Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany.
Chair of Molecular Infection Biology II, Institute of Molecular Infection Biology (IMIB), University of Würzburg, 97080 Würzburg, Germany.
Bioinformatics. 2021 Aug 4;37(14):2061-2063. doi: 10.1093/bioinformatics/btaa959.
Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50-100 amino acids) that are recalcitrant to many standard biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the automatic processing and analysis of data from bacteria, nor are they focused on the discovery of unannotated open reading frames (ORFs).
We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional feature information and expression values. This facilitates the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization.
HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO.
核糖体图谱(Ribo-seq)是一种强大的方法,基于核糖体保护的 RNA 片段的 cDNA 文库的深度测序,以探索细胞的翻译组,特别适用于检测许多标准生化和计算机方法难以处理的小蛋白质(50-100 个氨基酸)。虽然有用于分析 Ribo-seq 数据的管道,但没有一个专门用于自动处理和分析细菌数据,也没有专注于发现未注释的开放阅读框(ORF)。
我们提出了 HRIBO(通过 Ribo-seq 进行高通量注释),这是一个工作流程,能够对细菌 Ribo-seq 数据进行可重复和高通量的分析。该工作流程执行所有必需的预处理和质量控制步骤。重要的是,HRIBO 根据两个互补的专注于细菌的工具输出与注释无关的 ORF 预测,并将它们与其他特征信息和表达值集成在一起。这有助于快速、高置信度地发现新的 ORF,并对其进行功能表征的优先级排序。
HRIBO 是一个免费的开源项目,根据 GPL-3 许可证在以下网址提供:https://github.com/RickGelhausen/HRIBO。