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# Querying Ensembl

Section author: Gavin Huttley, Hua Ying

We begin this documentation with a note on dependencies, performance and code status. Ensembl makes their data available via MySQL servers, so the `cogent.db.ensembl` module has additional dependencies of MySQL-python and SQLAlchemy. You can use `easy_install` to install the latter, but the former is more involved. If you experience trouble, please post to the PyCogent help forums. Regarding performance, significant queries to the UK servers from Australia are very slow. The examples in this documentation, for instance, take ~15 minutes to run when pointed at the UK servers. Running against a local installation, however, is ~50 times faster. On status, the `cogent.db.ensembl` module should be considered alpha level code. It has been working well for the developers, but we strongly advise users to check results for a subset of their analyses against the data from the UK Ensembl web site.

The major components of Ensembl are compara and individual genomes. In all cases extracting data requires connecting to MySQL databases on a server and the server may be located remotely or locally. For convenience, the critical objects you’ll need to query a database are provided in the top-level module import, ie immediately under `cogent.db.ensembl`.

## Specifying a Host and Account

So the first step is to specify what host and account are to be used. On my lab’s machines, I have set an environment variable with the username and password for our local installation of the Ensembl MySQL databases, e.g. `ENSEMBL_ACCOUNT="username password"`. So I’ll check for that (since the documentation runs much quicker when this is true) and if it’s absent, we’ll use the UK Ensembl service using the convenience function `get_ensembl_account`. I also define which release of ensembl we’ll use in one place to allow easier updating of this documentation.

    >>> import os
    >>> Release = 52
    >>> from cogent.db.ensembl import HostAccount, get_ensembl_account
    >>> if 'ENSEMBL_ACCOUNT' in os.environ:
    ...     username, password = os.environ['ENSEMBL_ACCOUNT'].split()
    ...     account = HostAccount('127.0.0.1', username, password)
    ... else:
    ...     account = get_ensembl_account(release=Release)

To get the account at Ensembl appropriate for a different release we just specify the release number. Note that the ensembl account details differ by release.

    >>> ensembl_account_46 = get_ensembl_account(release=46)
    >>> assert ensembl_account_46.port == 3306
    >>> ensembl_account_48 = get_ensembl_account(release=48)
    >>> assert ensembl_account_48.port == 5306

At the time of writing, Ensembl serves all releases prior to 48 on port 3306 and port 5306 otherwise.

If you don’t explicitly provide an account, an ensembl `HostAccount` for the latest release is created.

## What Species Are Available?

Another key element, of course, is the species available. Included as part of `cogent.db.ensembl` is the module `species`. This module contains a class that translates between latin names, common names and ensembl database prefixes. The species listed are guaranteed to be incomplete, given Ensembl’s update schedule, so it’s possible to dynamically add to this listing, or even change the common name for a given latin name.

    >>> from cogent.db.ensembl import Species
    >>> print Species
    ====================================================================================
           Common Name                     Species Name                Ensembl Db Prefix
    ------------------------------------------------------------------------------------
             A.aegypti                    Aedes aegypti                    aedes_aegypti
                Alpaca                    Vicugna pacos                    vicugna_pacos
          Anole lizard              Anolis carolinensis              anolis_carolinensis
             Armadillo             Dasypus novemcinctus             dasypus_novemcinctus
    Bottlenose dolphin               Tursiops truncatus               tursiops_truncatus
              Bushbaby               Otolemur garnettii               otolemur_garnettii...

In Australia, the common name for Gallus gallus is chook, so I’ll modify that.

    >>> Species.amendSpecies('Gallus gallus', 'chook')
    >>> assert Species.getCommonName('Gallus gallus') == 'chook'

You can also add new species for when they become available using `Species.amendSpecies`.

The `Species` class is basically used to translate between latin names and ensembl’s database naming scheme. It also serves to allow the user to simply enter the common name for a species in order to reference it’s genome databases. The queries are case-insensitive.

