ReviewComputational and structural biotechnology journal2023
Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Gene-Based Clustering Identifies QSOX1 and IL1RAP as Biomarkers of Metabolic Dysfunction-Associated Steatotic Liver Disease.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis.Scientific reports · 2026Article
- An Integrative Small RNA-Degradome-Transcriptome Analysis Reveals Mechanisms of Heat-Induced Anther Indehiscence in Pepper.Biology · 2026Article
- SeqExpressionAnalyser: An R Package for Automated End-to-End RNA-Seq Analysis From Reads to Differential Expression.Bioinformatics and biology insights · 2026Article
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
- Transcriptomic Analysis of Tambaqui (Animals : an open access journal from MDPI · 2025Article
- Unveiling functional module associated with fungal disease stress in barley (Biochemistry and biophysics reports · 2025Article
- Integrated multi-omics and machine learning reveal an immunogenic cell death-related signature for prognostic stratification and therapeutic optimization in colorectal cancer.Frontiers in immunology · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- Article
- Estimating Transcriptome Diversity and Specialization inPlants (Basel, Switzerland) · 2024Article
- sRNAflow: A Tool for the Analysis of Small RNA-Seq Data.Non-coding RNA · 2024Article
- Evaluation of Near-Infrared Laser Effects on 143B Cells: A System Biology Approach.Journal of lasers in medical sciences · 2024Article
- A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging.PloS one · 2024Article
- Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness.Frontiers in genetics · 2024Article
- Article
- Comparative transcriptomics provides novel insights into the mechanisms of selenium accumulation and transportation in tea cultivars (Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.
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.