Key words

1 Introduction

Plants play a central role in the ecology and economy of our planet and are essential to our food security. As the world population increased by 145% in the last 60 years, the yields of cereals increased even more, while not needing much more land [1]. This has been possible as a result of improved agricultural practices and crops. Currently, breeding programs take advantage of inexpensive genomic and phenotypic data. The next steps towards what is being called Breeding 4.0 [2] include adapting crops to changing environments and broadening the diversity pool to compensate for the losses occurred during domestication. For this reason wild relatives of crops are being sequenced increasingly and added to pre-breeding programs [3]. In addition, natural plant populations and model plants are being studied to understand their ecology and the genetic basis of their adaptation mechanisms, which can then be applied to in crops. In this context, genomics is a foundation of plant sciences, as standard approaches such as marker-assisted breeding, QTL analysis, and genome-wide association studies, as well as genomic selection, induced variation experiments, and genome editing, all depend on genomic technologies and databases. These tools are accelerating breeding and helping to untangle complex polyploid genomes, such as that of bread wheat [4].

Ensembl Plants (http://plants.ensembl.org) is the Ensembl portal for plants and red algae [5] and provides a consistent set of interfaces to genomic data, including reference genome sequences, gene and transcript models, genetic variation, gene expression, markers, and comparative genomics. There are up to four releases per year. At the time of writing, the latest release of Ensembl Plants is version 47 (April 2020), which corresponds to Ensembl version 100. This release comprises 79 genomes, containing several cultivars and ecotypes for some species. Ensembl Plants is developed with our long-term partners Gramene [6] and with individual groups that publish plant genomes around the world. This chapter documents how the data at Ensembl Plants can be downloaded in bulk and interrogated programmatically using a variety of approaches. It provides a series of recipes, available as source code at https://github.com/Ensembl/plant-scripts, that can be modified to carry out more complex analyses of plant genomes.

2 Materials

2.1 Database Structure and Data Access

Ensembl Plants is implemented primarily as a collection of MySQL relational databases. The overall data structure is modular, with different data (e.g., core annotation, comparative genomics, functional genomics, variation data) modeled by distinct schemas. The core schema is modeled on the central dogma of molecular biology, linking genome sequence to genes, transcripts, and their translations, each of which can be decorated with functional annotation (see Note 1). Much annotation takes the form of cross-references, which are web links to entries in other resources, such as InterPro [7] or Gene Ontology [8], that either represent the primary source of the biological entity or provide additional information. Cross-references describe functional entities such as domains, reactions, and processes. Some also serve as controlled vocabularies for functional annotation.

The databases can be downloaded for local installation or alternatively accessed via a public MySQL server. Local MySQL databases are an efficient alternative to the public MySQL server, particularly if heavy use is anticipated (see Note 2). Programmatic access is supported by two APIs, which allow data discovery and access through an abstraction layer that hides the detailed structure of the underlying data store. One is a Perl API, while the other uses a language-agnostic REST interface [9]. The REST service allows up to 15 requests per second.

In addition to the primary databases, Ensembl Plants also provides access to denormalized data warehouses, constructed using the BioMart tool kit [10]. These are specialized databases that support efficient gene- and variant-centric queries. Finally, a variety of data selections are exported from the databases in common file formats and made available for download via an FTP site.

These resources are summarized in Table 1. Recipes to query each of them are listed in Table 7.

Table 1 Programming interfaces and data sources in Ensembl Plants. The public MySQL server contains databases from the most recent ten releases
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2.2 Overview of Data Content

2.2.1 Genomes and Core Data

Genome assemblies are typically imported from the European Nucleotide Archive (ENA) [11], which is part of the International Nucleotide Sequence Database Collaboration (http://www.insdc.org, INSDC). Gene model annotations are imported from the ENA [11], Phytozome [12], or provided by community members (see Note 3). For instance, the rice annotation was imported from RAP-DB [13]. After import, various computational analyses are performed for each genome. A summary of these is given in Table 2. In addition, specific datasets are imported and analyzed according to the requirements of individual communities. These datasets typically fall into two classes, markers, and variants across genotype panels.

