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FSPP:一种用于全基因组预测小开放阅读框编码肽及其功能的工具。

FSPP: A Tool for Genome-Wide Prediction of smORF-Encoded Peptides and Their Functions.

作者信息

Li Hui, Xiao Li, Zhang Lili, Wu Jiarui, Wei Bin, Sun Ninghui, Zhao Yi

机构信息

Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

School of Computer and Control Engineering, University of Chinese Academy of Sciences (UCAS), Beijing, China.

出版信息

Front Genet. 2018 Apr 5;9:96. doi: 10.3389/fgene.2018.00096. eCollection 2018.

Abstract

smORFs are small open reading frames of less than 100 codons. Recent low throughput experiments showed a lot of smORF-encoded peptides (SEPs) played crucial rule in processes such as regulation of transcription or translation, transportation through membranes and the antimicrobial activity. In order to gather more functional SEPs, it is necessary to have access to genome-wide prediction tools to give profound directions for low throughput experiments. In this study, we put forward a functional smORF-encoded peptides predictor (FSPP) which tended to predict authentic SEPs and their functions in a high throughput method. FSPP used the overlap of detected SEPs from Ribo-seq and mass spectrometry as target objects. With the expression data on transcription and translation levels, FSPP built two co-expression networks. Combing co-location relations, FSPP constructed a compound network and then annotated SEPs with functions of adjacent nodes. Tested on 38 sequenced samples of 5 human cell lines, FSPP successfully predicted 856 out of 960 annotated proteins. Interestingly, FSPP also highlighted 568 functional SEPs from these samples. After comparison, the roles predicted by FSPP were consistent with known functions. These results suggest that FSPP is a reliable tool for the identification of functional small peptides. FSPP source code can be acquired at https://www.bioinfo.org/FSPP.

摘要

小开放阅读框(smORFs)是指长度小于100个密码子的开放阅读框。最近的低通量实验表明,许多小开放阅读框编码的肽(SEPs)在转录或翻译调控、跨膜运输和抗菌活性等过程中发挥着关键作用。为了收集更多具有功能的SEPs,有必要使用全基因组预测工具,为低通量实验提供深入指导。在本研究中,我们提出了一种功能性小开放阅读框编码肽预测器(FSPP),它倾向于以高通量方法预测真实的SEPs及其功能。FSPP将核糖体图谱(Ribo-seq)和质谱检测到的SEPs重叠部分作为目标对象。利用转录和翻译水平的表达数据,FSPP构建了两个共表达网络。结合共定位关系,FSPP构建了一个复合网络,然后用相邻节点的功能对SEPs进行注释。在5种人类细胞系的38个测序样本上进行测试,FSPP成功预测了960个注释蛋白中的856个。有趣的是,FSPP还从这些样本中突出显示了568个具有功能的SEPs。经过比较,FSPP预测的作用与已知功能一致。这些结果表明,FSPP是一种可靠的功能性小肽鉴定工具。FSPP的源代码可在https://www.bioinfo.org/FSPP获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5f1/5896265/e6ed3baba52e/fgene-09-00096-g001.jpg

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