Literature DB >> 26463378

Exploring Ribosome Positioning on Translating Transcripts with Ribosome Profiling.

Pieter Spealman1, Hao Wang2, Gemma May1, Carl Kingsford2, C Joel McManus3.   

Abstract

Recent technological advances (e.g., microarrays and massively parallel sequencing) have facilitated genome-wide measurement of many aspects of gene regulation. Ribosome profiling is a high-throughput sequencing method used to measure gene expression at the level of translation. This is accomplished by quantifying both the number of translating ribosomes and their locations on mRNA transcripts. The inventors of this approach have published several methods papers detailing its implementation and addressing the basics of ribosome profiling data analysis. Here we describe our lab's procedure, which differs in some respects from those published previously. In addition, we describe a data analysis pipeline, Ribomap, for ribosome profiling data. Ribomap allocates sequence reads to alternative mRNA isoforms, normalizes sequencing bias along transcripts using RNA-seq data, and outputs count vectors of per-codon ribosome occupancy for each transcript.

Keywords:  Bioinformatics; High-throughput sequencing; Ribo-seq; Ribomap; Ribosome Profiling; Translation; Yeast

Mesh:

Substances:

Year:  2016        PMID: 26463378     DOI: 10.1007/978-1-4939-3067-8_5

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

1.  uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast.

Authors:  Pieter Spealman; Armaghan Naik; Joel McManus
Journal:  Methods Mol Biol       Date:  2021

2.  Translating Ribosome Affinity Purification (TRAP) of Cell Type-specific mRNA from Mouse Brain Lysates.

Authors:  Catherine L Salussolia; Kellen D Winden; Mustafa Sahin
Journal:  Bio Protoc       Date:  2022-05-05

3.  Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data.

Authors:  Pieter Spealman; Armaghan W Naik; Gemma E May; Scott Kuersten; Lindsay Freeberg; Robert F Murphy; Joel McManus
Journal:  Genome Res       Date:  2017-12-18       Impact factor: 9.043

4.  Global translational landscape of the Candida albicans morphological transition.

Authors:  Vasanthakrishna Mundodi; Saket Choudhary; Andrew D Smith; David Kadosh
Journal:  G3 (Bethesda)       Date:  2021-02-09       Impact factor: 3.154

5.  XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data.

Authors:  Jordan A Berg; Jonathan R Belyeu; Jeffrey T Morgan; Yeyun Ouyang; Alex J Bott; Aaron R Quinlan; Jason Gertz; Jared Rutter
Journal:  PLoS Comput Biol       Date:  2020-01-31       Impact factor: 4.475

  5 in total

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