## Interrogating a Genome

As implied above, Ensembl databases are versioned, hence you must explicitly state what release you want. Aside from that, getting an object for querying a genome is simply a matter of importing the `HostAccount` and `Genome` classes. Here I’m going to use the `cogent.db.ensembl` level imports.

    >>> from cogent.db.ensembl import HostAccount, Genome
    >>> human = Genome(Species='human', Release=Release, account=account)
    >>> print human
    Genome(Species='Homo sapiens'; Release='52')

Notice I used the common name rather than full name. The `Genome` provides an interface to obtaining different attributes. It’s primary role is to allow selection of genomic regions according to some search criteria. The type of region is presently limited to `Gene`, `Est`, `CpGisland`, `Repeat` and `Variation`. There’s also a `GenericRegion`. The specific types are also capable of identifying information related to themselves, as we will demonstrate below.

### A Note on Coordinate Systems

The positions employed on Ensembl’s web-site, and in their MySQL database differ from those used internally by `cogent.db.ensembl`. In all cases where you are querying `cogent.db.ensembl` objects directly inputting nucleotide positions you can indicate you are using Ensembl coordinates by setting `ensembl_coord=True`. If you are explicitly passing in a `cogent.db.ensembl` region, that argument has no effect.

### Selecting Gene’s

The genome can be queried for gene’s in a number of ways through the `Genome.getGenesMatching` method. The method allows querying for gene(s) by the following identifiers: HGNC symbol; Ensembl `stable_id`; description; or coding type. In general for such queries, case shouldn’t matter. For instance, find the BRCA2 gene by it’s HGNC symbol.

    >>> genes = human.getGenesMatching(Symbol='brca2')

Because there can be multiple hits from a query, and because we wish to not spend time doing things (like talking to the database) unnecessarily, the result of the query is a python generator. This acts like a series and allows you to iterate over the database hits until you find the one you want and then terminate the record collection.

    >>> for gene in genes:
    ...     if gene.Symbol.lower() == 'brca2':
    ...         break
    ...
    >>> brca2 = gene # so we keep track of this reference for later on
    >>> print brca2.Symbol
    BRCA2
    >>> print brca2.Description
    Breast cancer type...
    >>> print brca2
    gene(Species=Homo sapiens; BioType="protein_coding"; Description="Breast...

This code serves to illustrate a few things. First, the sorts of properties that exist on the object. These can be directly accessed as illustrated above. Secondly, that the argument names to `getGenesMatching` match the properties.

Gene’s also have a location. The length of a gene is the difference between that start and end location. (Unfortunately, gene length is something that can vary between builds, for the purpose of testing the accuracy of this document we use the following output provides for variability)

    >>> print brca2.Location
    Homo sapiens:chromosome:13:31787616...
    >>> print len(brca2) # ~84190
    841...

Each location is directly tied to the parent genome and the coordinate above also shows the coordinates’ type (chromosome in this case), name (13), start, end and strand. The start and end positions are python indices and will differ from the Ensembl indices in that start will be the Ensembl index - 1. This is because python counts from 0, not 1. In querying for regions using a specific set of coordinates, it is possible to put in the Ensembl coordinates (demonstrated below).

`Gene` has several useful properties, including the ability to directly get their own DNA sequence and their `CanonicalTranscript` and `Transcripts`. `CanonicalTranscript` is the characteristic transcript for a gene, as defined by Ensembl. `Transcripts` is a tuple attribute containing individual region instances of type `Transcript`. A `Transcript` has `Exons`, a `Cds` and, if the `BioType` is protein coding, a protein sequence. In the following we grab just the first transcript from `brca2`

    >>> print brca2.BioType
    protein_coding
    >>> print brca2.Seq
    GTGGCGCGAGCTT...
    >>> transcript = brca2.Transcripts[0]
    >>> print transcript.ProteinSeq
    MPIGSKERPTF...
    >>> print brca2.CanonicalTranscript.Cds
    ATGCCTATTGGATC...
    >>> print brca2.CanonicalTranscript.ProteinSeq
    MPIGSKERPTF...

