开放式参考框架
核糖体分析
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生物
核糖体
计算生物学
基因
编码
蛋白质组
遗传学
转录组
基因表达
核糖核酸
肽序列
作者
Lorenzo Calviello,Neelanjan Mukherjee,Emanuel Wyler,Henrik Zauber,Antje Hirsekorn,Matthias Selbach,Markus Landthaler,Benedikt Obermayer,Uwe Ohler
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2015-12-14
卷期号:13 (2): 165-170
被引量:411
摘要
RiboTaper quantifies the three-nucleotide periodicity in Ribo-seq data to find translated open reading frames (ORFs). The de novo inferred set of ORFs comprehensively defines the cellular proteome across a wide expression range and comprises few additional translated noncoding regions. RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/ ), a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq data. We used RiboTaper with deep Ribo-seq data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq data.
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