PDF(Pubmed)
Abstract：
Accurate and comprehensive annotation of microprotein-coding small open reading frames (smORFs) is critical to our understanding of normal physiology and disease. Empirical identification of translated smORFs is carried out primarily using ribosome profiling (Ribo-seq). While effective, published Ribo-seq datasets can vary drastically in quality and different analysis tools are frequently employed. Here, we examine the impact of these factors on identifying translated smORFs. We compared five commonly used software tools that assess open reading frame translation from Ribo-seq (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH) and found surprisingly low agreement across all tools. Only ~2% of smORFs were called translated by all five tools, and ~15% by three or more tools when assessing the same high-resolution Ribo-seq dataset. For larger annotated genes, the same analysis showed ~74% agreement across all five tools. We also found that some tools are strongly biased against low-resolution Ribo-seq data, while others are more tolerant. Analyzing Ribo-seq coverage revealed that smORFs detected by more than one tool tend to have higher translation levels and higher fractions of in-frame reads, consistent with what was observed for annotated genes. Together these results support employing multiple tools to identify the most confident microprotein-coding smORFs and choosing the tools based on the quality of the dataset and the planned downstream characterization experiments of the predicted smORFs.
摘要：
对编码微蛋白的小型开放阅读框（sMORFs）进行准确而全面的注释对于我们对正常生理和疾病的理解至关重要。翻译的sMORF的经验鉴定主要使用核糖体谱分析(Ribo-seq)进行。虽然有效，已发布的Ribo-seq数据集的质量可能会有很大差异，并且经常使用不同的分析工具。这里,我们研究了这些因素对识别翻译的sMORF的影响。我们比较了五种常用的软件工具，这些工具可以评估Ribo-seq的开放阅读框架翻译（RibORFv0.1，RibORFv1.0，RiboCode，ORFQuant,和Ribo-TISH)，并发现所有工具的一致性令人惊讶地很低。只有约2%的sMORF被所有五种工具翻译，在评估相同的高分辨率Ribo-seq数据集时，三个或更多工具使用~15%。对于更大的注释基因，同样的分析显示，所有五种工具的一致性约为74%。我们还发现，一些工具强烈偏向于低分辨率Ribo-seq数据，而其他人则更宽容。分析Ribo-seq覆盖率表明，由多个工具检测到的sMORF往往具有更高的翻译水平和更高的框内读数分数，与注释基因的观察结果一致。这些结果一起支持采用多种工具来鉴定最自信的编码微蛋白的sMORF，并基于数据集的质量和预测的sMORF的计划下游表征实验来选择工具。