It is also possible to iterate over a transcript’s exons, over their translated exons, or to obtain their coding DNA sequence.

    >>> for exon in transcript.Exons:
    ...     print exon, exon.Location
    exon(StableId=ENSE00001184784, Rank=1) Homo sapiens:chromosome:13:...
    >>> for exon in transcript.TranslatedExons:
    ...     print exon, exon.Location
    exon(StableId=ENSE00001484009, Rank=2) Homo sapiens:chromosome:13:...
    >>> print transcript.Cds
    ATGCCTATTGGATCCAAA...

The `Cds` sequence includes the stop-codon, if present. The reason for this is there are many annotated transcripts in the Ensembl database the length of whose transcribed exons are not divisible by 3. Hence we leave it to the user to decide how to deal with that, but mention here that determining the number of complete codons is trivial and you can slice the `Cds` so that it’s length is divisible by 3.

The `Exons` and `TranslatedExons` properties are tuples that are evaluated on demand and can be sliced. Each `Exons/TranslatedExons` is itself a region, with all of the properties of generic regions (like having a `Seq` attribute).

The `Gene` region also has convenience methods for examining properties of it’s transcripts, in presenting the `Cds` lengths and getting the `Transcript` encoding the longest `Cds`. (In the `brca2` case here these have little impact since at the time of writing there was a single annotated transcript.)

    >>> print brca2.getCdsLengths() # ~10257
    [102...
    >>> longest = brca2.getLongestCdsTranscript()
    >>> print longest.Cds
    ATGCCTATTGGATCCAAA...

All Regions have a `getFeatures` method which differs from that on genome only in that the genomic coordinates are automatically entered for you. Regions also have the ability to return their sequence as an annotated `cogent` sequence. The method on `Gene` simply queries the parent genome using the gene’s own location as the coordinate for the currently supported region types. We will query `brca2` asking for gene features, the end-result will be a `cogent` sequence that can be used to obtain the CDS, for instance, using the standard `cogent` annotation capabilities.

    >>> annot_brca2 = brca2.getAnnotatedSeq(feature_types='gene')
    >>> cds = annot_brca2.getAnnotationsMatching('CDS')[0].getSlice()
    >>> print cds
    ATGCCTATTGGATCCAAA...

Those are the properties of a `Gene`, at present, of direct interest to end-users.

There are obviously different types of genes, and the `Genome` object provides an ability to establish exactly what distinct types are defined in Ensembl.

    >>> print human.getDistinct('BioType')
    ['scRNA_pseudogene', ...

The genome can be queried for any of these types, for instance we’ll query for `rRNA`. We’ll get the first few records and then exit.

    >>> rRNA_genes = human.getGenesMatching(BioType='rRNA')
    >>> count = 0
    >>> for gene in rRNA_genes:
    ...     print gene
    ...     count += 1
    ...     if count == 1:
    ...         break
    ...
    gene(Species=Homo sapiens; BioType="Mt_rRNA"; ...

This has the effect of returning any gene whose `BioType` includes the phrase `rRNA`. If a gene is not a protein coding gene, as in the current case, then it’s `Transcripts` will have `ProteinSeq==None` and `TranslatedExons==None`, but it will have `Exons` and a `Cds`.

    >>> transcript = gene.Transcripts[0]
    >>> assert transcript.ProteinSeq == None
    >>> assert transcript.TranslatedExons == None
    >>> transcript.Cds != None
    True

### Getting ESTs

Ensembl’s `otherfeatures` database mirrors the structure of the `core` database and contains EST information. Hence, the `Est` region inherits directly from `Gene` (ie has many of the same properties). `est` is a supported `feature_types` for the `getFeatures` method. You can also directly query for an EST using Ensembl’s `StableID`. Here, however, we’ll just query for `Est` that map to the `brca2` region.

    >>> ests = human.getFeatures(feature_types='est', region=brca2)
    >>> for est in ests:
    ...     print est
    est(Species=Homo sapiens; BioType="protein_coding"; Description="None";...

