ArticleBMC bioinformatics2019
A statistical normalization method and differential expression analysis for RNA-seq data between different species.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Decoding the Pine Wood Nematode's survival mystery: Gene family expansion drives adaptation revealed by dual-omics.PLoS pathogens · 2026Article
- Comprehensive blood glucose level prediction from HbAScientific reports · 2026Article
- Remodeling of three-dimensional genome architecture in cardiac development and aging.BMC genomics · 2026Article
- ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian Brain.bioRxiv : the preprint server for biology · 2025Article
- PB-DiffHiC: a statistical framework for detecting differential chromatin interactions from high resolution pseudo-bulk Hi-C data.BMC genomics · 2025Article
- Article
- Convergent Molecular Evolution Associated With Repeated Transitions to Gregarious Larval Behavior in Heliconiini.Molecular biology and evolution · 2025Article
- Article
- Quantifying Transcriptome Turnover on Phylogenies by Modeling Gene Expression as a Binary Trait.Molecular biology and evolution · 2025Article
- Integrated transcriptomic analysis reveals evolutionary and developmental characteristics of tendon ossification in teleost.BMC biology · 2024Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- Decoding bee cleptoparasitism through comparative transcriptomics of Coelioxoides waltheriae and its host Tetrapedia diversipes.Scientific reports · 2024Article
- Article
- A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data.Molecular biology and evolution · 2023Article
- Comparative transcriptome analysis reveals deep molecular landscapes in stony coralFrontiers in genetics · 2023Article
- Positive selection and heat-response transcriptomes reveal adaptive features of the Brassicaceae desert model, Anastatica hierochuntica.The New phytologist · 2022Article
- Full-Length Transcriptome Maps of Reef-Building Coral Illuminate the Molecular Basis of Calcification, Symbiosis, and Circadian Genes.International journal of molecular sciences · 2022Article
- Article
- RNA-Seq Reveals Differentially Expressed Genes Associated with High Fiber Quality in Abaca (Genes · 2022Article
- Cross-Species Analysis Reveals Co-Expressed Genes Regulating Antler Development in Cervidae.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundHigh-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects.
resultsIn this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors.
conclusionsSimulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN", which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.