Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (474 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
A category of nucleic acid sequences that function as units of heredity and which code for the basic instructions for the development, reproduction, and maintenance of organisms.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Gene; Genetic Materials; Genetic Material; Material, Genetic; Materials, Genetic; Cistron; Cistrons.
Where it fits in the MeSH classification
- Broader topics
- Genome Components
- Narrower topics
- Alleles; Gene Components; Genes, cdc; Genes, Chloroplast; Genes, Developmental; Genes, Dominant; Genes, Duplicate; Genes, Essential; Genes, Helminth; Genes, Immediate-Early; Genes, Immunoglobulin; Genes, Insect; Genes, Lethal; Genes, MDR
- See also
- Base Sequence; Genomic Library; Genome
Research landscape
PubMed indexes 863,474 records tagged with the MeSH heading “Genes” (counts retrieved 2026-10-29). 88,154 of them (10.2%) were published from 2020 onward, and the busiest period so far is through 1999 with 280,379 records.
Reviews account for 67,746 records (7.8%) and meta-analyses for 4,101. Randomized controlled trials number 1,537 (0.2%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 280,379 |
| 2000-2009 | 252,266 |
| 2010-2019 | 242,675 |
| 2020 onward | 88,154 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 67,746 | 7.8% |
| Meta-analyses | 4,101 | 0.5% |
| Randomized controlled trials | 1,537 | 0.2% |
Method: PubMed query "Genes"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in genetics and genomics
Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Mapping enhancer-gene regulatory interactions from single-cell data | 2026 | Research article Design not recorded in structured metadata | 17 | cc by PMID 42547575 · DOI · PMC13447104 |
| A tumour-derived organoid biobank maps cancer gene dependencies | 2026 | Research article Design not recorded in structured metadata | 6 | cc by PMID 42557320 · DOI · PMC13581617 |
| Cumulative transcription factor binding and p300-mediated histone acetylation drive enhancer activation frequency | 2026 | Research article Design not recorded in structured metadata | 3 | cc by PMID 42538372 · DOI · PMC13447113 |
Mapping enhancer-gene regulatory interactions from single-cell data [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics.
- Closing excerpt
- The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of human cell types.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (158 words)
Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here we introduce a family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) or multiomic RNA and ATAC-seq data, and are trained on a CRISPR perturbation dataset including >10,000 evaluated element-gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped expression quantitative trait loci and genome-wide association study variant-gene associations and demonstrate state-of-the-art performance at prediction tasks across several cell types and categories of perturbations. We apply scE2G to build maps of enhancer-gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte count. The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of human cell types.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42547575 · DOI · PMC13447104
A tumour-derived organoid biobank maps cancer gene dependencies [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Cancer cell lines remain foundational for research and drug discovery, yet they incompletely capture tumour diversity, lack linked patient context, and have undergone adaptation to culture.
- Closing excerpt
- This open, publicly available resource provides a systematic map of gene dependencies in patient-derived organoids, expanding the model diversity and mechanistic insight needed to advance precision oncology.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (168 words)
Cancer cell lines remain foundational for research and drug discovery, yet they incompletely capture tumour diversity, lack linked patient context, and have undergone adaptation to culture. Tumour organoids are three-dimensional cultures derived from patient tissue that offer a powerful complement to cell lines1. Here we derived and characterized 256 clinically annotated tumour organoids directly from colorectal, oesophageal, ovarian, pancreatic and gastric cancers as renewable, genetically stable models. Extensive characterization of each model and matched patient tumour samples included whole-genome and transcriptome sequencing, and genome-wide CRISPR-Cas9 screens across 162 organoids mapped gene dependencies. Integrative analyses revealed genomic and clinical markers of dependency across common and rare subtypes, identified organoid-specific essential genes, and revealed targetable vulnerabilities following tumour evolution in paired pre- and post-treatment samples. In colorectal cancer, functional and pharmacological interrogation of the EGFR-RAS-MAPK axis uncovered differential effects of KRAS variant alleles. This open, publicly available resource provides a systematic map of gene dependencies in patient-derived organoids, expanding the model diversity and mechanistic insight needed to advance precision oncology.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42557320 · DOI · PMC13581617
Cumulative transcription factor binding and p300-mediated histone acetylation drive enhancer activation frequency [3]
Abstract reading summary — source excerpts
- Opening excerpt
- In eukaryotes, transcription factors (TFs) must continuously compete with nucleosomes to access their binding sites, leading to cell-to-cell variability in chromatin accessibility at regulatory regions.