### Getting Variation

`Variation` regions also have distinctive properties worthy of additional mention. As for genes, there are distinct types stored in Ensembl that may be of interest. Those types can likewise be discovered from the genome,

    >>> print human.getDistinct('Effect')
    ['INTERGENIC',...

and that information can be used to query the genome for all variation of that effect. (Note: what we term `effect`, Ensembl terms consquence. We use `effect` because it’s shorter.) We allow the query to be an inexact match by setting `like=True`. Again we’ll just iterate over the first few.

    >>> nsyn_variants = human.getVariation(Effect='NON_SYNONYMOUS_CODING',
    ...                             like=True)
    >>> for nsyn_variant in nsyn_variants:
    ...     print nsyn_variant
    ...     if nsyn_variant.Symbol == 'rs2853516':
    ...         break
    variation(Symbol="rs28358582"; Effect=['SPLICE_SITE', 'NON_SYNONYMOUS_CODING']; ...

`Variation` objects also have other useful properties, such as a location, the number of alleles. The length of a `Variation` instance is the length of it’s longest allele.

    >>> assert len(nsyn_variant) == 1
    >>> print nsyn_variant.Location
    Homo sapiens:chromosome:MT:3316-3317:1
    >>> assert nsyn_variant.NumAlleles == 2

They also have `PeptideAlleles` (when appropriate), `FlankingSeq` and even `Seq` which, of course, in the case of a SNP is a single nucleotide long and should correspond to one of the alleles. In the latter case, this property is a tuple with the 0th entry being the 5’ 300 nucleotides and the 1st entry being the 3’ nucleotides.

    >>> print nsyn_variant.FlankingSeq[0]
    TGCAGCCGCTA...
    >>> print nsyn_variant.FlankingSeq[1]
    CCAACCTCATA...
    >>> assert str(nsyn_variant.Seq) in nsyn_variant.Alleles, str(nsyn_variant.Seq)

As a standard feature, `Variation` within a specific interval can also be obtained. For instance, we can use the `brca2` gene region instance created above to find all the variants within the gene of a specific type. Not all done in the one query, mind, but in a relatively straightforward manner.

    >>> brca2_snps = human.getFeatures(feature_types='variation',
    ...                      region=brca2)
    >>> for snp in brca2_snps:
    ...     if 'non_synonymous' in snp.Effect.lower():
    ...         break
    >>> print snp
    variation(Symbol="rs4987046"; Effect='NON_SYNONYMOUS_CODING'; Alleles="A/G")
    >>> print snp.Location
    Homo sapiens:chromosome:13:31791270-31791271:1

### Other Region Types

These can be obtained from the genome instance using the genomes `getFeatures` method. At present, only repeats, CpG islands, variation, EST’s and genes can be obtained through this method. There’s also `GenericRegion`, which is precisely that.

In ensembl MySQL database, each type of feature may be recorded at multiple coordinate levels. The default level is the highest one. For example, CpG island may be stored at chromosome, supercontig and contig levels, the default will be chromosome.

    >>> chicken = Genome(Species='chook', Release=Release, account=account)
    >>> print chicken.FeatureCoordLevels
    Gallus gallus
    =============================================================
         Type                             Levels    Levels_in_use
    -------------------------------------------------------------
         gene                         chromosome       chromosome
       repeat                             contig           contig
          est                         chromosome       chromosome
    variation                         chromosome       chromosome
          cpg    chromosome, supercontig, contig       chromosome
    -------------------------------------------------------------

The default coordinate level can be changed:

    >>> chicken.setFeatureCoordLevel('cpg', 'supercontig')
    >>> print chicken.FeatureCoordLevels
    Gallus gallus
    =============================================================
         Type                             Levels    Levels_in_use
    -------------------------------------------------------------
         gene                         chromosome       chromosome
       repeat                             contig           contig
          est                         chromosome       chromosome
    variation                         chromosome       chromosome
          cpg    chromosome, supercontig, contig      supercontig
    -------------------------------------------------------------

## Comparative Analyses

The Ensembl compara database is represented by `cogent.db.ensembl.compara.Compara`. This object provides a means for querying for relationships among genomes and obtaining multiple alignments. For convenience the class is made available through the top-level module for importing (i.e. `cogent.db.ensembl.Compara`). Instantiating `Compara` requires, as before, an account instance, the ensembl release and the series of species of interest. For the purpose of illustration we’ll use the human, mouse and rat genomes.

