# Ensembl vs NCBI Gene: Key Differences Ensembl and NCBI Gene are two independent reference databases that describe the same genes, so the core difference between them is annotation lineage: Ensembl builds its gene set with the Ensembl annotation system (using GENCODE for human and mouse), while NCBI Gene consolidates gene records anchored to the RefSeq annotation pipeline. The rule of thumb is simple. Use Ensembl when you need genome-browser exploration, GENCODE-style transcript models, BioMart bulk downloads, or Variant Effect Predictor annotation. Use NCBI Gene when you need curated gene summaries, RefSeq transcript and protein accessions, dbSNP variant records, or programmatic retrieval through E-utilities. Most serious projects end up using both and mapping between them. This guide explains what each resource is, how their gene models are produced, how their identifiers differ, and how to choose between them for real tasks such as variant annotation, orthology mapping, and bulk data retrieval. Every comparison is anchored in published head-to-head evaluations rather than general impressions. ## What Ensembl and NCBI Gene Actually Are ### Ensembl Ensembl is a genome database and browser jointly produced by EMBL-EBI (the [European Bioinformatics Institute](/knowledge/bioinformatics/the-european-bioinformatics-institute-embl-ebi), part of the European [Molecular Biology](/blog/careers/molecular-biology) Laboratory) and the Wellcome Sanger Institute. It stores annotated genomes for vertebrates and, through companion sites such as Ensembl Genomes, for plants, bacteria, fungi, and protists. Its defining features are the Ensembl gene annotation pipeline, the [Ensembl genome browser](/blog/guides/ensembl-genome-browser), the BioMart bulk export tool, the REST API, and the Variant Effect Predictor (VEP), a tool that assigns functional consequences to genetic variants. A practical detail that surprises newcomers is that Ensembl gene sets for human and mouse are produced in collaboration with the GENCODE project. GENCODE is the reference gene annotation used by major consortia, and the Ensembl human release is a GENCODE-aligned release. This matters when you download a GTF file (a standard text format listing genes, transcripts, and exon coordinates) because the Ensembl human GTF and the GENCODE GTF share annotation lineage, which is why many RNA-seq pipelines default to them. Ensembl identifiers follow a predictable format. A human gene carries an ENSG identifier such as ENSG00000141568 for TP53, transcripts carry ENST identifiers, proteins carry ENSP identifiers, and the species prefix changes per organism. These are called stable identifiers because Ensembl tries to keep the same ID attached to the same gene across releases even when coordinates shift. ### NCBI Gene and RefSeq NCBI Gene is the gene database at the National Center for Biotechnology Information, part of the US National Library of Medicine at NIH. It serves as a centralized hub that collects gene records from many sources, including official nomenclature committees such as HGNC (the HUGO Gene Nomenclature Committee for human genes) and VGNC (the Vertebrate Gene Nomenclature Committee), and it links each gene to its RefSeq transcripts and proteins, its position on RefSeq genome assemblies, expression data, phenotype links, and homology records. RefSeq is the companion annotation and curation effort. RefSeq transcripts carry accessions such as NM_000546 for a well-characterized human TP53 messenger RNA and NP_000537 for its protein. RefSeq accessions are versioned with a trailing number (for example NM_000546.6), so the accession itself tells you which transcript sequence version you used. NCBI annotates eukaryotic genomes with its own Eukaryotic [Genome Annotation Pipeline](/blog/guides/genome-annotation-pipeline), which is a separate effort from Ensembl annotation and produces a different gene set. NCBI Gene identifiers are stable integers. TP53 is GeneID 7157. Unlike Ensembl identifiers, GeneIDs are short and species-agnostic in format, which makes them convenient as a cross-database hub key. ### Why both exist The two resources grew from different mandates. Ensembl grew out of large-scale [genome analysis](/blog/guides/genome-analysis) in Europe with heavy investment in automated annotation, [comparative genomics](/blog/guides/comparative-genomics), and variation interfaces. NCBI Gene grew out of the US effort to consolidate sequence, literature, and nomenclature information into a single curated entry point. Both are high