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Gene Expression

Genetics and Genomics · NLM MeSH · updated 2026-10-29 · version 1

Citations verified against PubMedNot yet clinician-reviewed9 referencesApproximately 3 min read

The phenotypic manifestation of a gene or genes by the processes of genetic transcription and genetic translation.

Table of Contents

1. Definition2. Research landscape3. Reading the evidence4. Open-access studies5. Key reviews6. References7. About this article

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 (535 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

The phenotypic manifestation of a gene or genes by the processes of genetic transcription and genetic translation.

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: Expression, Gene; Expressions, Gene; Gene Expressions.

Where it fits in the MeSH classification

Broader topics
Genetic Phenomena
Narrower topics
Protein Biosynthesis; Transcription, Genetic
See also
Phenotype; Gene Expression Profiling

Related concepts

  • Genetic Transcription
  • Protein Biosynthesis

Research landscape

PubMed indexes 543,202 records tagged with the MeSH heading “Gene Expression” (counts retrieved 2026-10-29). 93,482 of them (17.2%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 166,474 records.

Reviews account for 41,816 records (7.7%) and meta-analyses for 1,267. Randomized controlled trials number 1,257 (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.

PubMed records by publication periodthrough 1999: 154,901 records; 2000-2009: 128,345 records; 2010-2019: 166,474 records; 2020 onward: 93,482 records154,901through 1999128,3452000-2009166,4742010-201993,4822020 onward
Records in PubMed indexed with this heading, by publication period. Real counts from NCBI E-utilities; the final period covers only part of the current year. Volume reflects how much has been published, not how strong the evidence is.
Show the data
PeriodRecords
through 1999154,901
2000-2009128,345
2010-2019166,474
2020 onward93,482
Share of records by publication typeReviews: 7.7 percent; Meta-analyses: 0.2 percent; Randomized controlled trials: 0.2 percentReviews7.7% (41,816)Meta-analyses0.2% (1,267)Randomized controlled trials0.2% (1,257)
Share of all PubMed records on this heading that carry each publication type. Types overlap with other records, so the shares do not sum to 100%.
Publication-type chart data
Publication types among 543,202 PubMed records
TypeRecordsShare
Reviews41,8167.7%
Meta-analyses1,2670.2%
Randomized controlled trials1,2570.2%

Method: PubMed query "Gene Expression"[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 comparison — selected abstracts, not a systematic review
Study / authorsYearSource label / designEurope PMC citationsLicence / source
Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet
Hoffman GE, Lee D, Bendl J, Prashant NM, Hong A, Casey C, et al.
2026Research article
Design not recorded in structured metadata
28cc by
PMID 42778542 · DOI · PMC13601538
Structural Characterization of Native RNA Polymerase II Transcription Complexes and Nucleosomes in Drosophila melanogaster
Venette-Smith NL, Vishwakarma RK, Venkatakrishnan V, Dollinger R, Schultz J, Babitzke P, et al.
2026Research article
Design not recorded in structured metadata
2cc by
PMID 42711332 · DOI · PMC13554098
Ribosome stalling position, spacing, and A-site occupancy impact translation and cotranslational mRNA decay in plants
van der Horst S, Gage JL, Bailey-Serres J
2026Research article
Design not recorded in structured metadata
1cc by
PMID 42709815 · DOI · PMC13578316

Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet [1]

Research article Hoffman GE, Lee D, Bendl J, Prashant NM, Hong A, Casey C, et al. · · 2026 · cited 28 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells.
Closing excerpt
4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.
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 (150 words)

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet ( DiseaseNeurogenomics.github.io/dreamlet ) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42778542 · DOI · PMC13601538

Structural Characterization of Native RNA Polymerase II Transcription Complexes and Nucleosomes in Drosophila melanogaster [2]

Research article Venette-Smith NL, Vishwakarma RK, Venkatakrishnan V, Dollinger R, Schultz J, Babitzke P, et al. · · 2026 · cited 2 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Structural studies of eukaryotic RNA polymerase II (Pol II) transcription often rely on in vitro assembly, which may not fully represent native conditions.
Closing excerpt
This study provides a framework for investigations of transcription in cells, paving the way for future studies of transient and minor complexes.
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 (141 words)

Structural studies of eukaryotic RNA polymerase II (Pol II) transcription often rely on in vitro assembly, which may not fully represent native conditions. To investigate Pol II transcription in metazoan cells, we developed a method to isolate native transcription complexes from Drosophila melanogaster embryos using FLAG-tag affinity purification and Micrococcal Nuclease treatment. Cryo-EM and proteomics studies revealed diverse transcription complexes and nucleosomes, including a metazoan Rpb4/Rpb7 stalk-less Pol II elongation complex and a hexameric nucleosome lacking an H2A/H2B dimer. Notably, nucleosome is found only downstream of the nucleosome elongation complex, underscoring it as a major energy barrier and a time-consuming step during Pol II progression through chromatin. Proteomics identified co-purified factors involved in transcription initiation, elongation, and RNA modification. This study provides a framework for investigations of transcription in cells, paving the way for future studies of transient and minor complexes.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42711332 · DOI · PMC13554098

Ribosome stalling position, spacing, and A-site occupancy impact translation and cotranslational mRNA decay in plants [3]

Research article van der Horst S, Gage JL, Bailey-Serres J · · 2026 · cited 1 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Ribosomes can pause during mRNA translation, but what causes pausing, how pauses affect protein production, and whether they trigger cotranslational mRNA decay are poorly understood in plants.
Closing excerpt
In sum, the stalling of ribosomes at specific codons, coupled with ribosome A-site occupancy and disome spacing, modulates protein production and cotranslational mRNA decay in plants.
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 (244 words)