Note

Any queries on this instance of compara will only return results for the indicated species. If you want to query about other species, create another instance.

    >>> from cogent.db.ensembl import Compara
    >>> compara = Compara(['human', 'mouse', 'rat'], account=account,
    ...                  Release=Release)
    >>> print compara
    Compara(Species=('Homo sapiens', 'Mus musculus', 'Rattus norvegicus'); Release=52...

The `Compara` object loads the corresponding `Genome`‘s and attaches them to itself as named attributes. The genome instances are named according to their common name in CamelCase. For instance, if we had created a compara instance with the american pika species included, then that genome would be accessed as `compara.AmericanPika`. We access the human genome in this compara instance and conduct a gene search.

    >>> gene = compara.Human.getGenesMatching(StableId='ENSG00000139618')
    >>> brca2 = list(gene)[0]
    >>> print brca2
    gene(Species=Homo sapiens; BioType="protein_coding"; Description="Breast...

I’ve used `list` here as it causes the generator to be iterated over fully which I know is safe since I know there will only be 1 record returned.

We can now use this result to search compara for related genes. We note here that like `Genome`, `Compara` has the `getDistinct` method to assist in identifying appropriate search criteria. What are the distinct types of gene relationships recorded in Ensembl, for instance?

    >>> relationships = compara.getDistinct('relationship')
    >>> print relationships
    ['ortholog_one2one', 'within_species_paralog',...

So we use the `brca2` instance above and search for orthologs among the human, mouse, rat genomes.

    >>> orthologs = compara.getRelatedGenes(gene_region=brca2,
    ...                 Relationship='ortholog_one2one')
    >>> print orthologs
    related_genes:
     Relationships=ortholog_one2one
      gene(Species=Mus musculus; BioType="protein_coding"; Description="breast cancer...

I could also have done that query using a `StableId`, which I now do using the Ensembl mouse identifier for brca2.

    >>> orthologs = compara.getRelatedGenes(StableId='ENSMUSG00000041147',
    ...                 Relationship='ortholog_one2one')
    >>> print orthologs
    related_genes:
     Relationships=ortholog_one2one
      gene(Species=Mus musculus; BioType="protein_coding"; Description="breast cancer...

The `RelatedGenes` object has a number of properties allowing you to get access to data. A `Members` attribute holds each of the `Gene` instances displayed above. The length of this attribute tells you how many hits there were, while each member has all of the capabilities described for `Gene` above, eg. a `Cds` property. There is also a `getSeqLengths` method which returns the vector of sequence lengths for the members. This method returns just the lengths of the individual genes.

    >>> print orthologs.Members
    (gene(Species='Mus musculus'; BioType="protein_coding"; Descr...
    >>> print orthologs.getSeqLengths()
    [47...

In addition there’s a `getMaxCdsLengths` method for returning the lengths of the longest `Cds` from each member.

    >>> print orthologs.getMaxCdsLengths()
    [99...

You can also obtain the sequences as a `SequenceCollection` (unaligned), with the ability to have those sequences annotated as described above. The sequences are named in accordance with their genomic coordinates.

    >>> seqs = orthologs.getSeqCollection(feature_types='gene')
    >>> print seqs.Names
    ['Mus musculus:chromosome:5:15...

We can also search for other relationship types, which we do here for a histone.

