2017.14
M3 - Conference contribution
AN - SCOPUS:85028747220
VL - 88
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 17th International Workshop on Algorithms in Bioinformatics, WABI 2017
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - International Workshop on Algorithms in Bioinformatics
Y2 - 21 August 2017 through 23 August 2017
ER -
ID: 11802670