# Ensembl BioMart vs. Expression Atlas Ensembl BioMart is a bulk query and export tool for Ensembl's annotation databases, while Expression Atlas is a curated collection of [gene expression](/blog/guides/gene-expression) results from reanalyzed public experiments. BioMart answers the question "give me a table of gene attributes." Expression Atlas answers the question "where and when is this gene expressed, and does it go up or down under a condition." The rule of thumb is simple. If the deliverable is a list of gene identifiers, sequences, coordinates, or orthologs, use BioMart. If the deliverable is an expression pattern across tissues or conditions, use Expression Atlas. In many real projects you will use both in sequence, and this article shows how they fit together. Researchers frequently frame the difference between Ensembl BioMart and Expression Atlas as a choice of database, but that framing is misleading. They are different kinds of resources. One is an annotation query engine. The other is an expression evidence resource. Understanding that distinction resolves almost every "BioMart or Expression Atlas" question before it starts. ## Ensembl BioMart vs. Expression Atlas at a Glance | Criterion | Ensembl BioMart | Expression Atlas | |--|--|--| | Primary purpose | Bulk export of gene, variant, and comparative annotations | Curated gene expression results, baseline and differential | | Data type | Identifiers, sequences, coordinates, orthologs, homologs, GO terms, variant annotations | Normalized expression values, up/down regulation calls, tissue and cell-type patterns | | Data provenance | Ensembl annotation pipelines and gene builds, updated each Ensembl release | Public experiments reprocessed through a uniform EMBL-EBI analysis pipeline | | Query style | Pick a dataset, apply filters, choose attributes, export a table | Search a gene or condition, browse heatmaps and summaries | | Output | HTML, TSV, CSV tables, sequences in FASTA | Interactive web views, downloadable experiment data, programmatic API | | Programmatic access | biomaRt R package (Bioconductor), REST endpoints | Expression Atlas REST API | | Species coverage | Vertebrates plus Ensembl Genomes divisions (plants, fungi, metazoa, protists, bacteria) | Animals, plants, and single-cell experiments, depending on curated availability | | Versioning | Tied to Ensembl release numbers, which you must record | Tied to experiment releases and pipeline versions, which you must record | | Best for | Gene lists, ID mapping, sequence retrieval, orthology, bulk annotation | "Where is it expressed," "which condition turns it on," cross-experiment browsing | | Not designed for | Expression evidence, differential expression calls | Custom bulk annotation exports, sequence retrieval | The table is the core of the ensembl biomart vs. expression atlas decision, but the reasoning behind each row matters more than the row itself. The sections below explain each tool on its own terms, then compare them on the criteria that decide real choices in day-to-day analysis. ## What Ensembl BioMart Does BioMart is a data-mining interface that sits on top of Ensembl's relational databases. It was designed for one job, extracting large tables of structured annotation, and it does that job well. The public entry point is the BioMart portal at biomart.ensembl.org, and the same query engine powers the R package biomaRt on Bioconductor. ### The BioMart query model Every BioMart query has three parts. You choose a dataset, apply filters, and select attributes. - The dataset is the database you are querying, for example the human genes dataset or a plant genes dataset. BioMart lists all available Ensembl marts and datasets when you open the portal or call the appropriate function in R. - Filters narrow the rows. Common filters include chromosome, gene biotype, a list of HGNC symbols, a genomic region, or a list of Ensembl gene IDs. - Attributes define the columns. Common attributes include Ensembl gene ID, external gene name, chromosome, start and end position, biotype, transcript ID, [protein sequence](/blog/guides/protein-sequence), and ortholog identifiers in a chosen species. This filter-and-attribute model is why BioMart feels different from a search engine. You are not asking a question in words. You are constructing a table. A typical query might be "all protein-coding genes on chromosome 7, with Ensembl IDs, HGNC symbols, start and end coordinates, and their mouse orthologs, exported as TSV." That query runs in seconds and returns a file you can open in R, Python, or a spreadsheet. Output formats include HTML for browsing, TSV and CSV for