@article{btaa959,
    author = {Gelhausen, Rick and Svensson, Sarah L and Froschauer, Kathrin and Heyl, Florian and Hadjeras, Lydia and Sharma, Cynthia M and Eggenhofer, Florian and Backofen, Rolf},
    title = "{HRIBO: high-throughput analysis of bacterial ribosome profiling data}",
    journal = {Bioinformatics},
    year = {2020},
    month = {11},
    abstract = "{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.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa959},
    url = {https://doi.org/10.1093/bioinformatics/btaa959},
    note = {btaa959},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa959/34484598/btaa959.pdf},
  user =	 {egg}
}