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 (506 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 multistage process that includes the determination of a sequence (protein, carbohydrate, etc.), its fragmentation and analysis, and the interpretation of the resulting sequence information.
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: Analysis, Sequence; Analyses, Sequence; Sequence Analyses; Sequence Determinations; Determination, Sequence; Determinations, Sequence; Sequence Determination.
Where it fits in the MeSH classification
- Broader topics
- Genetic Techniques
- Narrower topics
- High-Throughput Nucleotide Sequencing; Molecular Sequence Annotation; Nanopore Sequencing; Oligonucleotide Array Sequence Analysis; Position-Specific Scoring Matrices; Sequence Analysis, DNA; Sequence Analysis, Protein; Sequence Analysis, RNA
Research landscape
PubMed indexes 514,798 records tagged with the MeSH heading “Sequence Analysis” (counts retrieved 2026-10-29). 122,315 of them (23.8%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 225,552 records.
Reviews account for 30,941 records (6.0%) and meta-analyses for 4,502. Randomized controlled trials number 1,090 (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 | 34,346 |
| 2000-2009 | 132,585 |
| 2010-2019 | 225,552 |
| 2020 onward | 122,315 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 30,941 | 6.0% |
| Meta-analyses | 4,502 | 0.9% |
| Randomized controlled trials | 1,090 | 0.2% |
Method: PubMed query "Sequence Analysis"[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 |
|---|---|---|---|---|
| Scalable high resolution ancestry deconvolution for genomic data | 2026 | Research article Design not recorded in structured metadata | 31 | cc by PMID 42649165 · DOI · PMC13518915 |
| Benchmarking of real-time, field-deployable whole-genome sequencing of Plasmodium falciparum using Nanopore technology | 2026 | Research article Design not recorded in structured metadata | 9 | cc by PMID 42525447 · DOI · PMC13435551 |
| Single-nucleus transcriptome-wide association study of human brain disorders | 2026 | Research article Design not recorded in structured metadata | 2 | cc by PMID 42778697 · DOI · PMC13600940 |
Scalable high resolution ancestry deconvolution for genomic data [1]
Abstract reading summary — source excerpts
- Opening excerpt
- As genome-wide association studies and genetic risk prediction models extend to globally diverse and admixed biobanks, accurate, scalable ancestry deconvolution, also called local ancestry inference (LAI), has become crucial.
- Closing excerpt
- com/AI-sandbox/gnomix .
- 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)
As genome-wide association studies and genetic risk prediction models extend to globally diverse and admixed biobanks, accurate, scalable ancestry deconvolution, also called local ancestry inference (LAI), has become crucial. LAI assigns ancestry to each genomic segment within an individual, enabling studies of population history and ancestry-associated haplotypic effects. Existing LAI methods scale poorly to biobank-scale data, to the distant past, and to large numbers of ancestries. Here, we introduce several independent LAI methods implemented in the Gnomix software suite, achieving higher accuracy and faster computational performance than all existing approaches and with portable models that can be shared without exposing individual-level training data. Gnomix is paired with Gnofix, a swift, scalable phase correction counterpart. We demonstrate performance on worldwide whole-genome data from humans and canids, leveraging high-resolution accuracy to localise ancient New World haplotypes in the Xoloitzcuintli, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix .
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42649165 · DOI · PMC13518915
Benchmarking of real-time, field-deployable whole-genome sequencing of Plasmodium falciparum using Nanopore technology [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Malaria parasite genomes have been generated predominantly using Illumina short-read sequencing that requires expensive equipment, is time-consuming with complex protocols, and does not adequately interrogate complex genomic regions that harbour important malaria virulence determinants.
- Closing excerpt
- Nanopore sequencing could expedite whole-genome surveillance of malaria and provide new insights into parasite genome biology.