analysis, and FASTA for sequence retrieval. Sequences are a significant BioMart strength. You can export cDNA, coding sequence, peptide sequence, or genomic sequence flanking a region, in bulk, for thousands of genes at a time. ### The biomaRt R package For reproducible pipelines, the biomaRt R package is the standard interface. A minimal session looks like this. ```r library(biomaRt) listMarts() mart <- useEnsembl(biomart = "genes") listDatasets(mart) mart <- useDataset(dataset = "hsapiens_gene_ensembl", mart = mart) getBM(attributes = c("ensembl_gene_id", "external_gene_name", "chromosome_name", "start_position", "end_position"), filters = "hgnc_symbol", values = c("TP53", "BRCA1", "EGFR"), mart = mart) ``` The two `list` calls are not decoration. Dataset names change between Ensembl releases, and mart names differ between the vertebrate server and the Ensembl Genomes servers. Listing them first prevents most connection errors. For non-vertebrate species, biomaRt connects to the Ensembl Genomes marts through the appropriate `useEnsembl` arguments, and the package documentation shows the current call signatures. biomaRt is not a niche tool. A study of sex-biased gene expression in human and macaque brains used biomaRt to acquire orthologous gene mappings between the two species directly from the BioMart Ensembl website, which then anchored their entire cross-species comparison [1]. That is the canonical use case. When a paper says "orthologs were obtained from BioMart," it almost always means this package. ### BioMart across the Ensembl family BioMart is not limited to the main vertebrate Ensembl site. Ensembl Genomes extends the annotation infrastructure to plants, fungi, metazoa, protists, and bacteria, and partner resources build on the same foundations. WormBase ParaSite, the integrated platform for helminth genomics, is developed in collaboration with WormBase and Ensembl Genomes and exposes genome browsers, bulk downloads, and programmatic interfaces built on this shared infrastructure [2]. If your organism lives in one of those divisions, the BioMart query model still applies, and the practical consequence is that the same filter-and-attribute workflow you learned on human genes transfers to maize, rice, or parasitic nematodes. Plant genomics work makes this concrete. One study of chromatin remodeling genes identified and analyzed 35 members of the maize Snf2 gene family, named ZmCHR1 through ZmCHR35, using the Ensembl Plants database as the annotation source for the entire gene family analysis [3]. A query of that shape, filter by gene symbol or family keyword, retrieve sequences and coordinates, is exactly what BioMart is built for. ### How BioMart is used in published work A scan of the recent literature shows a consistent pattern of BioMart use. - Sequence and protein retrieval. A study of the chicken LECT2 gene retrieved the amino acid sequences of LECT2 proteins from fowl, fish, and mammalian species from the Ensembl database, then ran downstream comparative analyses on those sequences [4]. - Reference annotation for transcriptomics. Differential expression studies routinely define their feature space using Ensembl gene models. An osteoarthritis cartilage study performed differential expression on long noncoding RNAs annotated in the GENCODE and Ensembl databases, identifying 5,053 robustly expressed lncRNAs of which 191 were significantly differentially expressed between lesioned and preserved tissue [5]. A heart failure study cross-referenced its RNA-seq transcripts to Ensembl release 73 on the GRCh37 genome build before classifying differentially expressed noncoding RNAs [6]. - Orthology and comparative genomics. The human and macaque brain study described above is representative [1]. Notice what is absent from these examples. None of them used BioMart to obtain expression values. They used it to build the annotated scaffolding, gene models, sequences, and ortholog maps, on which expression analysis then ran. That division of labor is the whole answer to "when to use Ensembl BioMart." ## What Expression Atlas Does ![Comparison of baseline, differential and single-cell Expression Atlas data by question answered and what the output shows.](/article-images/illustrations/ensembl-biomart-vs-expression-atlas-atlas-faces.webp) Expression Atlas, maintained by EMBL-EBI, answers a different question. Instead of exporting annotation, it stores expression evidence. The resource takes public experiments, reprocesses the raw data through a uniform analysis pipeline, and presents the results in browsable, searchable form. Because every experiment is processed the same way, Expression