quality. They simply answer slightly different questions, and several published studies now quantify exactly how much their outputs diverge,. ## Side-by-Side Comparison Table | Feature | Ensembl | NCBI Gene / RefSeq | |--|--|--| | Operated by | EMBL-EBI and the Wellcome Sanger Institute | National Center for Biotechnology Information, NIH | | Primary gene annotation | Ensembl Gene Annotation System, GENCODE for human and mouse | NCBI Eukaryotic Genome Annotation Pipeline, RefSeq curation | | Gene identifier | Stable IDs such as ENSG00000141568 | Integer GeneID such as 7157, RefSeq accessions NM_, NP_ | | Accession versioning | Release numbers (for example release 113), archive site for old releases | Versioned accessions such as NM_144701.3 | | Browser | Ensembl genome browser with location, gene, transcript, and variation views | Genome Data Viewer plus the Gene database entry | | Bulk download | GTF and FASTA files by FTP, BioMart web export, REST API | FTP annotation releases, GFF files, E-utilities API | | Variant annotation | Variant Effect Predictor (VEP) with plugins and custom annotations | dbSNP and ClinVar integration, Variation Services API | | Orthology resources | Ensembl Compara gene trees | HomoloGene and Gene orthology links | | Nomenclature linkage | HGNC and VGNC symbols in gene pages | HGNC, VGNC, and other nomenclature authorities consolidated per gene | | Best for | Transcript-level analysis, RNA-seq reference files, comparative genomics, [variant effect prediction](/knowledge/bioinformatics/machine-learning-for-variant-effect-prediction-on-protein-stability) | Gene-level curation, literature and phenotype links, RefSeq sequences, cross-database ID anchoring | The table captures the headline difference between Ensembl and NCBI Gene, but the criteria below are what actually decide the choice in a working project. ## The Identifier Systems Are the First Real Difference The most common practical problem in the ensembl vs ncbi gene comparison is not the browser. It is identifiers. A gene symbol such as ASMT or BID is not unique. The same symbol can refer to different genes, and different symbols can refer to the same gene across versions and synonyms. A large 2025 survey of protein-coding gene nomenclature in humans and model mammals retrieved all identifiers from the Ensembl genome browser, release 113, and found that the total number of symbols used to identify protein-coding genes, counting official symbols and synonyms, ranged from 16,600 in pig to 64,580 in mouse [3]. More striking, gene symbols are ambiguous at scale. Symbols that map to more than one gene could complicate the identification of roughly 10 percent of rat and mouse genes and about 18 percent of human protein-coding genes [3]. The authors' remedy matches standard practice in the field: use stable gene identifiers assigned by the scientific institutions and committees that maintain them, including Ensembl, NCBI, RGD, HGNC, and VGNC, and retrieve the genomic information for those identifiers directly from the databases that own them [3]. Three consequences follow for daily work. First, never store gene symbols as the primary key in an analysis. Store the Ensembl ID, the NCBI GeneID, or both, and derive symbols only at the last reporting step. Second, when you read a paper that reports a symbol, resolve it to a stable ID before you reproduce the analysis. A symbol plus species plus database release is the minimum needed to be sure you are looking at the same gene the authors meant. Third, mapping between the two systems is a standard operation. NCBI Gene records link to Ensembl identifiers, and Ensembl gene pages link to NCBI GeneIDs. Bulk mapping is available on both sides. Ensembl's BioMart can export an attribute table that includes NCBI GeneIDs, and NCBI provides E-utilities endpoints that return cross-references. One-verse conversions such as OrthologAL, described below, build directly on the BioMart interface to Ensembl [4]. ## How the Gene Models Are Produced, and Why They Differ Ensembl and NCBI annotate genomes with independent pipelines. Ensembl applies its annotation system, combining RNA-seq and protein evidence with ab initio prediction, and for human and mouse this is delivered in coordination with GENCODE. NCBI applies its Eukaryotic Genome Annotation Pipeline to each assembly and layers RefSeq curation on top for well-studied genes. Because the pipelines, evidence sets, and update schedules differ, the resulting gene models differ in three predictable ways. ### Gene and transcript counts