scholarly journals HRIBO: high-throughput analysis of bacterial ribosome profiling data

Author(s):  
Rick Gelhausen ◽  
Sarah L Svensson ◽  
Kathrin Froschauer ◽  
Florian Heyl ◽  
Lydia Hadjeras ◽  
...  

Abstract Motivation 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). Results 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. Availabilityand implementation HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO.

Author(s):  
Rick Gelhausen ◽  
Florian Heyl ◽  
Sarah L. Svensson ◽  
Kathrin Froschauer ◽  
Lydia Hadjeras ◽  
...  

AbstractMotivationRibosome profiling (Ribo-seq) is a powerful approach based on ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (<=70 amino acids) that are recalcitrant to biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the analysis of Ribo-seq data from prokaryotes, nor are they focused on the discovery of unannotated open reading frames (ORFs) in bacteria.ResultsWe 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 features. This facilitates the rapid discovery of novel ORFs and their prioritization for functional characterization.AvailabilityHRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO


2010 ◽  
Author(s):  
Elisabeth Kuhn ◽  
Elisabeth Naschberger ◽  
Andreas Konrad ◽  
Roland S. Croner ◽  
Helmut Münstedt ◽  
...  

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Sondos Samandi ◽  
Annie V Roy ◽  
Vivian Delcourt ◽  
Jean-François Lucier ◽  
Jules Gagnon ◽  
...  

Recent functional, proteomic and ribosome profiling studies in eukaryotes have concurrently demonstrated the translation of alternative open-reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by these altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and contain functional domains. Evolutionary analyses indicate that altORFs often show more extreme conservation patterns than their CDSs. Thousands of alternative proteins are detected in proteomic datasets by reanalysis using a database containing predicted alternative proteins. This is illustrated with specific examples, including altMiD51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission. Our results suggest that many genes are multicoding genes and code for a large protein and one or several small proteins.


2015 ◽  
Vol 11 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Luciano Cardoso ◽  
Suellen Cordeiro ◽  
Marcio Fronza ◽  
Denise Endringer ◽  
Tadeu de Andrade ◽  
...  

Author(s):  
Ruoxing Lei ◽  
Erin A. Akins ◽  
Kelly C. Y. Wong ◽  
Nicole A. Repina ◽  
Kayla J. Wolf ◽  
...  

The Analyst ◽  
2021 ◽  
Author(s):  
Jiawei Qi ◽  
Pinhua Rao ◽  
Lele Wang ◽  
Li Xu ◽  
Yanli Wen ◽  
...  

Pattern recognition, also called “array sensing” is a recognition strategy with a wide and expandable analysis range, based on the high-throughput analysis data. In this work, we constructed a sensor...


Author(s):  
Xiaojia Jiang ◽  
Mingsong Zang ◽  
Fei Li ◽  
Chunxi Hou ◽  
Quan Luo ◽  
...  

Biological nanopore-based techniques have attracted more and more attention recently in the field of single-molecule detection, because they allow the real-time, sensitive, high-throughput analysis. Herein, we report an engineered biological...


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