Atlas values are comparable within and across experiments in a way that ad hoc downloads from individual papers are not. The resource has three main faces. ### Baseline expression Baseline data answer "where is this gene expressed in normal conditions." For a human gene, you can see expression across anatomical systems and tissues, down to the level of individual tissue consensus values. For many species, including plants, baseline datasets show tissue-specific expression. A plant biologist asking whether a candidate gene is root-specific, leaf-specific, or broadly expressed gets that answer from baseline data without touching a single fastq file. This is the question the maize Snf2 study addressed after its BioMart-style retrieval. Having identified the 35 family members from Ensembl Plants, the authors analyzed tissue-specific expression patterns and differential expression under stress conditions, then validated selected genes by qRT-PCR under drought stress, observing upregulation at 12 hours and downregulation at 24 hours for certain genes [3]. The retrieval and the expression analysis are separate steps, and the second step belongs to an expression resource, whether Expression Atlas or the authors' own processed data. ### Differential expression Differential data answer "does this gene go up or down under a perturbation." Expression Atlas reanalyzes public RNA-seq experiments comparing a treatment against a control, a disease tissue against a healthy tissue, or a mutant against wild type, and reports fold changes and significance in a consistent format. You can search by gene to see every curated experiment in which that gene changes, or search by condition to find experiments relevant to your biological question. The value of the uniform pipeline is hard to overstate. Individual deposited datasets differ in platform, normalization, and statistical treatment. When a resource reprocesses everything identically, cross-experiment browsing becomes meaningful, and the up/down calls you see are on a common footing. ### Single-cell expression The Single Cell Expression Atlas extends the same idea to single-cell RNA-seq experiments, allowing you to see which cell types express a gene within a curated experiment. For cell-type-specific questions, such as whether a candidate marker is restricted to a specific immune subset, this is often the fastest route to an answer. ### What Expression Atlas is not Expression Atlas is not an annotation database. You cannot bulk-export exon coordinates, protein sequences, or orthology tables from it, because it does not store those as its primary content. It is also not a general-purpose bulk downloader of arbitrary attribute tables. If your deliverable is a file with 40,000 rows and a dozen annotation columns, BioMart is the tool. If your deliverable is a biological claim about expression, Expression Atlas is the tool. ## How to Use Ensembl: A Short Genome Browser Orientation The keyword cluster behind this article includes "how to use Ensembl" and "ensembl genome browser tutorial," so a brief orientation helps, because the genome browser is the front door to both BioMart and the annotation content that feeds your expression questions. The main Ensembl site at ensembl.org organizes data by species, with genome assemblies identified by version. The typical workflow for a single gene looks like this. 1. Search by gene symbol, Ensembl ID, or sequence. The search bar on the home page accepts all three. 2. Land on the gene summary page. This page shows the gene model, synonyms, biotype, and links out. 3. Explore the Location view. This is the genome browser proper, showing the gene in genomic context with tracks for transcripts, regulatory features, variants, and comparative data. You can zoom, drag, and configure tracks. 4. Open the Sequence view. Here you can retrieve cDNA, coding sequence, peptide, or genomic sequence for the gene, with exon boundaries marked. 5. Check the comparative genomics views. These show orthologs and paralogs, gene trees, and pairwise alignments. 