Table 2 Standard computational analyses that are typically run for genomes in Ensembl Plants. The full list of analyses for any species can be obtained with recipe A2
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The genomes currently included in Ensembl Plants are listed in Table 3. A summary of UniProt coverage of proteins encoded by genes within these genomes is given in Table 4 [17]. In all cases, genomes are identified by their Ensembl production name, which is usually binomial but can also include a strain name to distinguish particular cultivars or ecotypes, such as malus_domestica_golden. Details of other datasets incorporated can be found through the homepage for each species (see Note 3).

Table 3 Genomes available in release 47 (April 2020) of Ensembl Plants. The chr column indicates chromosome-level assemblies. The base count of the genome golden path is given in Mbp. This table was produced with recipe R2
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Table 4 Protein-coding genes annotated in release 47 (April 2020) of Ensembl Plants. The last two columns indicate how many genes encode proteins computationally predicted (TrEMBL) and manually curated (SwissProt) in UniProtKB. This table was produced with recipe F10. Mappings between Ensembl and UniProt proteins can be obtained with recipe F6
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2.2.2 Variation Data

The variation schema can store genetic variants observed in populations or germplasm collections, alleles, and frequencies, alongside sample genotype data. Supported variant types include single nucleotide polymorphisms, indels, and structural variants. The functional consequence of variants on genes is predicted with the Ensembl Variant Effect Predictor (VEP) [14]. Linkage disequilibrium data and statistical associations with phenotypes are available for selected species. The variation datasets of release 47 of Ensembl Plants are described in Table 5. The Ensembl VEP is also a command line tool that can be used to efficiently annotate variants and we provide recipes for it as well (see Table 7).

Table 5 Variation datasets available in release 47 (April 2020) of Ensembl Plants. The list can also be browsed interactively at http://plants.ensembl.org/species.html. This table was produced with recipe R9. The corresponding VCF files can be downloaded with recipe F7. Recipe F8 can be used to get the Ensembl VEP cache files in order to annotate variant consequences with recipes V2 and V3
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2.2.3 Comparative Genomics Data

The Ensembl Gene Tree pipeline is used to calculate evolutionary relationships among members of protein families (Table 2). For each gene, the translation of the canonical transcript is selected (see Note 4). Briefly, this pipeline first finds clusters of similar proteins and then, for each cluster, attempts to reconcile the relationship between the sequences with the known species cladogram (Fig. 1), derived from the NCBI Taxonomy database [42]. The analysis also contains a few non-plant outgroups. The TreeBeST software (https://github.com/Ensembl/treebest) is used to construct a consensus tree, which allows the identification of orthologues and paralogues. As polyploid genomes are split into components, homoeologous genes are effectively defined as orthologues among subgenomes. A number of plant genomes are also included in a pan-taxonomic gene tree, containing a representative selection of sequenced genomes from all domains of life. Recipe R2 can be used to check which comparative analyses have been run for a particular species. This information is also displayed in the table at http://plants.ensembl.org/species.html.

Fig. 1
figure 1

Species cladogram of release 47 (April 2020) of Ensembl Plants. Genomes of polyploid species are decomposed into genomic components. This topology is used in the comparative genomic analyses to derive orthologous and paralogous genes. This tree was produced with the Newick file obtained with recipe F12 and visualized with iToL [41]

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Other comparative analyses available in Ensembl Plants are pairwise whole-genome alignments and synteny (see Tables 2 and 6).

Table 6 Number of pairwise whole-genome alignments and synteny analyses in release 47 (April 2020) of Ensembl Plants. Pairwise alignments are computed with LastZ [43]. Two multiple alignments are also available for Oryza species. Data obtained with recipe S6
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2.2.4 Baseline Expression Data

Baseline gene expression reports are available as “Gene expression” on the website for selected species. An example for barley is shown at http://plants.ensembl.org/Hordeum_vulgare/Gene/ExpressionAtlas?g=HORVU5Hr1G095630;r=chr5H:599085656-599133086. The underlying curated expression data, produced by Expression Atlas [44], can be browsed and downloaded via the expression widget.

