HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae510
Xinwan Su, Chengyu Shi, Fangzhou Liu, Manman Tan, Ying Wang, Linyu Zhu, Yu Chen, Meng Yu, Xinyi Wang, Jian Liu, Yang Liu, Weiqiang Lin, Zhaoyuan Fang, Qiang Sun, Tianhua Zhou, Aifu Lin
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Abstract

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn.

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HMPA:非典型肽组从发现到功能深入研究的开创性框架。
肽组学的进步揭示了许多具有编码潜力的小型开放阅读框,并发现其中一些微肽与人类癌症密切相关。然而,从序列到结构和功能的系统分析和整合在很大程度上仍未得到发展。在这里,作为一种解决方案,我们建立了一个工作流程,利用来自大型队列的公开数据集,收集和分析与癌症相关的微肽的蛋白质组数据、转录组数据和临床结果。通过重新分析 8 种癌症类型 3753 个样本的蛋白质组数据,我们初步鉴定出 19 586 种新型微肽。通过对这些微肽以及相关临床数据的进一步定量分析,我们发现了3065种在癌症中调控失调的微肽,其中370种与预后密切相关。此外,我们还利用深度学习框架构建了微肽-蛋白质相互作用网络,用于进一步的生物信息学分析,揭示了微肽作为生物活性分子参与了多种生物过程。总之,我们的图集为微肽的高通量预测和功能探索提供了一个基准,为了解微肽在癌症中的生物学机制提供了新的视角。HMPA 可在 http://hmpa.zju.edu.cn 免费获取。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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