The same genome can carry different gene counts in each database, because the pipelines disagree about which regions are genes, how many transcripts a gene produces, and where exons start and end. The chicken gene atlas GEGA makes this concrete. It integrated the gene catalogs from NCBI-RefSeq and Ensembl/GENCODE for the GRCg7b chicken assembly, and the merged atlas reached 78,323 genes, including 24,102 protein-coding genes and 44,428 long non-coding RNAs, substantially increasing the gene number available from either resource alone [5]. A union catalog is larger than either catalog precisely because each database captures genes and transcripts the other misses or models differently. ### Per-gene model quality A 2025 manual assembly study of pig, mouse, and human genomes evaluated how many protein-coding genes were assembled and annotated without error in recent builds. The percentage of error-free assembled and annotated genes was 58.9 percent in the NCBI build 11.1, 51.7 percent in the Ensembl build, and 47.1 percent in the MARC build 1.0 [2]. The authors traced the errors to nine predominant sources and showed, with a library of 6,135 protein-coding genes, that no single build is uniformly correct [2]. The practical lesson is that annotation quality is gene-specific. For any individual gene, the Ensembl model and the NCBI model can differ in exon structure, and either can contain an error. ### Downstream effects on orthology Annotation differences propagate into comparative analyses. A 2025 benchmark compared gene models from four prominent sources, the NCBI Eukaryotic Genome Annotation Pipeline, the Ensembl Gene Annotation System, UniProt Reference Proteomes, and the ab initio predictor Augustus 3.4, and showed that different annotation methods yield markedly distinct orthology inferences, with discrepancies in the proportion of orthologous genes per genome, the completeness of hierarchical orthologous groups, and the accuracy and recall of predicted orthologs on a standard benchmark [1]. If your comparative genomics result depends on which annotation you fed in, the annotation is not a detail. It is part of the result. Whenever orthology conclusions matter, run the analysis with more than one gene set or state explicitly which pipeline produced your input models [1]. ## When to Use Ensembl The question of when to use Ensembl has a concrete answer for several recurring tasks. ### Transcript-level and RNA-seq work Ensembl GTF files are the default reference for most splice-aware alignment and quantification tools, including standard STAR and Salmon workflows. Because the human and mouse Ensembl annotations are GENCODE-coordinated, transcript IDs in your count matrix can be interpreted against the GENCODE transcript catalog, which documents each transcript's support level. If your analysis resolves individual isoforms rather than whole genes, this is the strongest reason to choose Ensembl. ### Variant effect prediction VEP is the single most cited reason researchers touch Ensembl programmatically. It takes a VCF (variant call format) file and reports each variant's consequence on transcripts, using cache files or online databases for a chosen assembly. Published variant-prioritization workflows illustrate the pattern. In an IL23R study of inflammatory bowel disease, 723 missense variants collected from NCBI dbSNP were verified against the RefSeq transcript NM_144701.3 and protein Q5VWK5-1 using Ensembl VEP on GRCh38, then filtered through SIFT, PolyPhen-2, PROVEAN, and other predictors to reach 37 high-risk variants [6]. That workflow is a textbook example of the division of labor: NCBI supplies the variant catalog and the reference transcript accession, Ensembl supplies the consequence annotation machinery. The same pattern appears across disease-gene studies. Missense variants for the PITX2 gene were mined from both NCBI dbSNP and the Ensembl databases before pathogenicity scoring with SIFT, PolyPhen-2, GERP, and CADD [7]. Variant data for the USH1G gene were curated and filtered from the Ensembl database before structural modeling, with NCBI BLASTP used to assess conservation [8]. Esophageal cancer work identified 126 protein isoforms for 28 genes from NCBI and Ensembl together and mapped rare variants to transcript-level isoforms [9]. Horse DMRT3 variants, including the well-known gait-associated stop-gain variant, were annotated and classified using Ensembl and NCBI databases side by side [10]. In other words, the community norm for variant studies is dual-database annotation, and