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2020年度入学学生!The pFind team is recruiting new members for 2020!

What's New

September 2, 2019 - The pFind team held its semi-annual meeting.All pFinders summarized and reported their work in 2019.


August 29-31, 2019 - Associate Professor Hao Chi and Master Student Zhuohong Wei visited Feng Ge's lab and communicated.


July 30, 2019 - Our paper "A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides" has been accepted for publication by the Nature Communications.Congratulations!


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.