The Ensembl gene annotation system

By Bronwen L. Aken, S. Ayling, Daniel Barrell, Laura Clarke, V. Curwen, S. Fairley, J. F. Banet, Konstantinos Billis, C. G. Girón, Thibaut Hourlier and 14 more. Database J. Biol. Databases Curation, 2016

1,134 citations recorded by the source corpus.

The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail. Database URL: http://www.ensembl.org/index.html

Subfields: Molecular Biology

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Citations in the corpus

GENCODE 2021 (2020, methodology): “The Ensembl/GENCODE geneset is a merge of the manual gene annotation created by the Ensembl-HAVANA team (methods and validation described in 6–8) and the automated annotation produced by the Ensembl Genebuild team (9,10).”
Ensembl 2019 (2018, methodology): “We import primary data, such as assemblies and discovered variants, and then annotate genes and transcripts (4), variants (5), regulatory regions (6) and comparative genomics features (7).”
GENCODE reference annotation for the human and mouse genomes (2018, methodology): “For other classes of sncRNA, including small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs) and small Cajal bodyspecific RNAs (scaRNAs), we use a homology-based, computational pipeline (24), which first compares sequences of known RNA families in Rfam (25) to the genome using BLAST (26).”
The stress granule transcriptome reveals principles of mRNA accumulation in stress granules (2017, neutral): “All length data (5 0 UTR, ORF, 3 0 UTR and total) was acquired using Ensemble’s Biomart tool (Aken et al., 2016; Kinsella et al., 2011).”
InterPro in 2019: improving coverage, classification and access to protein sequence annotations (2018, methodology): “InterPro data are also used by a variety of other annotation pipelines, including Ensembl (21), Ensembl Genomes (22), PDBe (23), BLAST2GO (24), Genome Properties (https://www.ebi.ac. uk/interpro/genomeproperties/), PhytoPath (25), MEGAN (26) and MGnify (previously known as EBI Metagenomics) ( 27).”
Ensembl 2020 (2019, methodology): “Our analysis methods are capable of annotating genes and transcripts (5), performing comparative genomics (6), integrating variation data sets from diverse resources (7) and annotating regulatory activity (8) to create a comprehensive and consistent baseline of reference annotation.”
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines (2018, positive): “Additional annotations were added from COSMIC(Forbes et al., 2015), dbGaP(Sherry et al., 2001), ExAC(Lek et al., 2016), and Ensembl(Aken et al., 2016) using Variant Effect Predictor (VEP)(McLaren et al., 2016) and other custom built annotation tools including the normal depth of coverage ﬁlter and s”
Ensembl 2021 (2020, background): “uk/eva), and add value via detailed and comprehensive annotation of transcript structures (3), genomic variants (4) and regulatory regions (5).”
CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores (2021, methodology): “For MMSplice, the exon-intron boundaries required were obtained from Ensembl [44] v95 transcript models.”
The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics (2024, positive): “We considered a total of 90,000 sequences annotated by Ensembl [30] as: “5’ UTR”, “3’ UTR”, “exon”, “intron”, “enhancer”, “promoter”, “CTCF binding site”, “open chromatin”, and “transcription factor binding sites”.”
Ensembl 2017 (2016, methodology): “The mouse GENCODE annotation has been updated several times this year and combines the standard Ensembl gene annotation approach (6) with manual annotation directly on the reference assembly.”
GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins (2020, neutral): “Therefore, ab initio gene finders [e.g. GENSCAN (13), Gen-eMark.hmm (14), AUGUSTUS (15) or GeneID (16)] have been a necessary part of genome annotation tools and pipelines [e.g. GNOMON (17), PASA (18) and Ensembl (19)].”
iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data (2018, methodology): “To enable gene ID conversion, we downloaded all available gene ID mappings for 220 species from Ensembl [26, 27] (Additional file 1: Table S1), including 98 from Ensembl (vertebrates, release 91), 53 from Ensembl Plants (release 37) [28], and 69 from Ensembl Metazoa (release 37).”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “Gene-level quantification consists of assigning fragments (reads or read pairs) to “genes”, where gene is often taken to represent the amalgamation of all transcripts produced from a specific strand at a specific locus [65], which typically share some exons or parts of exons.”
Ensembl core software resources: storage and programmatic access for DNA sequence and genome annotation (2016, methodology): “The Ensembl gene annotation system, described in detail by Aken et al. (7), uses stable identifiers to uniquely identify each feature across releases.”
RNAcentral: a hub of information for non-coding RNA sequences (2018, positive): “Ensembl provides automated RNA gene annotations for over 62 vertebrate genomes predicted based on Ensembl ncRNA and lincRNA pipeline (20), while GENCODE provides high quality manual annotations for large lncRNAs found in human and mouse genomes.”