2.2.5 RNA-seq Tracks

RNA-seq datasets from the public INSDC archives are mapped to genome assemblies in Ensembl Plants in every release. They are handled as ENA studies and for each of them CRAM files are created with the RNA-Seq-er pipeline (https://www.ebi.ac.uk/fg/rnaseq/api) [45] and published at ftp://ftp.ensemblgenomes.org/pub/misc_data/Track_Hubs. Each study contains a separate folder for each assembly that was used for mapping. These tracks can be interactively displayed in the browser, but can be of interest for high-throughput studies as well. For instance, study SRP133995 was mapped to tomato assembly SL3.0 and the tracksDb.txt file therein indicates the full path to the relevant CRAM file next to its metadata. CRAM files for a selected assembly can be discovered with recipe C1; note that the assembly name corresponds to column “assembly_default” in recipe R2. As of May 2020 there were 89,355 CRAM files available.

3 Methods

This section describes some of the recipes listed in Table 7 in detail so that the reader can execute or modify any of them. Software dependencies required by these recipes are listed in https://github.com/Ensembl/plant-scripts/blob/master/README.md.

Table 7 Programming recipes to analyze data in Ensembl Plants, including perl API (A), R BiomaRt (B), FTP (F), SQL (S), REST (R), and Ensembl VEP (V) examples. These recipes and their software dependencies, together with a few more scripts for phylogenomic analyses, are updated at https://github.com/Ensembl/plant-scripts
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The different approaches are complementary. While the native Perl API is the most powerful and used extensively by Ensembl developers, it also requires some Perl knowledge and the installation of several repositories. Similarly, the Biomart and MySQL examples require knowledge of R and SQL, respectively. However, the REST endpoints can be interrogated with any programming language; however, only a defined set of queries are currently supported. The FTP recipes allow efficient bulk downloads, but with no customization. The source code for all recipes can be found at https://github.com/Ensembl/plant-scripts.

3.1 Clone the GitHub Repository and Install Dependencies

The following steps explain how to obtain a local copy of the recipes and how to test them on Linux/MacOS operating systems (OS).

  1. 1.

    Open a terminal and check whether git is installed by typing: git --version.

  2. 2.

    If required install git if using the appropriate software manager for your OS.

  3. 3.

    Clone the repository: git clone https://github.com/Ensembl/plant-scripts.git.

  4. 4.

    Navigate to the scripts directory: cd plant-scripts.

  5. 5.

    Optionally test the scripts: perl demo_test.t.

3.2 Perl API Recipes

The Ensembl Perl API enables access to all types of data from Ensembl Plants (genes, variation, comparative genomics, regulation, etc.) and it is documented extensively (see Note 5). It allows complex queries to be executed without the construction of any explicit SQL queries. The repository contains eight Perl API recipes, of which three are described here (A1, A4, and A8).

3.2.1 Get a BED File with Repeats on Chromosome 4

  1. 1.

    Load the Registry object with details of genomes available from the public Ensembl Genomes servers (recipe A1):

    Use Bio::EnsEMBL::Registry; Bio::EnsEMBL::Registry->load_registry_from_db( -USER => 'anonymous', -HOST => 'mysql-eg-publicsql.ebi.ac.uk', -PORT => '4157', );

  2. 2.

    Set species and chromosome of interest and print BED file with repeats (recipe A4). Ensembl uses 1-based inclusive coordinates internally:

    my $species = 'arabidopsis_thaliana'; my $chrname = 'chr4'; my $slice_adaptor = Bio::EnsEMBL::Registry-> get_adaptor($species, 'core', 'Slice'); my $slice = $slice_adaptor-> fetch_by_region( 'toplevel', $chrname ); my @repeats = @{ $slice->get_all_RepeatFeatures() }; foreach my $repeat (@repeats) { printf("%s\t%d\t%d\t%s\t%s\t%s\n", $chrname, $repeat->start()-1, $repeat->end(), $repeat->analysis()->logic_name(), $repeat->repeat_consensus()->repeat_class(), $repeat->repeat_consensus()->repeat_type() ); }

