Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
Open accessAbstract
Motivation: RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored. Results: Here, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with quantitative reverse transcription–polymerase chain reaction data. Availability: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html. Contact: liw@cs.ucr.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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At the transcript level, current methods correct for biases such as fragment length distribution from size selection, positional bias from RNA degradation and mRNA selection, and read-start sequence bias from random hexamer primer binding.
“At the transcript level, sample-specific biases that current methods correct for include the fragment length distribution induced by size selection, positional bias along the transcript due to RNA degradation and mRNA selection techniques, and sequence-based bias in read start positions arising from the differential binding efficiency of random hexamer primers2,12-16”
From Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation (2016) · cites this paper as [16]
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- Authors
- Li, W., & Jiang, T.
- Affiliations
- University of California, Riverside (US)
- Corresponding author
- Li, Wei
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-10-11
- Volume
- 28
- Issue
- 22
- Pages
- 2914-2921
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3496342
- OpenAlex
- W2136132915
- Cited by
- 97
- References
- 53
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 13 · 2020: 4 · 2019: 3 · 2018: 4 · 2017: 14 · 2016: 15 · 2015: 9 · 2014: 11 · 2013: 9 · 2012: 1
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (R01AI078885)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- transcriptome assembly, position bias, sequencing bias, CEM
- MeSH terms
- Algorithms, Humans, Transcription, Genetic, Sequence Analysis, RNA, Protein Isoforms, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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