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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Updated: Jul 11, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

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

Bowen Zhao¹, Jing Zhao¹, Muyao Wang¹

¹State 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.

|November 3, 2023

Summary

Mip-mining is a new database for exploring microprotein functions using RNA sequencing data. It aids in understanding microprotein roles in health, stress, and biotechnology applications.

Keywords:

Disease Microprotein Mip-mining database RNA-Seq Stress response

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Published on: April 11, 2019

Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

Microproteins are vital across life, influencing cell physiology and human health.
Transcriptome data is abundant, but microprotein functional insights remain limited.
Existing resources do not specifically focus on microprotein functional analysis from transcriptomic data.

Purpose of the Study:

To introduce Mip-mining, a novel database for analyzing microprotein functions.
To provide a resource utilizing high-quality RNA-sequencing data exclusively for microprotein research.
To facilitate the exploration of microprotein roles in diverse biological contexts.

Main Methods:

Compilation of 336 high-quality transcriptome datasets from 8626 samples across nine species.
Inclusion of data covering various stress conditions (chemical, physical, biological, disease-related).
Development of a platform for customized analysis with user-defined parameters.

Main Results:

Mip-mining hosts extensive transcriptome data focused on microproteins.
The database enables identification of essential microproteins in different species.
Demonstrated the role of ATP15 in acetic acid stress tolerance in budding yeast.

Conclusions:

Mip-mining significantly enhances the understanding of microprotein functions.
The database supports applications in biotechnology and the development of therapeutic strategies.
Facilitates research into microprotein roles under various environmental and disease conditions.