Literature DB >> 28887026

Beyond Read-Counts: Ribo-seq Data Analysis to Understand the Functions of the Transcriptome.

Lorenzo Calviello1, Uwe Ohler2.   

Abstract

By mapping the positions of millions of translating ribosomes in the cell, ribosome profiling (Ribo-seq) has established its role as a powerful tool to study gene expression. Several laboratories have introduced modifications to the experimental protocol and expanded the repertoire of biochemical methods to study translation transcriptome-wide. However, the diversity of protocols highlights a need for standardization. At the same time, different computational analysis strategies have used Ribo-seq data to identify the set of translated sequences with high confidence. In this review we present an overview of such methodologies, outlining their assumptions, data requirements, and availability. At the interface between RNA and proteins, Ribo-seq can complement data from multiple omics approaches, zooming in on the central role of translation in the molecular cell.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Keywords:  Ribo-seq; bioinformatics; genomics; transcriptomics; translation

Mesh:

Year:  2017        PMID: 28887026     DOI: 10.1016/j.tig.2017.08.003

Source DB:  PubMed          Journal:  Trends Genet        ISSN: 0168-9525            Impact factor:   11.639


  34 in total

1.  Ribosome elongating footprints denoised by wavelet transform comprehensively characterize dynamic cellular translation events.

Authors:  Zhiyu Xu; Long Hu; Binbin Shi; SiSi Geng; Longchen Xu; Dong Wang; Zhi J Lu
Journal:  Nucleic Acids Res       Date:  2018-10-12       Impact factor: 16.971

2.  Proteogenomic Annotation of Chinese Hamsters Reveals Extensive Novel Translation Events and Endogenous Retroviral Elements.

Authors:  Shangzhong Li; Seong Won Cha; Kelly Heffner; Deniz Baycin Hizal; Michael A Bowen; Raghothama Chaerkady; Robert N Cole; Vijay Tejwani; Prashant Kaushik; Michael Henry; Paula Meleady; Susan T Sharfstein; Michael J Betenbaugh; Vineet Bafna; Nathan E Lewis
Journal:  J Proteome Res       Date:  2019-05-08       Impact factor: 4.466

3.  Recommendations for bacterial ribosome profiling experiments based on bioinformatic evaluation of published data.

Authors:  Alina Glaub; Christopher Huptas; Klaus Neuhaus; Zachary Ardern
Journal:  J Biol Chem       Date:  2020-05-08       Impact factor: 5.157

4.  Selective ribosome profiling to study interactions of translating ribosomes in yeast.

Authors:  Carla V Galmozzi; Dorina Merker; Ulrike A Friedrich; Kristina Döring; Günter Kramer
Journal:  Nat Protoc       Date:  2019-07-22       Impact factor: 13.491

5.  RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria.

Authors:  Rick Gelhausen; Teresa Müller; Sarah L Svensson; Omer S Alkhnbashi; Cynthia M Sharma; Florian Eggenhofer; Rolf Backofen
Journal:  Brief Bioinform       Date:  2022-03-10       Impact factor: 11.622

6.  Using the Ribodeblur pipeline to recover A-sites from yeast ribosome profiling data.

Authors:  Hao Wang; Carl Kingsford; C Joel McManus
Journal:  Methods       Date:  2018-01-09       Impact factor: 3.608

Review 7.  Alternative splicing in aging and longevity.

Authors:  Malini Bhadra; Porsha Howell; Sneha Dutta; Caroline Heintz; William B Mair
Journal:  Hum Genet       Date:  2019-12-13       Impact factor: 4.132

8.  Quantification of translation uncovers the functions of the alternative transcriptome.

Authors:  Lorenzo Calviello; Antje Hirsekorn; Uwe Ohler
Journal:  Nat Struct Mol Biol       Date:  2020-06-29       Impact factor: 15.369

9.  Robust single-cell discovery of RNA targets of RNA-binding proteins and ribosomes.

Authors:  Kristopher W Brannan; Isaac A Chaim; Ryan J Marina; Brian A Yee; Eric R Kofman; Daniel A Lorenz; Pratibha Jagannatha; Kevin D Dong; Assael A Madrigal; Jason G Underwood; Gene W Yeo
Journal:  Nat Methods       Date:  2021-05-07       Impact factor: 47.990

10.  The 18S rRNA m6 A methyltransferase METTL5 promotes mouse embryonic stem cell differentiation.

Authors:  Ming Xing; Qi Liu; Cong Mao; Hanyi Zeng; Xin Zhang; Shuqin Zhao; Li Chen; Mingxi Liu; Bin Shen; Xuejiang Guo; Honghui Ma; Hao Chen; Jun Zhang
Journal:  EMBO Rep       Date:  2020-08-11       Impact factor: 8.807

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.