6. Use the export controls. For one gene, the page-level exports are enough. For tens of thousands of genes, this is where you switch to BioMart, because clicking through pages one gene at a time does not scale. Two habits separate careful users from frustrated ones. First, always record the Ensembl release number shown at the top of the site. Second, when you leave the browser for a bulk query, remember that BioMart queries the same underlying databases as the browser, so the annotations you saw on the gene page are the annotations you will get in the table. A separate Ensembl tool worth knowing in this context is the Variant Effect Predictor, or VEP. It classifies the functional consequences of variants against Ensembl transcripts. A recent association study of FKBP5 polymorphisms in stress-related bipolar disorder used Ensembl VEP, alongside other annotation services, for functional annotation of significantly associated variants [7]. VEP sits outside the BioMart versus Expression Atlas choice, but knowing that the Ensembl ecosystem covers variant annotation too prevents wasted effort looking for variant consequence prediction in the wrong place. ## Choosing Between Them: The Criteria That Decide Real Choices ![Decision path asking whether the deliverable is an annotation table or an expression claim, leading to BioMart or Expression Atlas.](/article-images/illustrations/ensembl-biomart-vs-expression-atlas-tool-triage.webp) With both tools defined, the comparison comes down to five practical criteria. ### Data type and question type This is the deciding criterion, and it resolves the ensembl biomart or expression atlas question in almost every case. BioMart returns facts stored in Ensembl's annotation databases. Expression Atlas returns expression evidence derived from reprocessed experiments. If your question begins with "what is," "which genes have," "how many," or "give me the sequences for," it is a BioMart question. If your question begins with "where is," "when is," "does it change under," or "which tissues show," it is an Expression Atlas question. ### Scope of species and divisions BioMart, through the main Ensembl site and Ensembl Genomes, covers every species Ensembl annotates, including the non-vertebrate divisions. Expression Atlas coverage depends on which public experiments have been curated and reprocessed, which is broad for major model organisms and disease contexts but selective overall. For an unusual species, BioMart annotation may exist where no Expression Atlas experiment does. In that situation you may still find expression data for your species elsewhere, but you will need to process it yourself, as researchers did in the chicken GC-content study, which downloaded protein-coding, mRNA, and 5' UTR sequences from Ensembl and paired them with expression data from earlier work to compute expression level, expression breadth, and maximum expression level [8]. ### Data provenance and comparability Expression Atlas values are comparable across its experiments because a single pipeline processes them. BioMart values are authoritative for annotation because they come directly from the Ensembl annotation pipelines. Neither resource substitutes for your own differential expression analysis on your own data. If you need custom contrasts, custom covariates, or single-sample-level control, you run your own RNA-seq pipeline, and both web tools become reference points rather than analysis engines. ### Versioning and reproducibility Ensembl data change with each release. Gene models shift, IDs get retired, and ortholog calls update. The heart failure lncRNA study anchored its noncoding RNA identification to Ensembl release 73 on GRCh37, which is exactly the practice you should imitate, naming the release and genome build in your methods [6]. The scale of change across releases is not trivial. Ensembl v109 from 2023 annotated roughly 252,798 distinct RNA isoforms across 62,710 gene bodies in human, with 256 gene bodies carrying 50 or more annotated isoforms and one protein-coding gene, MAPK10, carrying 192 [9]. Annotation of that complexity gets revised continuously, so a BioMart query run against release 109 will not give the same row count as the same query run against a later release. Expression Atlas has the analogous discipline with experiment and pipeline versions, and you should record those the same way. ### Programmatic access and scale Both tools support automation. BioMart does it through biomaRt and REST, Expression Atlas through its documented API. The difference is what scales. BioMart scales to arbitrarily large table exports because that is its design purpose. Expression Atlas API queries scale to gene- and condition-level lookups. Neither is a substitute for the other at scale. ## Worked Scenarios Abstract comparisons fade quickly, so here are five concrete scenarios drawn from the shape of real published analyses. ### Scenario 1: Annotating a gene family in a plant genome You are studying a gene family in maize. Your first step is enumerating family members, confirming domain architecture, and mapping them to chromosomes. Start in Ensembl Plants. Use BioMart to filter the maize genes dataset and retrieve Ensembl gene IDs, symbols, chromosome, start