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Transcript length bias in RNA-seq data confounds systems biology
Open accessAbstract
BACKGROUND: Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. RESULTS: We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. CONCLUSION: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. REVIEWERS: This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit).
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![Figure 1 Differential expression as a function of transcript length. The data is binned according to transcript length and the percentage of transcripts called differentially expressed using a statistical cut-off is plotted (points). A linear regression is also plotted (lines). a – e use all the data from RNA-seq and the microarrays from studies [4-6] respectively. f and g plot 33% of genes with highest expression levels (blue crosses) and 33% of genes with low expression (red triangles) taken from the microarray data for genes which appear on both platforms in [6]. The regression gives a significant trend for the percent of differential expression with transcript length for a, c, d and f and the lowly expressed genes in g. Note that this figure illustrates common data features between disparate experiments and is not a comparison between platforms, methods or experiments.](/paper-figures/9746808909cd4073b297d14d8352ee14/transcript-length-bias-in-rna-seq-data-confounds-systems-biology-figure-1.jpg)
![Figure 2 Mean-variance relationship. Here we show the sample variance across lanes in the liver sample from the Marioni et al[6] data plotted as a function of the mean for each gene (a). Next we have the same data where the tag counts for each gene are divided by the length of the gene (b). The red line fits a linear relationship between the mean and variance for the one third of shortest genes while the blue line is the linear fit to the longest genes. In plot a the fits are very close to the line of equality between mean and variance (black line) which is what would be expected from a Poisson process. In plot b the short genes have higher variance for a given expression level than long genes.](/paper-figures/099f5ac8e3d14b989962c2f0d6cd8441/transcript-length-bias-in-rna-seq-data-confounds-systems-biology-figure-2.jpg)
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“It should be noted that these estimates apply to the average gene expression level, but as dynamic ranges in RNA-seq data are large, the probability that highly expressed genes will be detected as differentially expressed is greater than that for low-count genes [192].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [192]
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BackgroundMethodInfluentialTranscriptomic Data Normalization
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GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks
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“It should be noted that prior to applying our z-score normalization, we normalized each sample by its Library size to have a total read count of 10 million (“Methods”), similar to the library-size scaling step of traditional techniques like TPM/FPKM normalization [27, 28].”
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“In RNA-Seq data, transcript length biases may arise due to random RNA fragmentation and the inherent sampling nature (Oshlack and Wakefield, 2009).”
Optimization Techniques For Next-Generation Sequencing Data Analysis
Caciula, A. (2022) · doi:10.57709/5825568 · Cited by 2
Applications of Genomics in Weed Science
Jugulam, M., Gaines, T. A., Tranel, P. J., Fleming, M. B., Patterson, E. L., Küpper, A., Ravet, K., Giacomini, D. A., González, S., & Beffa, R. (2017) · CRC Press eBooks · doi:10.1201/9781315121031-11 · Cited by 2
Transcriptomics Using Axolotls
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Controlling the false-discovery rate by procedures adapted to the length bias of RNA-Seq
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Helicos Single‐Molecule Sequencing for Accurate Tag‐Based RNA Quantitation
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Flimma: a federated and privacy-preserving tool for differential gene expression analysisPreprint
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“count-based measurement, and their results subject to platform-specific biases [1,2].”
BackgroundHuerlimann, R., Cowley, J. A., Wade, N. M., Wang, Y., Kasinadhuni, N., Chan, C. K., Jabbari, J. S., Siemering, K. R., Gordon, L., Tinning, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.11.468259 · Cited by 2
3’ RNA sequencing does not increase power or reduce costs for gene expression analysisPreprint
Crow, T. M., Gill, J. A., Whitehead, A., & Runcie, D. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.13.488225 · Cited by 2
“The main advantage of 3’ libraries is that they are not subject to the length bias of WT, which can impact downstream analysis such as gene set enrichment [17, 1, 18, 19].”
BackgroundTo make a short story long: simultaneous short and long RNA profiling on Nanopore devicesPreprint
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An Integrated Approach to Plant Biology via Multi-Analogous Methods
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“This is a common problem with RNA-seq technologies [20].”
BackgroundCOMPARATIVE STUDIES OF DIFFERENTIAL GENE CALLING METHODS FOR RNA-SEQ DATA
Zheng, X. (2012) · Lincoln (University of Nebraska) · Cited by 1
Detecting differentially expressed genes by smoothing effect of gene length on variance estimation
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Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease
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A Hierarchical Bayesian Model for RNA-Seq Data
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Statistical Analyses of Next Generation Sequencing Data: An Overview
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RNA-Seq Data Analysis in Prokaryotes: A Review for Non-experts
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Differential expression and feature selection in the analysis of multiple omics studies
Ma, T. (2018) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 1
Circular RNAs in Human Health and Disease
Grouse, L., Curry‐Hyde, A., Chen, B. J., & Janitz, M. (2019) · Springer eBooks · doi:10.1007/978-981-13-8958-0_5 · Cited by 1
RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer
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An Overview of Gene Regulation
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Analysis of RNA Transcripts by High‐Throughput RNA Sequencing
Ribeca, P., Lacroix, V., Sammeth, M., & Guigó, R. (2012) · doi:10.1002/9783527636778.ch50 · Cited by 1
Metadata
- Authors
- Oshlack, A., & Wakefield, M. J.
- Affiliations
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Oshlack, Alicia
- Venue
- Biology Direct
- Published
- 2009
- Volume
- 4
- Issue
- 1
- Pages
- 14
- ISSN
- 1745-6150 (ISSN-L)
- PMC
- PMC2678084
- OpenAlex
- W2114504176
- Cited by
- 497
- References
- 12
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 16 · 2023: 14 · 2022: 24 · 2021: 35 · 2020: 12 · 2019: 29 · 2018: 30 · 2017: 36 · 2016: 33 · 2015: 39 · 2014: 39 · 2013: 37 · 2012: 44
- FWCI
- 18.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Biology Direct (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- RNA-seq, differentially expressed genes, gene expression profiling, pathway analysis, systems biology
- MeSH terms
- Base Sequence, Humans, RNA, Messenger, Transcription, Genetic, Bias, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Systems Biology
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