3.2.2 Get Markers Mapped on Chromosome 1D of Bread Wheat

Only a few plants have markers loaded. Recipe A8 retrieves wheat KASP markers, with coordinates returned in BED format:

$species = 'triticum_aestivum'; $chrname = '1D'; $slice_adaptor = Bio::EnsEMBL::Registry-> get_adaptor( $species, 'Core', 'Slice' ); $slice = $slice_adaptor-> fetch_by_region( 'chromosome', $chrname ); foreach my $mf (@{ $slice->get_all_MarkerFeatures() }) { my $marker = $mf->marker(); printf("%s\t%d\t%d\t%s\t%s\t%s\t%d\n", $mf->seq_region_name(), $mf->start()-1, $mf->end(), $mf->display_id(), $marker->left_primer(), $marker->right_primer(), $marker->max_primer_dist() ); }

3.3 R Biomart Recipes

The BioMart databases can be queried in many ways (see Note 6). There are five recipes in the repository written in the R language. They all use the BioConductor package BiomaRt [46], which can be installed as follows:

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("biomaRt")

This example corresponds to recipe R4, which queries sunflower genes to obtain annotated Pfam domains. Dataset names are abbreviations of Ensembl production names. See recipe R5 for an example querying BioMart variation databases:

EPgenes = useMart( biomart="plants_mart", host="plants.ensembl.org", dataset="hannuus_eg_gene") pfam = getBM( attributes=c("ensembl_gene_id", "pfam"), mart=EPgenes)

3.4 FTP Recipes

There are 12 recipes in the repository that query the Ensembl Genomes FTP server. They use shell variables and the wget program to download files. The recipes refer to the Ensembl release and the Ensembl Plants release as RELEASE and EGRELEASE, respectively. Recipe F5 involves a prewritten BioMart query.

3.4.1 Download Soft-Masked Genomic Sequences

Soft-masked sequences are FASTA files with all annotated repeated elements in lower case. Using recipe F4 they can be downloaded for a chosen species and release as follows:

SERVER=ftp://ftp.ensemblgenomes.org/pub DIV=plants EGRELEASE=47 SPECIES=Brachypodium_distachyon FASTASM="${SPECIES}*.dna_sm.toplevel.fa.gz" URL="${SERVER}/release-${EGRELEASE}/${DIV}/fasta/${SPECIES,,}/dna/${FASTASM}" wget -c "$URL"

3.4.2 Download All Homologies in a Single TSV File

Recipe F9 downloads a large file (several GB) with all homologies of a release in TSV format. Sequence identifiers correspond to canonical transcripts (see Note 4):

TSVFILE="Compara.${RELEASE}.protein_default.homologies.tsv.gz" URL="${SERVER}/${DIV}/release-${EGRELEASE}/tsv/ensembl-compara/homologies/${TSVFILE}" wget -c "$URL"

This file can be parsed in the command line in order to extract homologies (see Note 7):

zcat "$TSVFILE" | grep triticum_aestivum | grep oryza_sativa | grep ortholog

Homologies of each species can be retrieved from a smaller, specific file:

TSVFILE="Compara.${RELEASE}.protein_default.homologies.tsv.gz" SPECIES=Triticum_aestivum URL="${SERVER}/${DIV}/release-${EGRELEASE}/tsv/ensembl-compara/homologies/${SPECIES,,}${TSVFILE}" wget -c "$URL" zcat "$TSVFILE" | grep oryza_sativa | grep ortholog

Homologies can also be downloaded in OrthoXML format [47], which renders a smaller file but requires a more complex parser.

3.5 MySQL Recipes

Direct access to the public MySQL server requires knowledge of the schemas (see Notes 1 and 8). While this approach supports complex queries with high-performance, the schemas may change in a new release and thus some queries might stop working. For this reason, API access is recommended. Three recipes are shown here, they all require the mysql-client to be installed.