and end coordinates, peptide sequences, and exon-intron structures for candidates. This is precisely the entry step of the published maize study, which used Ensembl Plants to identify 35 Snf2 family members, then went on to characterize their conserved SNF2-N and Helicase-C domains, chromosomal distribution across all ten maize chromosomes, and diverse exon-intron arrangements [3]. Only after that annotation scaffold existed did the authors turn to expression, analyzing tissue-specific patterns and stress-responsive differential expression with their own data and validating by qRT-PCR [3]. Lesson: BioMart builds the gene list. Expression analysis, whether through a curated atlas or your own RNA-seq, comes after. ### Scenario 2: Mapping orthologs between species You have a list of human genes from a differential expression analysis and need their macaque orthologs for a cross-species comparison. This is unambiguously a BioMart job. In biomaRt, connect to the genes mart, select the human dataset, add the macaque homolog attributes, and call `getBM` with your human Ensembl IDs as the filter. The sex-biased brain expression study did exactly this, acquiring human to macaque orthologous genes via biomaRt from the BioMart Ensembl website before running their regulatory and functional analyses on the mapped gene sets [1]. Expression Atlas has no role here, because orthology is annotation, not expression. ### Scenario 3: Checking whether a candidate gene is tissue-specific You have five candidate genes from a proteomics experiment and want to know which tissues normally express them before committing to validation experiments. Go to Expression Atlas first. Search each gene, read the baseline expression summary across tissues, and scan the differential experiments for conditions in which the gene changes. If a gene is expressed strongly and specifically in a tissue relevant to your hypothesis, that is a lead. This kind of check takes minutes and can save weeks. Note the contrast with Scenario 1. There, the researchers needed genome-scale enumeration first. Here, you need biological context for a handful of genes, and a curated expression resource is faster than any BioMart query. ### Scenario 4: Joining genomic features with expression data in an older or unusual study design You want to relate a sequence feature, for example GC content of a transcript region, to expression patterns in a non-model species. The chicken study on GC content and expression is the template. The authors downloaded protein-coding sequences, complete mRNA sequences, and 5' UTR sequences from Ensembl, identified CpG islands using genome browser resources, and paired these with expression measures from prior work, quantified as expression level, expression breadth, and maximum expression level [8]. Their result, that GC content of the 5' UTR correlates significantly and positively with expression measures, required joining an Ensembl annotation download to expression data that did not come from a single unified portal. Modern projects can sometimes shorten this join by pulling expression context from Expression Atlas, but the underlying division of labor is unchanged. Sequence and structure attributes come from Ensembl, expression evidence comes from an expression resource or your own experiments. ### Scenario 5: Building a lncRNA differential expression study on Ensembl gene models You are running RNA-seq on disease and control tissue and want to include long noncoding RNAs properly. First, decide your annotation universe. The osteoarthritis study restricted its analysis to lncRNAs annotated in GENCODE and Ensembl, which made the feature space defensible and reproducible [5]. Retrieve your annotation (GENCODE and Ensembl GTF files, or a BioMart export for specific attributes), align and quantify against it, and cross-reference your transcripts to the named Ensembl release and genome build, as the heart failure study did with release 73 on GRCh37 [6]. Expression Atlas then serves a complementary role after your analysis. You can query your top lncRNA and protein-coding hits against curated expression patterns to prioritize which of 191 differentially expressed candidates, to borrow the osteoarthritis study's number, to validate experimentally [5]. Lesson: in transcriptomics, Ensembl annotation is the reference frame, your pipeline produces the expression values, and Expression Atlas provides cross-study context for prioritization. ## When to Use Ensembl BioMart The clearest use cases, supported by the patterns in the literature above, are these. - Bulk gene attribute extraction. Coordinates, biotypes, symbols, synonyms, and