VEP is the Ensembl side of that norm. ### Comparative genomics and orthology conversion Ensembl Compara maintains gene trees and orthology assignments across species, and BioMart exposes them for bulk export. Applied tools build on this layer. OrthologAL, a Shiny application for converting non-human [transcriptomics](/knowledge/bioinformatics/modern-transcriptomics-bulk-single-cell-spatial) data to human orthologs, interfaces with BioMart to access different gene sets from the Ensembl database and converts Seurat objects from single-cell and spatial experiments without user-written code [4]. If your task is humanization of mouse, rat, or xenograft expression matrices, the Ensembl orthology layer is the practical substrate. ### Bulk retrieval through BioMart and the REST API BioMart answers the question of how to use Ensembl at scale. You choose a dataset (for example Human genes, GRCh38), choose filters (for example a chromosome, a gene list, or a biotype), choose attributes (Ensembl ID, HGNC symbol, NCBI GeneID, coordinates, biotype), and export a table. The same queries are available through the Ensembl REST API for scripts. For GTF and FASTA files, the Ensembl FTP site provides per-release downloads, and the archive site serves previous releases so that an analysis can be reproduced against the exact annotation version it originally used. ## When to Use NCBI Gene ### Curated gene-level information A NCBI Gene record is the fastest way to answer gene-level questions that span literature, function, phenotype, and sequence. Each record consolidates the official symbol from the nomenclature committee, aliases, a summary, RefSeq transcript and protein accessions, genomic context, expression references, pathway links, interacting genes, homology, and links to dbSNP variation. For human genes, the HGNC official symbol and GeneID anchor the record. For a quick authoritative answer about what a gene is, NCBI Gene is usually the shorter path. ### RefSeq transcripts and proteins RefSeq accessions are the de facto standard for reporting a specific transcript or [protein sequence](/blog/guides/protein-sequence) in publications, because each accession is versioned. When a variant study pins its analysis to NM_144701.3, anyone can retrieve the exact [nucleotide sequence](/knowledge/molecular-biology/nucleotide-sequence) that was analyzed [6]. Ensembl transcript IDs serve the same role within Ensembl-based analyses, but RefSeq accessions remain the most widely recognized currency for reporting a single transcript in clinical and experimental genetics. ### Variation databases and Genome Data Viewer dbSNP, the archive of short genetic variation, sits inside NCBI, and NCBI Variation Tools link variants to genes directly. The NCBI Genome Data Viewer is NCBI's graphical genome browser. Comparative genomics work shows it in action. When the pig ASMT gene, which encodes the final enzyme of melatonin synthesis, lacked genomic annotation, the authors used the NCBI Genome Data Viewer to record that AKAP17A sits next to ASMT in most animals, and that ASMTL sits adjacent to ASMT in the opposite orientation. Applying that three-point localization logic to pig X chromosome coordinates, they identified the NCBI-predicted gene LOC110258194 as the positional match for ASMT [11]. Ensembl held a gene at the same position, ENSSSCG00000032659, with completely overlapping exons, but it was not annotated as ASMT [11]. Domain analysis with NCBI's CDD and orthology analysis with TreeFam at EMBL-EBI supported the assignment [11]. This single case study contains the whole ensembl or ncbi gene decision in miniature. NCBI's comparative context made the gene findable, Ensembl's stable ID gave it a permanent handle, and neither database alone told the complete story. ### Programmatic retrieval through E-utilities NCBI's E-utilities are a set of HTTP endpoints for querying Entrez databases. A typical pattern is to fetch gene metadata by GeneID and extract cross-references, RefSeq accessions, and summary text. For pipelines that need to resolve thousands of symbols to stable IDs, E-utilities against NCBI Gene plus the gene history and cross-reference files provide the hub mapping that symbol-based inputs require. ## How to Use Ensembl: A Practical Browser Walkthrough The following ensembl [genome browser tutorial](/blog/guides/genome-browsers-for-researchers-a-guide-to-inspecting-genomic-evidence) covers the moves that cover most needs. It uses human GRCh38 and the gene TP53 as the worked example. 