- Closing excerpt
- Our data support a model in which enhancer activation frequency depends on the cumulative function of multiple TFs and is modulated by p300 activity.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (148 words)
In eukaryotes, transcription factors (TFs) must continuously compete with nucleosomes to access their binding sites, leading to cell-to-cell variability in chromatin accessibility at regulatory regions. Although critical to understand enhancer function in transcription, the mechanisms that define how frequently an enhancer is active in a cell population remain unclear. Here we used single-molecule footprinting to quantify the frequency at which chromatin is accessible at enhancers in response to TF perturbations and changes in their chromatin environment. We find that, individually, most TFs open chromatin in a small fraction of cells, and that cumulative TF binding controls enhancer activation frequency. Moreover, testing the functionality of hundreds of enhancers when inserted at an ectopic genomic location indicates that p300 activity is required for their full activation. Our data support a model in which enhancer activation frequency depends on the cumulative function of multiple TFs and is modulated by p300 activity.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42538372 · DOI · PMC13447113
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection [4]
- Research article Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis [5]
- Research article Antibiotics and antibiotic resistance genes in global lakes: A review and meta-analysis [6]
- Research article Germline and Somatic Tumor Testing in Epithelial Ovarian Cancer: ASCO Guideline [7]
- Research article Genetic predisposition to neuroblastoma mediated by a LMO1 super-enhancer polymorphism [8]
- Research article Effect of BRCA germline mutations on breast cancer prognosis: A systematic review and meta-analysis [9]
References
- Sheth MU, Qiu WL, Ma XR, Gschwind AR, Jagoda E, Tan AS, et al.. Mapping enhancer-gene regulatory interactions from single-cell data. . 2026. PMID 42547575 · DOI · PMC13447104
- Herranz-Ors C, Bhosle SG, Beck AE, Gilbert JGR, Picco G, Espejo Valle-Inclan J, et al.. A tumour-derived organoid biobank maps cancer gene dependencies. . 2026. PMID 42557320 · DOI · PMC13581617
- Baderna V, Barzaghi G, Kleinendorst R, Chatsirisupachai K, Moniot-Perron L, Doyle C, et al.. Cumulative transcription factor binding and p300-mediated histone acetylation drive enhancer activation frequency. . 2026. PMID 42538372 · DOI · PMC13447113
- Pardiñas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al.. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018. PMID 29483656 · DOI · PMC5918692
- Ryman DC, Acosta-Baena N, Aisen PS, Bird T, Danek A, Fox NC, et al.. Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis. Neurology. 2014. PMID 24928124 · DOI · PMC4117367
- Yang Y, Song W, Lin H, Wang W, Du L, Xing W. Antibiotics and antibiotic resistance genes in global lakes: A review and meta-analysis. Environ Int. 2018. PMID 29653401 · DOI
- Konstantinopoulos PA, Norquist B, Lacchetti C, Armstrong D, Grisham RN, Goodfellow PJ, et al.. Germline and Somatic Tumor Testing in Epithelial Ovarian Cancer: ASCO Guideline. J Clin Oncol. 2020. PMID 31986064 · DOI · PMC8842911
- Oldridge DA, Wood AC, Weichert-Leahey N, Crimmins I, Sussman R, Winter C, et al.. Genetic predisposition to neuroblastoma mediated by a LMO1 super-enhancer polymorphism. Nature. 2015. PMID 26560027 · DOI · PMC4775078
- Baretta Z, Mocellin S, Goldin E, Olopade OI, Huo D. Effect of BRCA germline mutations on breast cancer prognosis: A systematic review and meta-analysis. Medicine (Baltimore). 2016. PMID 27749552 · DOI · PMC5059054
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.