HRIBO: high-throughput analysis of bacterial ribosome profiling data | Zendy

Rick Gelhausen | Zendy; Sarah L. Svensson | Zendy; Kathrin Froschauer | Zendy; Florian Heyl | Zendy; Lydia Hadjeras | Zendy; Cynthia M. Sharma | Zendy; Florian Eggenhofer | Zendy; Rolf Backofen | Zendy

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HRIBO: high-throughput analysis of bacterial ribosome profiling data

Author(s) -

Rick Gelhausen,

Sarah L. Svensson,

Kathrin Froschauer,

Florian Heyl,

Lydia Hadjeras,

Cynthia M. Sharma,

Florian Eggenhofer,

Rolf Backofen

Publication year - 2020

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa959

Subject(s) - ribosome profiling , computer science , orfs , annotation , workflow , mit license , computational biology , profiling (computer programming) , ribosome , open reading frame , data mining , biology , database , software , artificial intelligence , rna , genetics , peptide sequence , gene , programming language

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).

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