Evidence map›Paper›PMID 37919660›Full record

ArticleBMC genomics2023

Exploring microproteins from various model organisms using the mip-mining database.

Bowen Zhao, Jing Zhao, Muyao Wang, Yangfan Guo, Aamir Mehmood, Weibin Wang, Yi Xiong, Shenggan Luo, Dong-Qing Wei, Xin-Qing Zhao and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2023. 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. Chasing the Ghost Proteome in the Dark Matter.Molecular & cellular proteomics : MCP · 2025
    Review
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

11 authors.

Bowen Zhao *State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jing Zhao *State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Muyao WangState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yangfan GuoCentral Laboratory of Yan'an Hospital Affiliated to Kunming Medical University, Kunming, 650051, China.
Aamir MehmoodState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Weibin WangState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yi XiongState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shenggan LuoState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Dong-Qing WeiState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. dqwei@sjtu.edu.cn.
Xin-Qing ZhaoState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xqzhao@sjtu.edu.cn.
Yanjing WangState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. wangyanjing@sjtu.edu.cn.

Funding

Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University No. YG2022QN114National Natural Science Foundation of China No. 32200531National Natural Science Foundation of China No. 61832019Science and Technology Commission of Shanghai Municipality No. 19430750600SJTU JiRLMDS Joint Research Fund and Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University No. YG2021ZD02State Key Research and Development Program No. 2022YFE0108500
6 · The paper itself

Abstract

Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.

Indexed as

BiotechnologyTranscriptomeAnimalsHumansMicropeptidesSequence Analysis, RNAMicropeptidesDiseaseMicroproteinMip-mining databaseRNA-SeqStress response

Identifiers

PMID37919660
PMCPMC10623795

What OpenQuestion holds

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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.