HBFP: a new repository for human body fluid proteome.

Dan Shao, Lan Huang, Yan Wang, Xueteng Cui, Yufei Li, Yao Wang, Qin Ma, Wei Du, Juan Cui

Journal: Database : the journal of biological databases and curation 2022;2021():baab065

PMID: 34642750

Abstract

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery. Database URL: https://bmbl.bmi.osumc.edu/HBFP/.

© The Author(s) 2021. Published by Oxford University Press.

Address: Department of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA.; Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China.; Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China.; Department of Biomedical Informatics, College of Medicine, The Ohio State University, 310G Lincoln tower, 1800 cannon drive, Columbus, OH 43210, USA.
Bant logo

© Copyright 2026, Nutrition Evidence

NED wishes to thank the following organisations for their support:

We use cookies to improve your experience and analyze site traffic with Google Analytics. By continuing to use our site, you agree to our use of cookies. Learn more.