Find Studio

Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. While proteomics generally refers to the large-scale experimental analysis of proteins, it is often specifically used for protein purification and mass spectrometry which is an analytical technique that measures the mass-to-charge ratio of charged particles.

pFind Studio is a computational solution for such mass spectrometry-based proteomics. It germinated in 2002 in Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. We call ourselves "pFinders". Our goal is to study bioinformatics algorithms and to develop easy-to-use software tools to help answer biological questions.
The pFind team is recruiting new members for 2018!(pFind团队招收2018年度入学学生!)

What's New

June 22, 2017 - The pFind team is recruiting new members for 2018! For further details, please refer to the About us page.
(pFind团队招收2018年度入学学生!详情请点击About us页面。)

June 4-8, 2017 - Associate Professor Rui-Xiang Sun, Hao Chi, Dr. Chao Liu, and Wen-Feng Zeng attended the Annual Conference of American Society for Mass Spectrometry (ASMS). Dr. Chao Liu also gave an oral report about pLink2 in this conference.

May 25, 2017 - Rui-Min Wang, Zhao-Wei Wang and Xiao-Jin Zhang passed their dissertation defenses.


Software

pFind is a search engine for peptide and protein identification via tandem mass spectrometry.[download...]




pLink is a tool dedicated for the analysis of chemically cross-linked proteins or protein complexes using mass spectrometry. [download...]



pNovo+ is a de novo peptide sequencing algorithm using complementary HCD and ETD tandem mass spectra. [download...]

Benchmark

We participated in the ABRF iPRG study with pFind developed by our group in the past few years.


"The mission of the ABRF iPRG (formerly the Bioinformatics Committee) is to educate ABRF members and the scientific community on best application and practice of bioinformatics toward accurate and comprehensive analysis of proteomics data."


We believe it is advantageous to improve our algorithms, software tools and strategies for proteome informatics.