IVT-seq reveals extreme bias in RNA sequencing

Nicholas F. Lahens, Ibrahim Halil Kavakli, Ray Zhang, Katharina Hayer, Michael B. Black, Hannah Dueck, Angel Pizarro, Junhyong Kim, Rafael Irizarry, Russell S. Thomas, Gregory R. Grant, John B. Hogenesch

Research output: Contribution to journalArticle

Abstract

Background: RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value. Results: We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation. Conclusions: These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.

Original languageEnglish (US)
Article numberR86
JournalGenome Biology
Volume15
Issue number6
DOIs
StatePublished - Jun 30 2014

Fingerprint

RNA Sequence Analysis
RNA
sequence analysis
transcription (genetics)
Libraries
ribosomal RNA
RNA libraries
fold
construction method
cDNA libraries
exons
In Vitro Techniques
Gene Library
methodology
Exons
Complementary DNA
library

ASJC Scopus subject areas

  • Cell Biology
  • Ecology, Evolution, Behavior and Systematics
  • Genetics

Cite this

Lahens, N. F., Kavakli, I. H., Zhang, R., Hayer, K., Black, M. B., Dueck, H., ... Hogenesch, J. B. (2014). IVT-seq reveals extreme bias in RNA sequencing. Genome Biology, 15(6), [R86]. https://doi.org/10.1186/gb-2014-15-6-r86

IVT-seq reveals extreme bias in RNA sequencing. / Lahens, Nicholas F.; Kavakli, Ibrahim Halil; Zhang, Ray; Hayer, Katharina; Black, Michael B.; Dueck, Hannah; Pizarro, Angel; Kim, Junhyong; Irizarry, Rafael; Thomas, Russell S.; Grant, Gregory R.; Hogenesch, John B.

In: Genome Biology, Vol. 15, No. 6, R86, 30.06.2014.

Research output: Contribution to journalArticle

Lahens, NF, Kavakli, IH, Zhang, R, Hayer, K, Black, MB, Dueck, H, Pizarro, A, Kim, J, Irizarry, R, Thomas, RS, Grant, GR & Hogenesch, JB 2014, 'IVT-seq reveals extreme bias in RNA sequencing', Genome Biology, vol. 15, no. 6, R86. https://doi.org/10.1186/gb-2014-15-6-r86
Lahens NF, Kavakli IH, Zhang R, Hayer K, Black MB, Dueck H et al. IVT-seq reveals extreme bias in RNA sequencing. Genome Biology. 2014 Jun 30;15(6). R86. https://doi.org/10.1186/gb-2014-15-6-r86
Lahens, Nicholas F. ; Kavakli, Ibrahim Halil ; Zhang, Ray ; Hayer, Katharina ; Black, Michael B. ; Dueck, Hannah ; Pizarro, Angel ; Kim, Junhyong ; Irizarry, Rafael ; Thomas, Russell S. ; Grant, Gregory R. ; Hogenesch, John B. / IVT-seq reveals extreme bias in RNA sequencing. In: Genome Biology. 2014 ; Vol. 15, No. 6.
@article{92389d5f2ea9465cb4808a6362e4abc5,
title = "IVT-seq reveals extreme bias in RNA sequencing",
abstract = "Background: RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value. Results: We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50{\%} of transcripts have more than two-fold and 10{\%} have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6{\%} of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation. Conclusions: These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.",
author = "Lahens, {Nicholas F.} and Kavakli, {Ibrahim Halil} and Ray Zhang and Katharina Hayer and Black, {Michael B.} and Hannah Dueck and Angel Pizarro and Junhyong Kim and Rafael Irizarry and Thomas, {Russell S.} and Grant, {Gregory R.} and Hogenesch, {John B.}",
year = "2014",
month = "6",
day = "30",
doi = "10.1186/gb-2014-15-6-r86",
language = "English (US)",
volume = "15",
journal = "Genome Biology",
issn = "1474-7596",
publisher = "BioMed Central",
number = "6",

}

TY - JOUR

T1 - IVT-seq reveals extreme bias in RNA sequencing

AU - Lahens, Nicholas F.

AU - Kavakli, Ibrahim Halil

AU - Zhang, Ray

AU - Hayer, Katharina

AU - Black, Michael B.

AU - Dueck, Hannah

AU - Pizarro, Angel

AU - Kim, Junhyong

AU - Irizarry, Rafael

AU - Thomas, Russell S.

AU - Grant, Gregory R.

AU - Hogenesch, John B.

PY - 2014/6/30

Y1 - 2014/6/30

N2 - Background: RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value. Results: We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation. Conclusions: These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.

AB - Background: RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value. Results: We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation. Conclusions: These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.

UR - http://www.scopus.com/inward/record.url?scp=84911861819&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84911861819&partnerID=8YFLogxK

U2 - 10.1186/gb-2014-15-6-r86

DO - 10.1186/gb-2014-15-6-r86

M3 - Article

C2 - 24981968

AN - SCOPUS:84911861819

VL - 15

JO - Genome Biology

JF - Genome Biology

SN - 1474-7596

IS - 6

M1 - R86

ER -