ArticleBMC bioinformatics2022
Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transcriptomic Landscape and Regulatory Pathways of Drought Response in Rice (International journal of molecular sciences · 2026Pooled it
- Biological Functional Class Enrichment Analysis with R, an Annotated Tutorial for Bench Scientists.Methods and protocols · 2026Article
- Exposure-inducible genes may contribute to missingness in RNAseq-based gene expression analyses.Scientific reports · 2025Article
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- Article
- Quantitative mapping of pseudouridines in bacteria RNA.bioRxiv : the preprint server for biology · 2024Article
- Next-generation data filtering in the genomics era.Nature reviews. Genetics · 2024Review
- Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results.Nucleic acids research · 2024Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide.Frontiers in zoology · 2024Review
- Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.International journal of molecular sciences · 2024Review
- Article
- Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data.Frontiers in genetics · 2023Article
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6 authors.
Funding
Abstract
backgroundRNA-seq has become a standard technology to quantify mRNA. The measured values usually vary by several orders of magnitude, and while the detection of differences at high values is statistically well grounded, the significance of the differences for rare mRNAs can be weakened by the presence of biological and technical noise.
resultsWe have developed a method for cleaning RNA-seq data, which improves the detection of differentially expressed genes and specifically genes with low to moderate transcription. Using a data modeling approach, parameters of randomly distributed mRNA counts are identified and reads, most probably originating from technical noise, are removed. We demonstrate that the removal of this random component leads to the significant increase in the number of detected differentially expressed genes, more significant pvalues and no bias towards low-count genes.
conclusionApplication of RNAdeNoise to our RNA-seq data on polysome profiling and several published RNA-seq datasets reveals its suitability for different organisms and sequencing technologies such as Illumina and BGI, shows improved detection of differentially expressed genes, and excludes the subjective setting of thresholds for minimal RNA counts. The program, RNA-seq data, resulted gene lists and examples of use are in the supplementary data and at https://github.com/Deyneko/RNAdeNoise .
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