Literature DB >> 34705568

Accurate Quantification of Overlapping Herpesvirus Transcripts from RNA Sequencing Data.

Alejandro Casco1, Akansha Gupta1, Mitchell Hayes1, Reza Djavadian1, Makoto Ohashi1, Eric Johannsen1,2.   

Abstract

Herpesviruses employ extensive bidirectional transcription of overlapping genes to overcome length constraints on their gene product repertoire. As a consequence, many lytic transcripts cannot be measured individually by reverse transcription-quantitative PCR (RT-qPCR) or conventional RNA sequencing (RNA-seq) analysis. A. G. Bruce, S. Barcy, T. DiMaio, E. Gan, et al. (Pathogens 6:11, 2017, https://doi.org/10.3390/pathogens6010011) have proposed an approximation method using unique coding sequences (UCDS) to estimate lytic gene abundance from Kaposi's sarcoma-associated herpesvirus (KSHV) RNA-seq data. Although UCDS has been widely employed, its accuracy, to our knowledge, has never been rigorously validated for any herpesvirus. In this study, we use cap analysis of gene expression sequencing (CAGE-seq) as a gold-standard to determine the accuracy of UCDS for estimating Epstein-Barr virus (EBV) lytic gene expression levels from RNA-seq data. We also introduce the Unique TranScript (UTS) method, which, like UCDS, estimates transcript abundance from changes in mean RNA-seq read depth. UTS is distinguished by its use of empirically determined 5' and 3' transcript ends rather than coding sequence annotations. Compared to conventional read assignment, both UCDS and UTS improved the accuracy of quantitation of overlapping genes, with UTS giving the most-accurate results. The UTS method discards fewer reads and may be advantageous for experiments with less sequencing depth. UTS is compatible with any aligner and, unlike isoform-aware alignment methods, can be implemented on a laptop computer. Our findings demonstrate that the accuracy achieved by complex and expensive techniques such as CAGE-seq can be approximated using conventional short-read RNA-seq data when read assignment methods address transcript overlap. Although our study focuses on EBV transcription, the UTS method should be applicable across all herpesviruses as well as to other genomes with extensively overlapping transcriptomes. IMPORTANCE Many viruses employ extensively overlapping transcript structures. This complexity makes it difficult to quantify gene expression by using conventional methods, including RNA-seq. Although high-throughput techniques that overcome these limitations exist, they are complex, expensive, and scarce in the herpesvirus literature relative to short-read RNA-seq. Here, using Epstein-Barr virus (EBV) as a model, we demonstrate that conventional RNA-seq analysis methods fail to accurately quantify the abundances of many overlapping transcripts. We further show that the previously described Unique CoDing Sequence (UCDS) method and our Unique TranScript (UTS) method greatly improve the accuracy of EBV lytic gene measurements obtained from RNA-seq data. The UTS method has the advantages of discarding fewer reads and being implementable on a laptop computer. Although this study focuses on EBV, the UCDS and UTS methods should be applicable across herpesviruses and for other viruses that make extensive use of overlapping transcription.

Entities:  

Keywords:  EBV; RNA-seq; herpesvirus; transcription

Mesh:

Substances:

Year:  2021        PMID: 34705568      PMCID: PMC8791286          DOI: 10.1128/JVI.01635-21

Source DB:  PubMed          Journal:  J Virol        ISSN: 0022-538X            Impact factor:   6.549


  20 in total

1.  STAR: ultrafast universal RNA-seq aligner.

Authors:  Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras
Journal:  Bioinformatics       Date:  2012-10-25       Impact factor: 6.937

2.  Comprehensive profiling of EBV gene expression in nasopharyngeal carcinoma through paired-end transcriptome sequencing.

Authors:  Lijuan Hu; Zhirui Lin; Yanheng Wu; Juqin Dong; Bo Zhao; Yanbing Cheng; Peiyu Huang; Lihua Xu; Tianliang Xia; Dan Xiong; Hongbo Wang; Manzhi Li; Ling Guo; Elliott Kieff; Yixin Zeng; Qian Zhong; Musheng Zeng
Journal:  Front Med       Date:  2016-03-11       Impact factor: 4.592

3.  Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing.