3.5.1 Count Protein-Coding Genes of a Particular Species

This is recipe S2. The source code works out the current release number, but it can also be set manually as in this example:

SERVER=mysql-eg-publicsql.ebi.ac.uk USER=anonymous PORT=4157 EGRELEASE=47 RELEASE=$((EGRELEASE + 53)) SPECIES=arabidopsis_thaliana SPECIESCORE=$(mysql --host $SERVER --user $USER --port $PORT \ -e "show databases" | grep \ "${SPECIES}_core_${EGRELEASE}_${RELEASE}") mysql --host $SERVER --user $USER --port $PORT \ $SPECIESCORE -e "SELECT COUNT(*) FROM gene \ WHERE biotype='protein_coding'"

3.5.2 Get stable_ids of Transcripts Used in Compara Analyses

Recipe S3 gets a list of identifiers of all transcript used in the comparative genomics gene tree analysis (see Note 4):

SERVER=mysql-eg-publicsql.ebi.ac.uk USER=anonymous PORT=4157 EGRELEASE=47 RELEASE=$((EGRELEASE + 53)) SPECIES=arabidopsis_thaliana mysql --host $SERVER --user $USER --port $PORT \ "ensembl_compara_plants_${EGRELEASE}_${RELEASE}" \ -e "SELECT sm.stable_id \ FROM seq_member sm, gene_member gm, genome_db gdb \ WHERE sm.seq_member_id = gm.canonical_member_id \ AND sm.genome_db_id = gdb.genome_db_id \ AND gdb.name = '$SPECIES'"

See recipe F3 to obtain the corresponding sequences.

3.5.3 Get Variants Significantly Associated with Phenotypes

Recipe S4 queries several tables of the variation schema (see Note 8):

SPECIESVAR=$(mysql --host $SERVER --user $USER --port $PORT \ -e "show databases" | \ grep "${SPECIES}_variation_${EGRELEASE}_${RELEASE}") mysql --host $SERVER --user $USER --port $PORT \ $SPECIESVAR<<SQL SELECT f.object_id, s.name, f.seq_region_start, f.seq_region_end, p.description FROM phenotype p JOIN phenotype_feature f ON p.phenotype_id = f.phenotype_id JOIN seq_region s ON f.seq_region_id = s.name WHERE f.type = 'Variation' AND f.is_significant=1 SQL

3.6 REST Recipes

The following recipes, written in Python, can also be found in R and Perl languages in the repository. They communicate with the Ensembl REST service at https://rest.ensembl.org (see Note 9) using the functions get_json and get_json_post, defined in file exampleREST.py.

3.6.1 Find Features Overlapping a Genomic Region

Recipe R3 queries the endpoint overlap/region and returns all features overlapping a selected genomic region:

def get_overlapping_features(species,region): overlap_url = ("/overlap/region/" + species + "/" + region) # repeat or variation could have been used instead of gene ext = (overlap_url + "?feature=gene;content-type=application/json") overlap_data = get_json(ext) for overlap_feat in overlap_data: print("%s\t%s\t%s" % (overlap_feat['id'], overlap_feat['start'], overlap_feat['end'])) species = 'triticum_aestivum'; region = '3D:379400000-379540000'; get_overlapping_features(species,region)

3.6.2 Check Consequences of SNPs Within CDS Sequences

Recipe R8 queries two endpoints (map/cds/ and info/vep/:species/region). The first one translates CDS to genomic coordinates, the second one retrieves the predicted consequences of the SNP in the coding sequence. This recipe can be used to annotate genomic variants in a given gene across germplasm panels, as done in [48]:

def check_snp_consequences(species,transcript_id,SNPCDScoord,SNPbase): # convert CDS coords to genomic coords ext = ("/map/cds/" + transcript_id + "/" + SNPCDScoord + ".." + SNPCDScoord + "?content-type=application/json;species=" + species) map_cds = get_json(ext) if map_cds['mappings'][0]['seq_region_name']: mapping = map_cds['mappings'][0] # fetch VEP consequences for this region SNPgenome_coord = ( mapping['seq_region_name'] + ':' + str(mapping['start']) + '-' + str(mapping['end']) ) ext = ("/vep/"+ species + "/region/" + SNPgenome_coord + "/" + SNPbase + "?content-type=application/json") conseq = get_json(ext) # Print all the relevant info for the given variant if conseq[0]['allele_string']: for tcons in conseq[0]['transcript_consequences']: #... some lines omitted, check exampleREST.py values = (transcript_id, SNPCDScoord, conseq[0]['allele_string'], tcons['biotype'], tcons['codons'], tcons['amino_acids'], tcons['protein_start'], tcons['impact'], tcons['sift_prediction'], tcons['sift_score']) for val in values: print (val, end="\t") print() species = 'triticum_aestivum' transcript_id = 'TraesCS4B02G042700.1' SNPCDScoord = '812' SNPbase = 'T' check_snp_consequences(species,transcript_id,SNPCDScoord,SNPbase)

3.7 Annotate the Effect of Variants with the Ensembl Variant Effect Predictor

The Ensembl VEP tool can be used to predict the effect of variants on genes, transcripts, and protein sequences (see Note 10). As mentioned in Table 2, this analysis is run for all genomic variants imported into Ensembl (see Table 5). While the Ensembl VEP is available through a web interface, the advantage of a local installation is that it can be used to analyze variation sets of any species, including species that are not in Ensembl Plants. If variants are mapped to a reference genome supported in Ensembl Plants, using a cache file increases performance. However, as shown in recipe V4, it is possible to use other reference FASTA files together with the corresponding GFF/GTF annotation files. The next steps summarize how the software is installed and used following recipes F8, V1, V2, and V3.

  1. 1.

    Clone the repository: git clone https://github.com/Ensembl/ensembl-vep.git.

  2. 2.

    Navigate to the Ensembl VEP directory: cd ensembl-vep.

  3. 3.

    Install Ensembl VEP: perl INSTALL.pl.

  4. 4.

    Download cache file with recipe F8

    SPECIES=arabidopsis_thaliana VEPCACHE="${SPECIES,,}*.tar.gz*" URL="${SERVER}/${DIV}/release-${EGRELEASE}/variation/vep/${VEPCACHE}" wget -c "$URL"

  5. 5.

    Unpack downloaded cache file and check SIFT support:

    tar xfz $VEPCACHE grep sift "${SPECIES}/${EGRELEASE}_*/info.txt"

  6. 6.

    Predict effect of variants, see Note 11:

    EGRELEASE=47 VCFILE=ensembl-vep/examples/arabidopsis_thaliana.TAIR10.vcf VEPOPTIONS=( --genomes # Ensembl Genomes, for Plants --species $SPECIES --cache # use local cache file, opposed to --database --dir_cache ./ # path of unpacked cache $SPECIES folder --cache_version $EGRELEASE --input_file $VCFILE --output_file ${VCFILE}.vep --check_existing # co-located known variants --distance 5000 # max dist between variant and transcript --biotype # show biotype of neighbor transcript ) ensembl-vep/vep "${VEPOPTIONS[@]"

3.8 Querying Plant Pangenomes

Upcoming Ensembl Plants releases will have an increasing number of species with multiple cultivars or ecotypes as additional assemblies are added in collaboration with the relevant communities. On the website these cultivars can be browsed from the appropriate reference genome page such as http://plants.ensembl.org/Triticum_aestivum/Info/Strains?db=core (see Note 12). Starting with several UK cultivars in release 48 (August 2020), Ensembl will host all cultivars of the first assembled wheat pangenome [49] from release 50 planned for early 2021 (see example Fig. 2). Note that related noncultivated species are often included in the pangenomes of crops. For example, Ensembl Plants hosts 11 Oryza species plus the outgroup plant Leersia perrieri. Both types of genome sets can be considered pangenomes.

