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.
pFind团队招收2019年度入学学生!The pFind team is recruiting new members for 2019!

What's New

August 22-23, 2018 - The Fifth China Workshop on Computational Proteomics successfully completed. We give thanks to all experts for their good presentations and to all participants for their support.



July 19, 2018 - The pFind team held its semi-annual meeting. All pFinders summarized and reported their work in the first half of 2018.




June 3-7, 2018 - Dr. Wen-Feng Zeng and Hao Yang attended the Annual Conference of American Society for Mass Spectrometry (ASMS) 2018.





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.