Authors:  Hideya Kawaji; Marina Lizio; Masayoshi Itoh; Mutsumi Kanamori-Katayama; Ai Kaiho; Hiromi Nishiyori-Sueki; Jay W Shin; Miki Kojima-Ishiyama; Mitsuoki Kawano; Mitsuyoshi Murata; Noriko Ninomiya-Fukuda; Sachi Ishikawa-Kato; Sayaka Nagao-Sato; Shohei Noma; Yoshihide Hayashizaki; Alistair R R Forrest; Piero Carninci
Journal:  Genome Res       Date:  2014-03-27       Impact factor: 9.043

4.  Interaction of Epstein-Barr virus genes with human gastric carcinoma transcriptome.

Authors:  Ruiyuan Zhang; Michael J Strong; Melody Baddoo; Zhen Lin; Yu-Ping Wang; Erik K Flemington; Yao-Zhong Liu
Journal:  Oncotarget       Date:  2017-06-13

5.  mmquant: how to count multi-mapping reads?

Authors:  Matthias Zytnicki
Journal:  BMC Bioinformatics       Date:  2017-09-15       Impact factor: 3.169

6.  How Kaposi's sarcoma-associated herpesvirus stably transforms peripheral B cells towards lymphomagenesis.

Authors:  Aurélia Faure; Mitch Hayes; Bill Sugden
Journal:  Proc Natl Acad Sci U S A       Date:  2019-07-30       Impact factor: 11.205

7.  CAGE-seq analysis of Epstein-Barr virus lytic gene transcription: 3 kinetic classes from 2 mechanisms.

Authors:  Reza Djavadian; Mitchell Hayes; Eric Johannsen
Journal:  PLoS Pathog       Date:  2018-06-04       Impact factor: 6.823

8.  Quantitative RNAseq analysis of Ugandan KS tumors reveals KSHV gene expression dominated by transcription from the LTd downstream latency promoter.

Authors:  Timothy M Rose; A Gregory Bruce; Serge Barcy; Matt Fitzgibbon; Lisa R Matsumoto; Minako Ikoma; Corey Casper; Jackson Orem; Warren Phipps
Journal:  PLoS Pathog       Date:  2018-12-17       Impact factor: 6.823

Review 9.  Transcriptional Control by Premature Termination: A Forgotten Mechanism.

Authors:  Kinga Kamieniarz-Gdula; Nick J Proudfoot
Journal:  Trends Genet       Date:  2019-06-15       Impact factor: 11.639

10.  deepTools2: a next generation web server for deep-sequencing data analysis.

Authors:  Fidel Ramírez; Devon P Ryan; Björn Grüning; Vivek Bhardwaj; Fabian Kilpert; Andreas S Richter; Steffen Heyne; Friederike Dündar; Thomas Manke
Journal:  Nucleic Acids Res       Date:  2016-04-13       Impact factor: 16.971

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  2 in total

1.  Reduced IRF4 expression promotes lytic phenotype in Type 2 EBV-infected B cells.

Authors:  Jillian A Bristol; Joshua Brand; Makoto Ohashi; Mark R Eichelberg; Alejandro Casco; Scott E Nelson; Mitchell Hayes; James C Romero-Masters; Dana C Baiu; Jenny E Gumperz; Eric C Johannsen; Huy Q Dinh; Shannon C Kenney
Journal:  PLoS Pathog       Date:  2022-04-26       Impact factor: 7.464

2.  Rta is the principal activator of Epstein-Barr virus epithelial lytic transcription.

Authors:  Ahmed Ali; Makoto Ohashi; Alejandro Casco; Reza Djavadian; Mark Eichelberg; Shannon C Kenney; Eric Johannsen
Journal:  PLoS Pathog       Date:  2022-09-29       Impact factor: 7.464

  2 in total

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