Research on Efficient Algorithm of Peptide Identification and Quantification in Proteomics

DOI

10.2991/bbe-16.2016.22How to use a DOI?

Keywords

Proteomics, Label-free quantification, XICFinder, Detectability, Reproducibility, Precision.

Abstract

Because of its simple experiment design and low experiment cost, label-free quantitative technology based on mass spectrometry analysis is being used more and more widely. Aiming at peptide identification and quantification, which are pivotal step of the label-free quantitative analysis, we develop an efficient algorithm named XIC Finder based on C++ platform. In term of the peptides which failed to be identified by MS/MS spectrum, we utilize ESP model trained by 20 optimized peptide features with Random Forest method, to predict those peptides detectability and chose the highest scored peptide as the correct peptide. Compared with MaxQuant and IDEAL-Q, other algorithms developed for quantitative MS data, XIC Finder improves the performance of the peptide identification and quantification significantly. Furthermore, we evaluated the reproducibility and precision of XIC Finder by using the replication dataset and the UPS1 standard data set respectively and prove the result is better than other algorithms.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhen Wang
AU  - Jiyang Zhang
AU  - Yunping Zhu
AU  - Hongwei Xie
PY  - 2016/07
DA  - 2016/07
TI  - Research on Efficient Algorithm of Peptide Identification and Quantification in Proteomics
BT  - Proceedings of the 2016 International Conference on Biomedical and Biological Engineering
PB  - Atlantis Press
SP  - 130
EP  - 136
SN  - 2468-5747
UR  - https://doi.org/10.2991/bbe-16.2016.22
DO  - 10.2991/bbe-16.2016.22
ID  - Wang2016/07
ER  -