Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

Marek Gierlinski; Christian Cole; Pietà Schofield; Nicholas J. Schurch; Alexander Sherstnev; Vijender Singh; Nicola Wrobel; Karim Gharbi; Gordon Simpson; Tom Owen-Hughes; Mark Blaxter; Geoffrey J. Barton

doi:10.1093/bioinformatics/btv425

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

Marek Gierlinski, Christian Cole, Pietà Schofield, Nicholas J. Schurch, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon Simpson, Tom Owen-Hughes, Mark Blaxter, Geoffrey J. Barton

Computational Biology

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Abstract

Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.
Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution.
Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.

Original language	English
Pages (from-to)	3625-3630
Number of pages	6
Journal	Bioinformatics
Volume	31
Issue number	22
Early online date	23 Jul 2015
DOIs	https://doi.org/10.1093/bioinformatics/btv425
Publication status	Published - 15 Nov 2015

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10.1093/bioinformatics/btv425Licence: CC BY

Final Published Version
Copyright The Author 2015. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Final published version, 406 KBLicence: CC BY

Diversifying Transcription Termination Function
Barton, G. (Investigator) & Simpson, G. (Investigator)
Biotechnology and Biological Sciences Research Council
1/06/15 → 31/05/19
Project: Research
The Arabidopsis Epitranscriptome (Joint with University of Nottingham)
Barton, G. (Investigator) & Simpson, G. (Investigator)
Biotechnology and Biological Sciences Research Council
1/04/15 → 31/03/19
Project: Research
Strategic Award: Wellcome Trust Technology Platform
Blow, J. (Investigator), Lamond, A. (Investigator) & Owen-Hughes, T. (Investigator)
1/01/13 → 30/09/18
Project: Research

Owen-Hughes, Tom
- Molecular Cell and Developmental Biology - Professor of Chromatin Structure and Dynamics
Person: Academic

Cite this

@article{f7805eb4538d4a62af55d9e844c1df4f,

title = "Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment",

abstract = "Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of {\textquoteleft}bad{\textquoteright} replicates, which can drastically affect the gene read-count distribution.Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.",

author = "Marek Gierlinski and Christian Cole and Piet{\`a} Schofield and Schurch, {Nicholas J.} and Alexander Sherstnev and Vijender Singh and Nicola Wrobel and Karim Gharbi and Gordon Simpson and Tom Owen-Hughes and Mark Blaxter and Barton, {Geoffrey J.}",

note = "This work was supported by: The Wellcome Trust (92530/Z/10/Z and strategic awards WT09230, WT083481 and WT097945), Biotechnology and Biological Sciences Research Council (BB/H002286/1 and BB/J00247X/1).",

year = "2015",

month = nov,

day = "15",

doi = "10.1093/bioinformatics/btv425",

language = "English",

volume = "31",

pages = "3625--3630",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

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TY - JOUR

T1 - Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

AU - Gierlinski, Marek

AU - Cole, Christian

AU - Schofield, Pietà

AU - Schurch, Nicholas J.

AU - Sherstnev, Alexander

AU - Singh, Vijender

AU - Wrobel, Nicola

AU - Gharbi, Karim

AU - Simpson, Gordon

AU - Owen-Hughes, Tom

AU - Blaxter, Mark

AU - Barton, Geoffrey J.

N1 - This work was supported by: The Wellcome Trust (92530/Z/10/Z and strategic awards WT09230, WT083481 and WT097945), Biotechnology and Biological Sciences Research Council (BB/H002286/1 and BB/J00247X/1).

PY - 2015/11/15

Y1 - 2015/11/15

N2 - Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution.Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.

AB - Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution.Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.

U2 - 10.1093/bioinformatics/btv425

DO - 10.1093/bioinformatics/btv425

M3 - Article

C2 - 26206307

SN - 1367-4803

VL - 31

SP - 3625

EP - 3630

JO - Bioinformatics

JF - Bioinformatics

IS - 22

ER -

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

Abstract

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Projects

Diversifying Transcription Termination Function

The Arabidopsis Epitranscriptome (Joint with University of Nottingham)

Strategic Award: Wellcome Trust Technology Platform

Profiles

Owen-Hughes, Tom

Cite this