RiboNT: A Noise-Tolerant Predictor of Open Reading Frames from Ribosome-Protected Footprints

文献类型： 外文期刊

第一作者： Song, Bo

作者： Song, Bo;Gao, Lei;Song, Bo;Jiang, Mengyun;Jiang, Mengyun;Jiang, Mengyun

作者机构：

关键词： Ribo-seq; ribosome profiling; ORFs; small ORFs; periodicity; RPFs

期刊名称：LIFE-BASEL （ 影响因子：3.817； ）

ISSN：

年卷期： 2021 年 11 卷 7 期

页码：

收录情况： SCI

摘要： Ribo-seq, also known as ribosome profiling, refers to the sequencing of ribosome-protected mRNA fragments (RPFs). This technique has greatly advanced our understanding of translation and facilitated the identification of novel open reading frames (ORFs) within untranslated regions or non-coding sequences as well as the identification of non-canonical start codons. However, the widespread application of Ribo-seq has been hindered because obtaining periodic RPFs requires a highly optimized protocol, which may be difficult to achieve, particularly in non-model organisms. Furthermore, the periodic RPFs are too short (28 nt) for accurate mapping to polyploid genomes, but longer RPFs are usually produced with a compromise in periodicity. Here we present RiboNT, a noise-tolerant ORF predictor that can utilize RPFs with poor periodicity. It evaluates RPF periodicity and automatically weighs the support from RPFs and codon usage before combining their contributions to identify translated ORFs. The results demonstrate the utility of RiboNT for identifying both long and small ORFs using RPFs with either good or poor periodicity. We implemented the pipeline on a dataset of RPFs with poor periodicity derived from membrane-bound polysomes of Arabidopsis thaliana seedlings and identified several small ORFs (sORFs) evolutionarily conserved in diverse plant species. RiboNT should greatly broaden the application of Ribo-seq by minimizing the requirement of RPF quality and allowing the use of longer RPFs, which is critical for organisms with complex genomes because these RPFs can be more accurately mapped to the position from which they were derived.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序