ReviewQuantitative biology (Beijing, China)2025
Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.
Review in Quantitative biology (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- The transcriptional cortical response to daily torpor inLife science alliance · 2026Article
- Advances in sex-specific single-cell transcriptomic profiling in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Matricellular Proteins in Bladder Cancer: Context-Dependent Roles in Tumor Promotion and Suppression.International journal of molecular sciences · 2026Review
- Review
- Review
- Unveiling anti-atherosclerotic targets of Perilla frutescens through a multi-scale computational framework integrating network pharmacology, single-cell analysis, machine learning, and molecular dynamics.Bioresources and bioprocessing · 2026Article
- Comparative Analysis of Quorum Sensing-Dependent Transcriptomic Responses in Phytopathogenic Burkholderia spp.The plant pathology journal · 2026Article
- Enhanced endocrine-metabolic support and axonemal assembly in high-sperm-motility geese: insights from testicular cellular heterogeneity by scRNA-seq.Poultry science · 2026Article
- Transcriptomic Analysis Based on RNA-Seq Technology Reveals the Molecular Mechanisms of Sunflower (Genes · 2026Article
- Transcriptome Profiling of Leaves and Roots from Rooibos (Plants (Basel, Switzerland) · 2026Article
- Single-cell transcriptomic profiling of the chicken spleen reveals cell-type-specific immune responses to Salmonella infection.Poultry science · 2026Article
- Epithelial-Mesenchymal Transition Markers in Clear Cell Renal Cell Carcinoma: Expression Patterns and Prognostic Significance.Journal of personalized medicine · 2026Article
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Molecular Subtypes of Pancreatic Cancer: A Review of the Literature.Current issues in molecular biology · 2026Review
- Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology.Cancers · 2026Review
- Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine.International journal of molecular sciences · 2026Review
- Network-based exploration of 4-(phenylsulfonyl)morpholine molecules for metastatic triple-negative breast cancer suppression.PLoS computational biology · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Article
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 transcriptome, the complete set of RNA molecules within a cell, plays a critical role in regulating physiological processes. The advent of RNA sequencing (RNA-seq) facilitated by Next Generation Sequencing (NGS) technologies, has revolutionized transcriptome research, providing unique insights into gene expression dynamics. This powerful strategy can be applied at both bulk tissue and single-cell levels. Bulk RNA-seq provides a gene expression profile within a tissue sample. Conversely, single-cell RNA sequencing (scRNA-seq) offers resolution at the cellular level, allowing the uncovering of cellular heterogeneity, identification of rare cell types, and distinction between distinct cell populations. As computational tools, machine learning techniques, and NGS sequencing platforms continue to evolve, the field of transcriptome research is poised for significant advancements. Therefore, to fully harness this potential, a comprehensive understanding of bulk RNA-seq and scRNA-seq technologies, including their advantages, limitations, and computational considerations, is crucial. This review provides a systematic comparison of the computational processes involved in both RNA-seq and scRNA-seq, highlighting their fundamental principles, applications, strengths, and limitations, while outlining future directions in transcriptome research.
Indexed as
Identifiers
What Socratic 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 Socratic graph.