    >>> paralogs = compara.getRelatedGenes(StableId='ENSG00000164032',
    ...             Relationship='within_species_paralog')
    >>> print paralogs
    related_genes:
     Relationships=within_species_paralog
      gene(Species=Homo sapiens; BioType="protein_coding"; Description="Histone...

### Getting Comparative Alignments

Ensembl stores multiple sequence alignments for selected species. For a given group of species, you can examine what alignments are available by printing the `method_species_links` attribute of `Compara`. This will return something like

    >>> print compara.method_species_links
    Align Methods/Clades
    ===================================================================================...
    method_link_species_set_id  method_link_id  species_set_id  align_method           ...
    -----------------------------------------------------------------------------------...
                           338              10           31347         PECAN  12 amniot...
                           341              12           31349       ORTHEUS       9 eu...
                           352              12           31684       ORTHEUS      29 eu...
    -----------------------------------------------------------------------------------...

The `align_method` and `align_clade` columns can be used as arguments to `getSyntenicRegions`. This method is responsible for returning `SyntenicRegions` instances for a given coordinate from a species. As it’s possible that multiple records may be found from the multiple alignment for a given set of coordinates, the result of calling this method is a python generator. The returned regions have a length, defined by the full set of aligned sequences. If the `omit_redundant` argument is used, then positions with gaps in all sampled species will be removed in the alignment to be returned. The length of the syntenic region, however, is the length of the unfiltered alignment.

Note

It’s important to realise that multiple alignments are from these clades. Hence, sequence regions that you might expect would result in a contiguous alignment in the species subset of interest may be returned as separate `SyntenicRegions` due to the influence on the alignment of the other species.

    >>> syntenic_regions = compara.getSyntenicRegions(region=brca2,
    ...                      align_method='ORTHEUS', align_clade='mammal')
    >>> for syntenic_region in syntenic_regions:
    ...     print syntenic_region
    ...     print len(syntenic_region)
    ...     print repr(syntenic_region.getAlignment(omit_redundant=False))
    syntenic_regions:
    Coordinate('Human':"chro...":"13":31787616-31798755:1)
    16637
    1 x 16637 dna alignment: Homo sapiens:chromosome:13:31787616-31798755:1[GTGGCGCGAGC...]
    syntenic_regions:
      Coordinate('Human':"chro...":"13":31798641-31871809:1)
      Coordinate('Mouse':"chro...":"5":151334868-151372713:-1)
      Coordinate('Rat':"chro...":"12":4282529-4317319:1)
    148073
    3 x 148073 dna alignment: Rattus norvegicus:chromosome:12:4282529-4317319:-1[-AG...

We consider a species for which only pairwise alignments are available, the bush baby.

    >>> compara_pair = Compara(['Human', 'Bushbaby'], Release=Release,
    ...                        account=account)
    >>> print compara_pair
    Compara(Species=('Homo sapiens', 'Otolemur garnettii'); Release=52; connected=True)

Printing the `method_species_links` table provides all the necessary information for specifying selection conditions.

    >>> print compara_pair.method_species_links
    Align Methods/Clades
    ===================================================================================...
    method_link_species_set_id  method_link_id  species_set_id  align_method           ...
    -----------------------------------------------------------------------------------...
                           264               1           30604    BLASTZ_NET  H.sap-O.g...
                           352              12           31684       ORTHEUS           ...
    -----------------------------------------------------------------------------------...

    >>> gene = compara_pair.Bushbaby.getGenesMatching(
    ...                             StableId='ENSOGAG00000003166'
    ...                             )
    ...
    >>> gene=list(gene)[0]
    >>> print gene
    gene(Species=Otolemur garnettii; BioType="protein_coding"...
    >>> syntenic = compara_pair.getSyntenicRegions(region=gene,
    ...          align_method='BLASTZ_NET', align_clade='H.sap-O.gar')
    ...
    >>> for region in syntenic:
    ...     print region
    ...     break
    syntenic_regions:
      Coordinate('Bushbaby':"gene...":"Gene...":196128-196245:-1)
      Coordinate('Human':"chro...":"7":135016850-135016966:1)

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