1. Search. Go to the Ensembl site, type TP53 in the search box, and select the human gene result. The search resolves the symbol to the current stable ID and opens the gene page. Confirm the species and assembly in the page header before you continue, because the same symbol exists in many species. 2. Read the gene page. The gene page shows the location, the biotype (protein coding for TP53), the stable ID, and tabs for transcripts, regulation, variation, comparative genomics, and external references. The external references section lists the NCBI GeneID, HGNC symbol, UniProt entry, and other cross-links, which is the quickest manual way to bridge the two databases. 3. Inspect transcripts. The transcript table lists every Ensembl transcript with its biotype, length, and protein product. The canonical transcript is flagged. Compare this list to the RefSeq transcript set on the NCBI Gene page and note that the counts differ. That difference is the annotation-pipeline divergence described above, not an error on either side. 4. Explore the location view. The Location tab shows the genomic region with tracks for transcripts, regulatory features, variants, and comparative data. Track configuration lives in the Configure this page menu, where you can turn on specific data categories, change the rendering density, and export the view image. 5. Check variants. The Variation table lists variants in the gene with their consequence types and allele frequencies where population data exist. Clicking a variant opens its page with links onward to source databases. 6. Export data. Use Export data on any view to pull sequence, coordinates, or gene trees for the current region. For whole-genome or gene-list exports, open BioMart, select the Ensembl Genes dataset, choose the human genes dataset with the correct assembly and release, add a filter (for example the list of Ensembl IDs from your differential expression table), select attributes including the NCBI GeneID, and export as CSV or TSV. 7. Annotate variants with VEP. For a VCF, run the standard command form `vep -i input.vcf, cache, assembly GRCh38, everything` locally, or use the web interface by pasting variants. VEP returns per-transcript consequences, and plugins extend it with scores from external predictors. The IL23R workflow cited above used exactly this tool, on GRCh38, as the verification step between variant collection and pathogenicity filtering [6]. 8. Reproduce later. Record the Ensembl release number from the page footer. If the analysis must be reproduced years later, retrieve the same release from the Ensembl archive. This habit costs seconds and prevents the silent version drift that breaks reproducibility. ## Working With Both Databases in One Workflow The strongest workflows treat the two resources as complementary layers rather than competitors. ### Variant annotation pipeline ![Flowchart from collecting variants in dbSNP, pinning a RefSeq transcript version, running Ensembl VEP, filtering, to reporting both identifiers.](/article-images/illustrations/ensembl-vs-ncbi-gene-variant-pipeline.webp) A realistic variant triage workflow looks like this. Collect variants from NCBI dbSNP or from your own calling pipeline. Pin the analysis to a specific RefSeq transcript version so the reference sequence is unambiguous. Run Ensembl VEP on GRCh38 to assign transcript consequences. Filter with pathogenicity tools. Report both the RefSeq accession and the Ensembl transcript ID for every variant. Published studies on IL23R, USH1G, PITX2, and the esophageal cancer gene panel all follow some version of this dual-database design [6], [8], [7], [9]. ### Gene-level review after browser exploration Sequence-complementarity work shows the reverse direction. In a study of TRIM33 and paraneoplastic dermatomyositis, the authors started from the NCBI RefSeq sequence for the gene region and ran BLASTN against human GRCh38 within the Ensembl database to find overlapping non-coding RNAs, then followed the hits into other interaction databases [12]. An ensembl or ncbi gene choice made at the first step would have constrained the result. Using the NCBI accession as the input sequence and Ensembl as the search target widened it. ### Orthology and cross-species projects For cross-species expression analysis, decide the orthology source deliberately, because annotation source changes orthology inference [1]. Ensembl Compara through BioMart is one option, and tools such as OrthologAL package it for Seurat objects [4]. NCBI Gene orthology links are the alternative for gene-level work. Where 