Evidence map›Paper›PMID 34642750›Full record

ArticleDatabase : the journal of biological databases and curation2021

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

Open access · goldAbstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.3field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 35 citations in OpenAlex.

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  9. LiqBioer: a manually curated database of cancer biomarkers in body fluid.Database : the journal of biological databases and curation · 2022
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Dan ShaoDepartment of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA.ORCID 0000-0003-1050-2808
Lan HuangKey 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.
Yan WangKey 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.ORCID 0000-0002-4751-0708
Xueteng CuiDepartment of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China.
Yufei LiDepartment of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China.
Yao WangKey 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.
Qin MaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, 310G Lincoln tower, 1800 cannon drive, Columbus, OH 43210, USA.ORCID 0000-0002-3264-8392
Wei DuKey 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.ORCID 0000-0001-9872-4821
Juan CuiDepartment of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA.ORCID 0000-0001-6581-6850
Jilin University · CNChangchun University of Science and Technology · CNThe Ohio State University · USUniversity of Nebraska–Lincoln · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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/.

Indexed as

Body FluidsProteomeBiomarkersDatabases, ProteinHumansProteomicsSecretomeBiomarkersProteome

Identifiers

PMID34642750
PMCPMC8516408
OpenAlexW3205873152

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.