- 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 (156 words)
Malaria parasite genomes have been generated predominantly using Illumina short-read sequencing that requires expensive equipment, is time-consuming with complex protocols, and does not adequately interrogate complex genomic regions that harbour important malaria virulence determinants. The portable Oxford Nanopore Technologies MinION platform generates long reads in real time and may overcome these limitations. We present compelling evidence that Nanopore sequencing delivers valuable additional information for malaria parasites with similar data fidelity for single nucleotide variant (SNV) calls compared to standard Illumina whole-genome sequencing. We demonstrate this through sequencing of pure Plasmodium falciparum DNA, mock infections and natural isolates from low-density, asymptomatic infections. Nanopore has low error rates for haploid SNV genotyping and identifies structural variants not detected with short reads. Nanopore genomes can be directly compared to publicly available genomes and produce high-quality end-to-end chromosome assemblies including complex, previously difficult-to-access regions. Nanopore sequencing could expedite whole-genome surveillance of malaria and provide new insights into parasite genome biology.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42525447 · DOI · PMC13435551
Single-nucleus transcriptome-wide association study of human brain disorders [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Common brain disorders impose a substantial health burden, but localizing their genetic risk in the brain remains challenging1.
- Closing excerpt
- Together, these findings provide a cell-type-resolved and ancestry-aware atlas of genetically regulated expression in the human prefrontal cortex and illustrate how single-nucleus transcriptomics can sharpen gene discovery and therapeutic target prioritization for complex brain disorders.
- 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 (202 words)
Common brain disorders impose a substantial health burden, but localizing their genetic risk in the brain remains challenging1. Although genome-wide association studies have identified numerous loci associated with neuropsychiatric and neurodegenerative disorders, many of these loci lie in non-coding regions that influence gene expression in specific cell types2-5. Traditional bulk brain transcriptomic analyses, which often focus on European ancestry cohorts, average over cellular diversity, obscuring genetic risk-related changes in gene expression. Here we use single-nucleus gene expression profiles from the dorsolateral prefrontal cortex in the multi-ancestry PsychAD cohort to develop transcriptomic imputation models of genetically regulated expression across major brain cell types. Applying these models to neuropsychiatric and neurodegenerative disorders reveals thousands of gene-trait associations that are undetectable in bulk tissue analyses and resolves many signals to discrete neuronal, glial and immune cell populations. Cross-ancestry analyses in the Million Veteran Program confirm these associations, reveal pleiotropic effects of cell-type-specific predicted expression and demonstrate that trait-related dysregulation is conserved across ancestries, enabling mapping of causal genes and pathways. Together, these findings provide a cell-type-resolved and ancestry-aware atlas of genetically regulated expression in the human prefrontal cortex and illustrate how single-nucleus transcriptomics can sharpen gene discovery and therapeutic target prioritization for complex brain disorders.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42778697 · DOI · PMC13600940
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 Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains [4]
- Research article Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies [5]
- Research article Multi-ancestry genome-wide association meta-analysis of Parkinson's disease [6]
- Research article Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention [7]
- Research article GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture [8]
- Research article GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19 [9]
References
- Hilmarsson H, Kumar AS, Barrabés M, Rastogi R, Bustamante CD, Montserrat DM, et al.. Scalable high resolution ancestry deconvolution for genomic data. . 2026. PMID 42649165 · DOI · PMC13518915
- Razook Z, Mehra S, Naung MT, Gilchrist B, Wijegunasekara S, Utama D, et al.. Benchmarking of real-time, field-deployable whole-genome sequencing of Plasmodium falciparum using Nanopore technology. . 2026. PMID 42525447 · DOI · PMC13435551
- Venkatesh S, Kosoy R, Wu Z, Anyfantakis M, Dillard C, N M P, et al.. Single-nucleus transcriptome-wide association study of human brain disorders. . 2026. PMID 42778697 · DOI · PMC13600940
- Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Nielsen TT, et al.. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet. 2023. PMID 36702997 · DOI · PMC10914347
- Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Electronic address: andrew.mcintosh@ed.ac.uk, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. Cell. 2025. PMID 39814019 · DOI · PMC11829167
- Kim JJ, Vitale D, Otani DV, Lian MM, Heilbron K, 23andMe Research Team, et al.. Multi-ancestry genome-wide association meta-analysis of Parkinson's disease. Nat Genet. 2024. PMID 38155330 · DOI · PMC10786718
- Wang Z, Emmerich A, Pillon NJ, Moore T, Hemerich D, Cornelis MC, et al.. Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. Nat Genet. 2022. PMID 36071172 · DOI · PMC9470530
- International League Against Epilepsy Consortium on Complex Epilepsies. GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture. Nat Genet. 2023. PMID 37653029 · DOI · PMC10484785
- Pairo-Castineira E, Rawlik K, Bretherick AD, Qi T, Wu Y, Nassiri I, et al.. GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19. Nature. 2023. PMID 37198478 · DOI · PMC10208981
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.