Song Bo, Jiang Mengyun, Gao Lei
Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China.
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
Life (Basel). 2021 Jul 16;11(7):701. doi: 10.3390/life11070701.
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 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.
核糖体测序（Ribo-seq），也称为核糖体谱分析，是指对核糖体保护的mRNA片段（RPFs）进行测序。这项技术极大地推进了我们对翻译的理解，并有助于在非翻译区或非编码序列中识别新的开放阅读框（ORF）以及非经典起始密码子。然而，Ribo-seq的广泛应用受到了阻碍，因为获得周期性的RPFs需要高度优化的方案，这可能难以实现，尤其是在非模式生物中。此外，周期性的RPFs太短（28个核苷酸），无法准确映射到多倍体基因组，但较长的RPFs通常在周期性上有所妥协。在这里，我们展示了RiboNT，一种耐噪声的ORF预测器，它可以利用周期性较差的RPFs。它评估RPF的周期性，并在结合它们对识别翻译的ORF的贡献之前，自动权衡来自RPFs的支持和密码子使用情况。结果证明了RiboNT在使用周期性好或差的RPFs识别长ORF和小ORF方面的实用性。我们在从幼苗膜结合多核糖体衍生的周期性较差的RPFs数据集上实施了该流程，并鉴定了几种在不同植物物种中进化保守的小ORF（sORFs）。RiboNT应该通过最小化对RPF质量的要求并允许使用更长的RPFs来极大地拓宽Ribo-seq的应用，这对于具有复杂基因组的生物体至关重要，因为这些RPFs可以更准确地映射到它们的来源位置。
