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2022年度入学学生!The pFind team is recruiting new members for 2022!

What's New

April 10, 2021 - WANG Kai-Fei (王凯菲) won two first prizes in the table tennis competition hosted by School of Computer Science and Technology, UCAS.

April 10, 2021 - pFinders visited the Summer Palace (颐和园) together.

April 6, 2021 - Our paper pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning has been published on Journal of Proteome Research.

April 2, 2021 - Our paper pDeep3: Toward More Accurate Spectrum Prediction with Fast Few-Shot Learning has been published on Analytical Chemistry.

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.
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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.