1:1 orthology between pig, mouse, and human proteins was tested with a manually curated protein library, 73.8 percent of the proteins were conserved across the three species, and when a gene was missing from one genome, the pig genome was 5.0 times more likely to have retained the human gene than the mouse genome, a difference the authors attribute to assembly and annotation completeness rather than true gene loss [2]. The numbers reinforce the same caution. Missing genes in comparative pipelines are often annotation gaps, not biology. ### Integrated species atlases When a community needs both catalogs at once, the productive move is to merge them with transparent gene-level mapping. GEGA does this for chicken, combining NCBI-RefSeq and Ensembl/GENCODE catalogs on GRCg7b into a single atlas with expression profiles across 47 tissues from 36 datasets and 1,400 samples, plus orthology retrieval and genomic context views [5]. The union catalog principle in GEGA, where the combined set is larger than either source, generalizes to any species where you can afford the bookkeeping. ## Common Mistakes and Limitations These are the errors that appear repeatedly in real projects, each with the fix. **Using symbols as primary keys.** With ambiguous symbols affecting on the order of 10 percent of mouse and rat genes and about 18 percent of human protein-coding genes, symbol-keyed analyses misassign genes at a rate that dwarfs most other error sources [3]. Fix: store stable IDs, resolve symbols last. **Assuming the two databases agree on gene structure.** They frequently do not. Gene counts and transcript models differ by construction, and merged catalogs exceed either source alone [5]. Fix: name the annotation source and release in every figure and file, and never mix Ensembl and RefSeq transcript coordinates in one coordinates file. **Assuming either annotation is complete or error-free.** Manual assembly of pig, mouse, and human genes showed error-free annotation in the range of roughly half of protein-coding genes across the major builds, with nine predominant error sources [2]. An absent gene can be an assembly or annotation gap rather than a true absence, and orthology inference quality tracks annotation quality directly [1], [2]. Fix: when a gene of interest is missing, check the other database and the raw assembly before concluding deletion or loss. **Mixing assemblies.** Coordinates from GRCh37 and GRCh38, or from different assemblies of the same species, must never be combined without liftover. Different builds of the pig genome alone produced materially different error profiles in the manual comparison [2]. Fix: one assembly per analysis, stated everywhere. **Ignoring accession versions.** NM_000546 without a version number does not pin a sequence. Fix: always record the versioned accession, as variant studies that verify against a specific RefSeq protein sequence do [6]. **Running orthology conversion without checking the source of the gene models.** Because the NCBI pipeline, the Ensembl system, UniProt proteomes, and ab initio prediction produce different orthology outputs, converting identifiers through a tool inherits the tool's underlying annotation [1]. Fix: record which annotation your orthology calls derive from, and spot-check a sample of 1:1 pairs. **Reproducing against a moving release.** Ensembl updates its releases, and archived releases are the only way to return to an older annotation. NCBI annotation releases behave the same way. Fix: freeze the release or accession version at project start and store it in the repository. **Trusting an unannotated stable ID as a dead end.** The pig ASMT case shows an Ensembl gene with completely overlapping exons at the expected position that simply lacked the ASMT name, while the NCBI viewer supplied the genomic neighborhood logic that identified it [11]. Fix: when a stable ID has no symbol, resolve it by orthology, domain tools such as NCBI CDD, and synteny rather than discarding it. One general limitation applies to any database-driven result. Annotation reflects current evidence and changes with every release, so conclusions should be tied to the release you used, and individual research questions, like individual clinical cases, deserve expert review before decisions rest on a database record alone. ## Frequently Asked Questions ### Which should I choose, Ensembl or NCBI Gene? ![Decision path asking whether the task is transcript level, variant annotation, orthology or gene-level curation, ending at Ensembl or NCBI