descriptions for thousands of genes in one query. - Sequence retrieval at scale. cDNA, CDS, peptide, or upstream and downstream genomic sequence for a gene list, exported as FASTA. - Orthology mapping. One-to-one and one-to-many orthologs between a chosen species pair, as in the human and macaque brain study [1]. - Gene family enumeration. Filtering a genome's gene set to a family or biotype and pulling full annotation, as in the maize Snf2 study [3]. - Cross-species sequence collection. Gathering homologous protein sequences for alignment and domain analysis, as in the chicken LECT2 study [4]. - ID conversion. Mapping between Ensembl IDs, external gene names, Entrez IDs, UniProt IDs, and array probe identifiers within one consistent annotation snapshot. - Reference scaffolding for transcriptomics. Defining the annotated gene space your count matrix will use, consistent with studies that anchored their lncRNA analyses in Ensembl annotations [5] [6]. A simple self-test. If your desired output has rows of genes and columns of attributes, BioMart is the right shape of tool. ## When to Use Expression Atlas Use Expression Atlas when the deliverable is expression biology rather than a data file. - Baseline tissue and cell-type questions. Where in the body, or in which plant tissue, is a gene normally expressed. - Condition scanning. Which curated experiments show your gene changing under disease, treatment, or environmental perturbation. - Cross-experiment browsing. Because all experiments are reprocessed uniformly, you can compare up/down calls across studies without reconciling their pipelines. - Single-cell context. Which cell types in a curated experiment express your gene. - Hypothesis triage. Ranking a candidate list by expression evidence before committing laboratory resources. Expression Atlas also earns its place as a teaching and triage tool. For a student or clinician who needs to know "is this gene plausibly relevant to this tissue" without running a pipeline, it is the fastest defensible answer available. ## Common Mistakes and Limitations Every experienced analyst has made at least a few of these errors. Learning them here is cheaper than learning them in review. - Using BioMart to look for expression data. BioMart exports annotation. It does not store expression evidence, and searching its attribute lists for expression values wastes time that a visit to Expression Atlas would answer. This single confusion accounts for most of the difference between ensembl biomart and expression atlas support questions. - Using Expression Atlas as an annotation source. Downloading a "gene list" from an expression experiment and treating it as complete genome annotation is a mistake. The list reflects what was measured in that experiment, filtered by the analysis, not what Ensembl annotates. - Ignoring the Ensembl release. Queries run against different releases return different results, because gene models and IDs evolve. Record the release number and genome build in your methods, as published studies do when they name release 73 on GRCh37 or v109 [6]. - Ignoring the attribute release when using biomaRt. Homolog and ortholog attribute names are versioned and can change between releases. Run `listAttributes()` before finalizing a pipeline, and pin your scripts to a named release for reproducibility. - Confusing ID types. Ensembl gene IDs, transcript IDs, protein IDs, and external gene names are different identifiers. Mixing them in a single filter is the most common cause of empty or nonsensical BioMart results. Check the attribute and filter lists before each query. - Assuming expression absence means gene absence. A gene with no baseline evidence in Expression Atlas may still be real. Curated experiments cover selected tissues, conditions, and species. Low or absent expression evidence in a curated resource is not equivalent to annotation absence in Ensembl, and the two claims have different evidentiary weight. - Forgetting that atlas values are reprocessed, not raw. Expression Atlas results are a uniform reanalysis of public data. If your project depends on a specific paper's normalization or statistical model, go to the primary experiment and the authors' processed data rather than treating atlas values as the paper's values. - Overreading up/down calls from a single experiment. A differential call in one curated experiment is one experiment. The osteoarthritis lncRNA study needed 98 patient samples, FDR control, and functional validation by GapmeR knockdown before drawing conclusions about its 191 hits [5]. Curated atlas evidence is for prioritization and context, not