Ensembl

• Find a geneTurn whatever names a gene into the gene itself. Give a symbol, an Ensembl ID with or without its version, a transcript or protein ID, a former name, or an ID from UniProt, NCBI Gene, HGNC or RefSeq, and get back its Ensembl ID, its official symbol and full name, which chromosome it sits on and where, what kind of gene it is and its main transcript. Turn on transcripts to list every isoform with its protein, its length and whether it is the MANE Select transcript clinical reports use. This is the step to put first: the ID it returns is what every other genomics tool asks for.
• Look up a list of genesBuild one table of genes from a whole column of them. Hand it the symbols or IDs out of a spreadsheet, an RNA-seq result or an earlier step, up to a thousand at a time and mixed freely, and get a row per gene with its Ensembl ID, symbol, full name, biotype and position. Versioned IDs from GENCODE and TCGA files work as they are. Anything Ensembl cannot place comes back in its own list with the reason, rather than being quietly dropped, so a run over real data says exactly which rows need a human.
• Get a gene, transcript or protein sequenceGet the sequence of a gene, a transcript or a protein. Choose the protein's amino acids, the coding sequence from start codon to stop, the full spliced transcript, or the genomic DNA with its introns and, if asked, a stretch either side for the promoter. A gene gives its main isoform unless every isoform is asked for. Comes back as text and as FASTA ready to paste into an alignment or primer design tool, or as a FASTA file.
• Get the DNA of a regionRead the DNA of any stretch of a genome by its coordinates: the bases around a variant for a probe or a primer, a promoter upstream of a gene, a whole locus. Up to fifty regions at once, each up to ten million bases, written the way Ensembl or a genome browser writes them. Read either strand, and mark repeats in lower case or as N, which is what primer design tools expect. Comes back as text and FASTA, or as a FASTA file.
• Predict variant effectsRead what a list of DNA variants does, with the Variant Effect Predictor that clinical and research labs run their sequencing results through. Give up to two hundred, as rsIDs, HGVS, SPDI or chromosome, position and alleles the way a VCF writes them, mixed freely. Each comes back with the gene it hits, what it does to the protein in words and as HGVS, how severe that is, SIFT, PolyPhen and AlphaMissense predictions, how common it is in gnomAD and 1000 Genomes, and what ClinVar says about it. The summary is read on the MANE Select transcript, the one clinical reports use. Anything it cannot read comes back in its own list with the reason, such as a reference base that shows the variant is from the other build.
• Get a variantRead everything Ensembl holds on one known variant: where it is, its alleles and which is ancestral, what it does, how common it is in each population of gnomAD and 1000 Genomes, ClinVar's verdict, and the diseases and traits studies have tied it to, curated sources first and then GWAS hits by strength. Give an rsID, or the variant in any notation and it is looked up under the rsID it is catalogued as.
• Translate a variant between notationsWrite each variant every way it is written elsewhere, so records from different sources can be matched up: its rsID, genomic HGVS, SPDI, VCF style, and HGVS on every transcript and protein, Ensembl's and RefSeq's side by side. Give up to two hundred as rsIDs, HGVS, SPDI or chromosome, position and alleles, mixed freely. A variant with two alternate alleles comes back as two rows, one per allele.
• Find the same gene in other speciesFind a gene's counterpart in mouse, zebrafish, fly or any of the hundreds of other species Ensembl compares, which is the step between a human disease gene and the model organism to study it in. Each comes back with its ID and symbol, whether it is a clean one to one match or one of several copies, how much of the protein is identical, and the last ancestor the two species shared. Ask for paralogues instead to find the gene's relatives within its own species.
• List what is in a regionList what sits in a stretch of genome: the genes and their transcripts, the known variants with what they do and what ClinVar says, structural variants, regulatory features and the chromosome band. Give a region, or a gene to list what lies in and around it. This answers what else is near a GWAS hit, which genes a deletion takes out, and which ClinVar variants fall in an exon. Each kind comes back in its own list, with a count.
• Convert coordinates between genome buildsMove positions and regions from one build of a genome to another, most often from GRCh37 (hg19), which older pipelines and many clinical reports still use, to the current GRCh38, or back. Up to fifty at a time. A region that the two builds assemble differently can come back in pieces, and says so; one in sequence the other build does not have comes back in its own list rather than at a wrong position.
• Find a protein or transcript position in the genomeFind where a position along a protein or a transcript lies on the chromosome: which three bases code amino acid 600 of BRAF, or where a domain from residue 100 to 300 sits in the genome. A codon that straddles an intron comes back in two pieces. Protein numbering belongs to one isoform, so a gene named by symbol is read on its main transcript, the one clinical numbering uses.
• List a gene's diseases and traitsList the diseases and traits a gene is linked to, and who says so: OMIM, Orphanet, GenCC and the Gene2Phenotype panels for inherited disease, each with the ontology terms that tie the disease name to other databases. Turn on tumour types to add the cancers the Cancer Gene Census finds it mutated in. Curated disease sources come first.
• Find the genes and variants behind a traitStart from a disease or trait and find what studies have tied to it: the GWAS catalog's hits with their p-values and effect sizes, ClinVar's pathogenic variants, and the genes named. Give the trait in words, such as type 2 diabetes, or as an ontology term (EFO, HP, MONDO, Orphanet) to be exact. The strongest results come first, with a tally of the genes named most often.
• Get a gene's IDs in other databasesGet the same gene's IDs everywhere else, which is how records from different tools are joined: HGNC, NCBI Gene, UniProt, RefSeq transcripts and proteins, OMIM, CCDS and the UCSC browser, with the gene's former names, its Reactome pathways, its Gene Ontology terms and the protein structures solved for it. The IDs people join on most are also given on their own, ready to use.
• List a protein's domains and featuresMap what is along a protein: its domains from Pfam, SMART, PROSITE, CDD and the other InterPro member databases with where each starts and ends, grouped by InterPro entry, and its coiled coils, disordered stretches, signal peptide and membrane helices. Turn on structures to add the solved PDB structures covering it. This is the map for reading whether a variant lands inside a functional domain.
• Find variants in linkage disequilibriumFind the variants inherited together with one variant in a population, which is the first step after a GWAS hit: the variant a study reports is often only a marker for a neighbour that does the damage. Each linked variant comes back with how tightly it travels with the first (r squared and D prime), where it is, what it does and ClinVar's verdict, closest first. Name a second variant instead to measure the link between the two. Computed on the 1000 Genomes populations.
• Check Ensembl IDs are currentCheck a list of Ensembl IDs against the current release: which are still current, which have moved on to a newer version, and which have been retired, with the release they were last seen in and, where Ensembl knows one, what replaced them. Up to a thousand at a time, versions and all. Run it over IDs from an old spreadsheet or paper before trusting them.

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