Gene.](/article-images/illustrations/ensembl-vs-ncbi-gene-database-choice.webp) Neither is universally better. Choose Ensembl for transcript-level reference files, VEP variant annotation, BioMart bulk export, and cross-species comparative work. Choose NCBI Gene for curated gene summaries, versioned RefSeq accessions, dbSNP integration, and E-utilities retrieval. Projects that mix variant catalogs from NCBI with consequence annotation from Ensembl, as many published pipelines do, get the best of both [6]. ### Do Ensembl and NCBI Gene agree on gene counts? No. Their independent annotation pipelines produce different gene and transcript sets for the same assembly, which is why a merged catalog of the two chicken resources yielded 78,323 genes, more than either source provides alone [5]. Counts also shift between releases of the same database. ### How do I map Ensembl gene IDs to NCBI Gene IDs? Use the cross-references on either database's gene page for one-off lookups. For bulk mapping, BioMart can export Ensembl IDs together with NCBI GeneIDs as attributes, and E-utilities can return cross-references for GeneIDs. Tools built on BioMart, such as OrthologAL, perform the mapping inside an orthology conversion step [4]. ### When is Ensembl the better choice for a project? Ensembl is the better choice when your analysis depends on transcript models, when you need a GTF for RNA-seq alignment and quantification, when you want VEP consequence annotation on GRCh38 or another assembly, or when you need orthology groups across species from Compara [1], [4]. Use the archive site to lock a release for reproducibility. ### How do I download a gene list from Ensembl? Open BioMart, choose the species and assembly dataset, apply filters such as a chromosome or biotype, select attributes including coordinates and identifiers, and export the table. For whole-genome files, download the GTF and FASTA from the Ensembl FTP site for a specific release, and note the release number in your methods. ### Why does a gene have a different name or position in Ensembl than in NCBI? Names differ because nomenclature evolves and synonyms accumulate, and positions differ when the underlying assemblies or gene models differ. The pig ASMT gene was carried as an unnamed Ensembl ID at the same exons where NCBI showed a predicted LOC entry, and comparative context from both databases resolved it [11]. Record the stable ID and the release, and treat the symbol as a label rather than an identifier. ## Sources 1. [Annotation matters: the effect of structural gene annotation on orthology inference.](https://pubmed.ncbi.nlm.nih.gov/40560601/) 2. [Verification and comparison of pig, mouse, and human genome similarities: use of manual assembly and analyses.](https://pubmed.ncbi.nlm.nih.gov/41413762/) 3. [Protein-coding genes in humans and model mammals (mouse, rat and pig): gene identifiers and disambiguation of gene nomenclature retrieved from the Ensembl genome browser.](https://pubmed.ncbi.nlm.nih.gov/41408139/) 4. [OrthologAL: a Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data.](https://pubmed.ncbi.nlm.nih.gov/40392208/) 5. [GEGA (Gallus Enriched Gene Annotation): an online tool providing genomics and functional information across 47 tissues for a chicken gene-enriched atlas gathering Ensembl and Refseq genome annotations.](https://pubmed.ncbi.nlm.nih.gov/39157583/) 6. [In Silico Identification and Structural Characterization of High-Risk Missense SNVs in the Human IL23R Gene Relevant to Inflammatory Bowel Disease.](https://pubmed.ncbi.nlm.nih.gov/42353858/) 7. [In Silico Characterization of Pathogenic Homeodomain Missense Mutations in the PITX2 Gene.](https://pubmed.ncbi.nlm.nih.gov/38802693/) 8. [Computational study of deleterious missense SNPs in the USH1G gene implicated in Usher syndrome.](https://pubmed.ncbi.nlm.nih.gov/40892044/) 9. [Computational identification of rare pathogenic genomic variants in esophageal cancer markers: Transcript-level analysis, sequence-based insights, and structural-functional impacts of non-synonymous SNPs.](https://pubmed.ncbi.nlm.nih.gov/41777455/) 10. [DMRT3 gene variations in horse breeds selected for gaited movement - established research and novel findings.](https://pubmed.ncbi.nlm.nih.gov/41936970/) 11. [Localization and Molecular Cloning of the ASMT Gene for Melatonin Synthesis in Pigs.](https://pubmed.ncbi.nlm.nih.gov/39859321/) 12. [Sequence Alignment between TRIM33 Gene and Human Noncoding RNAs: A Potential Explanation for Paraneoplastic Dermatomyositis.](https://pubmed.ncbi.nlm.nih.gov/38929849/)