DegNorm

Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis

Bin Xiong, Yiben Yang, Frank R. Fineis, Jiping Wang*

*Corresponding author for this work

Research output: Contribution to journalReview article

Abstract

RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.

Original languageEnglish (US)
Article number75
JournalGenome biology
Volume20
Issue number1
DOIs
StatePublished - Apr 16 2019

Fingerprint

RNA
degradation
Genes
gene
RNA Stability
genes
normalisation
analysis
cells
sampling
methodology

Keywords

  • Alternative splicing
  • Degradation normalization
  • Non-negative matrix factorization
  • Normalization
  • RNA degradation
  • RNA-seq

ASJC Scopus subject areas

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

Cite this

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title = "DegNorm: Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis",
abstract = "RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.",
keywords = "Alternative splicing, Degradation normalization, Non-negative matrix factorization, Normalization, RNA degradation, RNA-seq",
author = "Bin Xiong and Yiben Yang and Fineis, {Frank R.} and Jiping Wang",
year = "2019",
month = "4",
day = "16",
doi = "10.1186/s13059-019-1682-7",
language = "English (US)",
volume = "20",
journal = "Genome Biology",
issn = "1474-7596",
publisher = "BioMed Central",
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DegNorm : Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis. / Xiong, Bin; Yang, Yiben; Fineis, Frank R.; Wang, Jiping.

In: Genome biology, Vol. 20, No. 1, 75, 16.04.2019.

Research output: Contribution to journalReview article

TY - JOUR

T1 - DegNorm

T2 - Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis

AU - Xiong, Bin

AU - Yang, Yiben

AU - Fineis, Frank R.

AU - Wang, Jiping

PY - 2019/4/16

Y1 - 2019/4/16

N2 - RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.

AB - RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.

KW - Alternative splicing

KW - Degradation normalization

KW - Non-negative matrix factorization

KW - Normalization

KW - RNA degradation

KW - RNA-seq

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U2 - 10.1186/s13059-019-1682-7

DO - 10.1186/s13059-019-1682-7

M3 - Review article

VL - 20

JO - Genome Biology

JF - Genome Biology

SN - 1474-7596

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M1 - 75

ER -