Fig. 2
figure 2

The RFL gene (TraesCS1B02G038500) lifted over from the reference landrace Chinese Spring to three wheat cultivars (CDC Landmark, Julius and Jagger). The genes are displayed in the Ensembl Plants genome browser. While in the first cultivar there are three annotated transcript isoforms including one with two exons, the others have a single transcript with one exon. Furthermore, the locus is annotated as a pseudogene in Julius

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Currently, some pangenomes in Ensembl can be interrogated using gene trees and whole-genome alignments (WGAs; see Tables 2 and 6). For example, recipe A9 can be used to retrieve syntenic orthologous genes in rice or Brassicaceae species. These analyses will be available for wheat as well once de novo gene annotation and WGAs are produced.

3.9 Getting Help

Documentation for Ensembl Plants, including FAQs, tutorials, and detailed information about the project, datasets, and pipelines that we run can be found under the “Documentation” and “Website help” links at the top of every page. Detailed information for each species can be found on the species homepage. The EMBL-EBI train online website has several free courses on Ensembl, including the recently updated “Ensembl Genomes (non-chordates): Quick tour” (https://www.ebi.ac.uk/training/online/course/ensembl-genomes-non-chordates-quick-tour) and “Ensembl REST API” courses (https://www.ebi.ac.uk/training-beta/online/courses/ensembl-rest-api). Any data problems are reported on our blog http://www.ensembl.info/known-bugs. If the available documentation cannot answer your question, a helpdesk is provided (mail helpdesk@ensemblgenomes.org with your query).

4 Notes

  1. 1.

    The core schema is fully described at https://www.ensembl.org/info/docs/api/core/core_schema.html. There are similar documents for variation, comparative genomics, and regulation schemas: https://www.ensembl.org/info/docs/api/variation/variation_schema.html

    https://www.ensembl.org/info/docs/api/compara/compara_schema.html

    https://www.ensembl.org/info/docs/api/funcgen/funcgen_schema.html

    The schema for the metadata database can be found at https://github.com/Ensembl/ensembl-metadata.

  2. 2.

    Instructions to set up a local Ensembl database are provided at http://plants.ensembl.org/info/docs/webcode/mirror/install/ensembl-data.html.

  3. 3.

    Check the annotation page for each species in Ensembl Plants. For Arabidopsis thaliana, this is http://plants.ensembl.org/Arabidopsis_thaliana/Info/Annotation/#genebuild.

  4. 4.

    Gene trees use canonical transcripts, defined at http://plants.ensembl.org/info/website/glossary.html. In plant species, the canonical transcript of a protein-coding gene is the transcript with the longest translation with no stop codons. This does not necessarily reflect the most biologically relevant transcript of a gene. The script https://github.com/Ensembl/plant_tools/blob/master/phylogenomics/ens_sequences.pl can be used to obtain sequences of canonical transcripts in FASTA format.

  5. 5.

    Check http://plants.ensembl.org/info/docs/Doxygen. See also debugging instructions and tutorials at http://plants.ensembl.org/info/docs/api.

  6. 6.

    See http://plants.ensembl.org/info/data/biomart.

  7. 7.

    See http://plants.ensembl.org/info/genome/compara/homology_method.html for the definitions of the different homology types.

  8. 8.

    The variation schema is described at http://plants.ensembl.org/info/docs/api/variation/variation_schema.html.

  9. 9.

    Training material to learn more about the Ensembl REST interface can be found at https://www.ebi.ac.uk/training/online/course/ensembl-rest-api and https://mybinder.org/v2/gh/Ensembl/rest-api-jupyter-course/master. The different endpoints are documented at https://rest.ensembl.org/documentation.

  10. 10.

    Ensembl VEP functionality can be extended to utilize additional data or run additional analyses using plugins, see https://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html.

  11. 11.

    The full list of options of Ensembl VEP is described at http://www.ensembl.org/info/docs/tools/vep/script/vep_options.html, there are examples at http://www.ensembl.org/info/docs/tools/vep/script/vep_example.html.

  12. 12.

    Reference genomes have binomial production names when possible, such as oryza_sativa (rice) or triticum_aestivum (bread wheat). Additional cultivars or ecotypes have longer trinomial names such as oryza_sativa_indica or triticum_aestivum_cadenza. Following this convention, theobroma_cacao_matina and panicum_hallii_hal2 will be renamed to theobroma_cacao and panicum_hallii by release 50.