Ribosomes can pause during mRNA translation, but what causes pausing, how pauses affect protein production, and whether they trigger cotranslational mRNA decay are poorly understood in plants. Here, we investigate the causes and consequences of ribosome pausing in Arabidopsis and maize. This is accomplished by sizing, mapping, and quantifying footprints of individual ribosomes (monosomes) and closely spaced ribosome pairs (disomes) at single-codon resolution on open reading frames (ORFs). Ribosome footprinting was combined with 5'P-degradome-seq to examine the coincidence of pausing with cotranslational decay under control conditions and brief hypoxia in Arabidopsis. The data resolve two monosome conformations and three disome configurations. These include monosomes with a vacant or occupied A-site and disomes that have collided or are separated by one or two codons. Pausing is prevalent at initiation, termination, and di-Proline codons. Di-Proline pauses do not trigger cotranslational decay but appear important in cotranslational protein processing. Brief hypoxia induces stalling of A-site vacant ribosomes at Aspartate codons, often coinciding with 5'P peaks, indicating that rate-limiting decoding can trigger cotranslational mRNA decay. Notably, actively transcribed and translated hypoxia-response mRNAs accumulate 1- to 2-codon-separated disomes and are actively degraded. Comparative analysis of footprints in the two species reveals ribosome conformations and codon-specific pausing can be conserved or lineage-specific, as exemplified by pausing at di-Prolines and on Conserved Peptide upstream ORFs. In sum, the stalling of ribosomes at specific codons, coupled with ribosome A-site occupancy and disome spacing, modulates protein production and cotranslational mRNA decay in plants.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42709815 · DOI · PMC13578316

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 The landscape of long noncoding RNAs in the human transcriptome [4]
    Iyer MK, Niknafs YS, Malik R, Singhal U, Sahu A, Hosono Y, et al. · Nat Genet · 2015 · cited 2,218 times (Europe PMC)
  • Research article Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas [5]
    Kool M, Korshunov A, Remke M, Jones DT, Schlanstein M, Northcott PA, et al. · Acta Neuropathol · 2012 · cited 787 times (Europe PMC)
  • Research article Epigenetic Signatures of Cigarette Smoking [6]
    Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al. · Circ Cardiovasc Genet · 2016 · cited 783 times (Europe PMC)
  • Research article The transcriptional landscape of age in human peripheral blood [7]
    Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al. · Nat Commun · 2015 · cited 637 times (Europe PMC)
  • Research article H3K4me3 breadth is linked to cell identity and transcriptional consistency [8]
    Benayoun BA, Pollina EA, Ucar D, Mahmoudi S, Karra K, Wong ED, et al. · Cell · 2014 · cited 451 times (Europe PMC)
  • Research article Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models [9]
    Wan YW, Al-Ouran R, Mangleburg CG, Perumal TM, Lee TV, Allison K, et al. · Cell Rep · 2020 · cited 285 times (Europe PMC)

References

  1. Hoffman GE, Lee D, Bendl J, Prashant NM, Hong A, Casey C, et al.. Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet. . 2026. PMID 42778542 · DOI · PMC13601538
  2. Venette-Smith NL, Vishwakarma RK, Venkatakrishnan V, Dollinger R, Schultz J, Babitzke P, et al.. Structural Characterization of Native RNA Polymerase II Transcription Complexes and Nucleosomes in Drosophila melanogaster. . 2026. PMID 42711332 · DOI · PMC13554098
  3. van der Horst S, Gage JL, Bailey-Serres J. Ribosome stalling position, spacing, and A-site occupancy impact translation and cotranslational mRNA decay in plants. . 2026. PMID 42709815 · DOI · PMC13578316
  4. Iyer MK, Niknafs YS, Malik R, Singhal U, Sahu A, Hosono Y, et al.. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet. 2015. PMID 25599403 · DOI · PMC4417758
  5. Kool M, Korshunov A, Remke M, Jones DT, Schlanstein M, Northcott PA, et al.. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathol. 2012. PMID 22358457 · DOI · PMC3306778
  6. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al.. Epigenetic Signatures of Cigarette Smoking. Circ Cardiovasc Genet. 2016. PMID 27651444 · DOI · PMC5267325
  7. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al.. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015. PMID 26490707 · DOI · PMC4639797
  8. Benayoun BA, Pollina EA, Ucar D, Mahmoudi S, Karra K, Wong ED, et al.. H3K4me3 breadth is linked to cell identity and transcriptional consistency. Cell. 2014. PMID 25083876 · DOI · PMC4137894
  9. Wan YW, Al-Ouran R, Mangleburg CG, Perumal TM, Lee TV, Allison K, et al.. Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models. Cell Rep. 2020. PMID 32668255 · DOI · PMC7428328

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.

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Related research topics

  • Gene Expression ProfilingGenetics and Genomics
  • Gene Expression RegulationGenetics and Genomics
  • Reverse GeneticsGenetics and Genomics
  • Gene FlowGenetics and Genomics
  • GenesGenetics and Genomics
  • Gene AmplificationGenetics and Genomics

All 95 articles in Genetics and Genomics →

At a glance

543,202
PubMed records on this heading
17%
published since 2020
41,816
review articles
1,257
randomized trials
9
verified references below

Counts come from PubMed on 2026-10-29; they show how much has been written, not how strong the evidence is.

Related topics See all →

  • Gene Expression ProfilingGenetics and Genomics
  • Gene Expression RegulationGenetics and Genomics
  • Reverse GeneticsGenetics and Genomics
  • Gene FlowGenetics and Genomics
  • GenesGenetics and Genomics

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