ReviewCellular and molecular life sciences : CMLS2010
RNA-seq: from technology to biology.
Review in Cellular and molecular life sciences : CMLS, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 245 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
245 citing papers in PubMed.
- Transcriptomic Analysis Reveals Molecular Mechanisms of Fleeing, Adhesion, and Thanatosis Patterns in Sea CucumberBiology · 2026Article
- Benchmarking genome choice in functional genomics analyses.Nature communications · 2026Article
- Article
- Persistent viral infection in the Drosophila fat body is associated with immune activation at the single cell level.BMC genomics · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- Elucidation of the XX/XY Sex Determination System and Development of a Sex-Linked Molecular Marker in the Freshwater SnailAnimals : an open access journal from MDPI · 2026Article
- scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling.Interdisciplinary sciences, computational life sciences · 2026Article
- Quantifying transcript complexity via the condition number of gene-specific random matrix.Briefings in bioinformatics · 2026Article
- Developmental and Reproductive Impacts ofInsects · 2026Article
- Interactive online tool to identify genes associated with longevity in a panel of long-lived genetic mutants inmicroPublication biology · 2026Article
- Differentially Expressed Gene Annotator (DEGAn): automated annotation and analysis of DEGs datasets with OS and PFS data.Bioinformatics advances · 2026Article
- Comprehensive analysis of malignant subtypes of lung adenocarcinoma based on multi-omics landscape and functional validation of prognostic biomarker BAIAP2L2.Scientific reports · 2025Article
- Integrative Transcriptome Analysis Across Follicles Highlights Key Regulatory Pathways in Low and High-Egg-Laying Hens.Animals : an open access journal from MDPI · 2025Article
- STALARD: Selective Target Amplification for Low-Abundance RNA Detection.Plant methods · 2025Article
- Senataxin regulates cisplatin resistance through an R-loop-mediated mechanism in HPV-associated head and neck cancer.iScience · 2025Article
- Article
- Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer.Artificial intelligence in medicine · 2025Article
- Hepatic antioxidant capacity, immune response, and glycolysis of Tibetan sheep in response to dietary soluble protein levels.Protoplasma · 2025Article
- Molecular and physiological basis of heterosis in hybrid rice performance.Molecular breeding : new strategies in plant improvement · 2025Review
- DNA replication timing reveals genome-wide features of transcription and fragility.Nature communications · 2025Article
185 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
2 authors.
Funding
Abstract
Next-generation sequencing technologies are now being exploited not only to analyse static genomes, but also dynamic transcriptomes in an approach termed RNA-seq. Although these powerful and rapidly evolving technologies have only been available for a couple of years, they are already making substantial contributions to our understanding of genome expression and regulation. Here, we briefly describe technical issues accompanying RNA-seq data generation and analysis, highlighting differences to array-based approaches. We then review recent biological insight gained from applying RNA-seq and related approaches to deeply sample transcriptomes in different cell types or physiological conditions. These approaches are providing fascinating information about transcriptional and post-transcriptional gene regulation, and they are also giving unique insight into the richness of transcript structures and processing on a global scale and at unprecedented resolution.
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.