proof. - Retrieving sequences without checking biotypes. A BioMart sequence export that silently includes pseudogenes or retired models contaminates downstream analyses. Filter on biotype and verify a sample of results against the genome browser. - Treating cross-species orthologs as equivalence. One-to-many orthologs mean exactly what they say. Downstream analyses that assume a one-to-one mapping, such as simple expression ratio comparisons, need explicit handling of many-to-many cases, a distinction the ortholog-retrieval step in the brain study had to accommodate [1]. - Individual cases need subject-specific judgment. For clinical questions arising from human genomics work, individual findings require appropriately qualified professional interpretation, just as veterinary findings require a veterinarian. ## Frequently Asked Questions ### What is the difference between Ensembl BioMart and Expression Atlas? BioMart is a query engine that exports annotation tables (gene IDs, sequences, coordinates, orthologs) from Ensembl's databases, while Expression Atlas is a curated EMBL-EBI resource of baseline and differential gene expression results reprocessed from public experiments. Choose BioMart for annotation files and Expression Atlas for expression patterns. ### Can I get expression values from Ensembl BioMart? No. BioMart exports annotation attributes such as identifiers, sequences, coordinates, and orthology, not expression measurements. For curated expression evidence, use Expression Atlas, and for expression in your own samples, run your own RNA-seq analysis pipeline. ### When should I use Ensembl BioMart instead of the genome browser? Use the genome browser for one gene at a time and use BioMart when your list grows beyond a handful of genes, because BioMart retrieves the same underlying annotation in bulk with filters and attributes and exports TSV, CSV, or FASTA. Published gene family studies, such as the maize Snf2 analysis built on Ensembl Plants, rely on that bulk capability [3]. ### How do I cite Ensembl data reproducibly? Record the Ensembl release number and genome build in your methods section, for example Ensembl release 73 on GRCh37 as one heart failure transcriptomics study did, and pin any biomaRt pipeline to that release [6]. Annotation content changes with every release, so the release number is part of your results. ### Does Expression Atlas cover my species? Coverage depends on which public experiments EMBL-EBI has curated and reprocessed, and it is broadest for major model organisms and well-studied disease contexts, including plant and single-cell datasets. Check the species list on the Expression Atlas site. If your species is absent, Ensembl or Ensembl Genomes annotation may still exist through BioMart, and you can process expression data from public repositories yourself. ### Can BioMart and Expression Atlas be used together in one project? Yes, and that is the typical pattern. Use BioMart to build the annotated gene list, sequences, and orthologs, then use Expression Atlas to place those genes in tissue and condition context. The sequence-versus-expression division seen in studies like the chicken GC-content analysis, which joined Ensembl sequence downloads to expression measures, illustrates the pairing [8]. ## Sources 1. [Human-specific protein-coding and lncRNA genes cast sex-biased genes in the brain and their relationships with brain diseases.](https://pubmed.ncbi.nlm.nih.gov/39472939/) 2. [Using WormBase ParaSite: An Integrated Platform for Exploring Helminth Genomic Data.](https://pubmed.ncbi.nlm.nih.gov/29761467/) 3. [Exploring the Roles of the Swi2/Snf2 Gene Family in Maize Abiotic Stress Responses.](https://pubmed.ncbi.nlm.nih.gov/39273633/) 4. [Transcriptional regulation of chicken leukocyte cell-derived chemotaxin 2 in response to toll-like receptor 3 stimulation.](https://pubmed.ncbi.nlm.nih.gov/31480179/) 5. [Elucidating Epigenetic Regulation by Identifying Functional cis-Acting Long Noncoding RNAs and Their Targets in Osteoarthritic Articular Cartilage.](https://pubmed.ncbi.nlm.nih.gov/32840049/) 6. [Right ventricular long noncoding RNA expression in human heart failure.](https://pubmed.ncbi.nlm.nih.gov/25992278/) 7. [Association of FKBP5 gene polymorphism with insomnia symptoms in the depressive episodes of stress-related bipolar disorder.](https://pubmed.ncbi.nlm.nih.gov/40939766/) 8. [Impact of GC content on gene expression pattern in chicken.](https://pubmed.ncbi.nlm.nih.gov/23557030/) 9. [A bioinformatic survey of RNA isoform diversity and expression across 9 GTEx tissues using long-read sequencing data.](https://pubmed.ncbi.nlm.nih.gov/41345545/)