ReviewPloS one2017
RNA-Seq differential expression analysis: An extended review and a software tool.
Review in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 297 papers, 4 of them syntheses that pooled it.
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
297 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Identification of a forkhead box protein transcriptional network induced in human neutrophils in response to inflammatory stimuli.Frontiers in immunology · 2023Pooled it
- Uncovering Oncogenic Mechanisms of Tumor Suppressor Genes in Breast Cancer Multi-Omics Data.International journal of molecular sciences · 2022Pooled it
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- Vitamin D-regulated Gene Expression Profiles: Species-specificity and Cell-specific Effects on Metabolism and Immunity.Endocrinology · 2021Pooled it
- Evolutionary Reorganization of Transcriptomic Architecture Across a UVB Tolerance Gradient in Fish.Ecology and evolution · 2026Article
- Noncoding RNA Transcripts Based Molecular Phenotyping of MASLD and MASH Patients.Liver international communications · 2026Article
- A novel causality-based method for identifying drivers of breast cancer progression.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Inducible CRISPR-Cas9 screening platform to interrogate non-proliferative cellular states.Nature protocols · 2026Review
- Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis.Scientific reports · 2026Article
- Representation Methods of Transcriptomics with Applications in Neuroimmune Biology.bioRxiv : the preprint server for biology · 2026Article
- Mitochondria in situ releasing corosolic acid enhanced antitumor effects via activating mitophagy in castration-resistant prostate cancer.Journal of translational medicine · 2026Article
- Article
- Identifying Relevant Covariates in RNA-seq Analysis by Pseudo-Variable Augmentation.Journal of agricultural, biological, and environmental statistics · 2026Article
- Navigating the molecular landscape: integrated multiomics liquid biopsy for biomarker discovery in early detection and monitoring of colorectal cancer.Frontiers in molecular biosciences · 2026Review
- Microbial Omics.Progress in molecular and subcellular biology · 2026Review
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
- Identification of Clinically Relevant Genes Using Machine Learning Techniques.Methods in molecular biology (Clifton, N.J.) · 2026Article
- From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods.Human genomics · 2025Review
- Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes.Molecular oncology · 2025Article
237 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at https://github.com/costasilvati/consexpression. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.
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.