ArticleBMC bioinformatics2011
Bias detection and correction in RNA-Sequencing data.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed.
- A framework for identifying transcript orthologs: the evolution of sex bias in alternative transcript structure in Drosophila.bioRxiv : the preprint server for biology · 2026Article
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- Machine learning-optimized targeted detection of alternative splicing.Nucleic acids research · 2025Article
- Reliable RNA-seq analysis from FFPE specimens as a means to accelerate cancer-related health disparities research.PloS one · 2025Article
- Machine learning-optimized targeted detection of alternative splicing.bioRxiv : the preprint server for biology · 2024Article
- Cortexa: a comprehensive resource for studying gene expression and alternative splicing in the murine brain.BMC bioinformatics · 2024Article
- Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing.International journal of molecular sciences · 2024Article
- RNA-sequencing Reveals Altered Gene Expression in the Ventromedial Hypothalamus Following Predator Odor Exposure.F1000Research · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- Artifacts and biases of the reverse transcription reaction in RNA sequencing.RNA (New York, N.Y.) · 2023Review
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- How can early life adversity still exert an effect decades later? A question of timing, tissues and mechanisms.Frontiers in immunology · 2023Review
- Library adaptors with integrated reference controls improve the accuracy and reliability of nanopore sequencing.Nature communications · 2022Article
- Comparative Analysis of Single-Cell RNA Sequencing Platforms and Methods.Journal of biomolecular techniques : JBT · 2021Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- Estimation of peptide elongation times from ribosome profiling spectra.Nucleic acids research · 2021Article
- Correcting signal biases and detecting regulatory elements in STARR-seq data.Genome research · 2021Article
- Article
- AIDE: annotation-assisted isoform discovery with high precision.Genome research · 2019Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundHigh throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.
resultsIn this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively.
conclusionsOur method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.