Evidence map›Paper›PMID 39504480›Full record

ArticleBriefings in bioinformatics2024

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data.

Peng Ran, Yunzhi Wang, Kai Li, Shiman He, Subei Tan, Jiacheng Lv, Jiajun Zhu, Shaoshuai Tang, Jinwen Feng, Zhaoyu Qin and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

16 authors.

Peng RanCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Yunzhi WangCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Kai LiCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Shiman HeCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Subei TanCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Jiacheng LvCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Jiajun ZhuCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Shaoshuai TangCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Jinwen FengCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Zhaoyu QinCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Yan LiCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Lin HuangCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Yanan YinCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Lingli ZhuCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.
Wenjun YangDepartment of Pediatric Orthopedics, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai 200092, China.
Chen DingCenter for Cell and Gene Therapy, Clinical Research Center for Cell-based Immunotherapy, Shanghai Pudong Hospital, State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, E301, School of Life Sciences, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, P.R. China.ORCID 0000-0001-8673-3464

Funding

Fudan Original Research Personalized Support ProjectHuman Phenome Data Center of Fudan UniversityMajor Project of Special Development Funds of Zhangjiang National Independent Innovation Demonstration Zone ZJ2019-ZD-004National Key Research and Development Program of China 2022YFA1303200National Natural Science Foundation of China 32330062National Natural Science Foundation of China 82272166Shanghai Academic/Technology Research Leader 22XD1420100Shanghai Municipal Science and Technology Major ProjectShanghai Municipal Science and Technology Major Project 2023SHZDZX02Shanghai Natural Science Foundation 18ZR1446300Young Scientists Fund of the National Natural Science Foundation of China 32201212
6 · The paper itself

Abstract

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Indexed as

AlgorithmsBenchmarkingProteomicsDatabases, ProteinHumansMass SpectrometryReproducibility of ResultsSoftwarebioinformaticsdata-independent acquisitionnon-biological noiseproteomics analysisquantitative proteomicsSTAVER algorithm

Identifiers

PMID39504480
PMCPMC11540132

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.