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Note that much more extensive documentation is available in :ref:`query-ensembl`.
Connecting
----------
.. Gavin Huttley
`Ensembl <http://www.ensembl.org>`_ provides access to their MySQL databases directly or users can download and run those databases on a local machine. To use the Ensembl's UK servers for running queries, nothing special needs to be done as this is the default setting for PyCogent's ``ensembl`` module. To use a different Ensembl installation, you create an account instance:
.. doctest::
>>> from cogent.db.ensembl import HostAccount
>>> account = HostAccount('fastcomputer.topuni.edu', 'username',
... 'canthackthis')
To specify a specific port to connect to MySQL on:
.. doctest::
>>> from cogent.db.ensembl import HostAccount
>>> account = HostAccount('anensembl.server.edu', 'someuser',
... 'somepass', port=3306)
.. we create valid account now to work on my local machines here at ANU
.. doctest::
:hide:
>>> import os
>>> hotsname, uname, passwd = os.environ['ENSEMBL_ACCOUNT'].split()
>>> account = HostAccount(hotsname, uname, passwd)
Species to be queried
---------------------
To see what existing species are available
.. doctest::
>>> from cogent.db.ensembl import Species
>>> print Species
================================================================================
Common Name Species Name Ensembl Db Prefix
--------------------------------------------------------------------------------
A.aegypti Aedes aegypti aedes_aegypti
A.clavatus Aspergillus clavatus aspergillus_clavatus...
If Ensembl has added a new species which is not yet included in ``Species``, you can add it yourself.
.. doctest::
>>> Species.amendSpecies('A latinname', 'a common name')
You can get the common name for a species
.. doctest::
>>> Species.getCommonName('Procavia capensis')
'Rock hyrax'
and the Ensembl database name prefix which will be used for all databases for this species.
.. doctest::
>>> Species.getEnsemblDbPrefix('Procavia capensis')
'procavia_capensis'
Species common names are used to construct attributes on PyCogent ``Compara`` instances). You can get the name that will be using the ``getComparaName`` method. For species with a real common name
.. doctest::
>>> Species.getComparaName('Procavia capensis')
'RockHyrax'
or with a shortened species name
.. doctest::
>>> Species.getComparaName('Caenorhabditis remanei')
'Cremanei'
Get genomic features
--------------------
Find a gene by gene symbol
^^^^^^^^^^^^^^^^^^^^^^^^^^
We query for the *BRCA2* gene for humans.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> print human
Genome(Species='Homo sapiens'; Release='67')
>>> genes = human.getGenesMatching(Symbol='BRCA2')
>>> for gene in genes:
... if gene.Symbol == 'BRCA2':
... print gene
... break
Gene(Species='Homo sapiens'; BioType='protein_coding'; Description='breast cancer 2,...'; StableId='ENSG00000139618'; Status='KNOWN'; Symbol='BRCA2')
Find a gene by Ensembl Stable ID
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We use the stable ID for *BRCA2*.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> gene = human.getGeneByStableId(StableId='ENSG00000139618')
>>> print gene
Gene(Species='Homo sapiens'; BioType='protein_coding'; Description='breast cancer 2,...'; StableId='ENSG00000139618'; Status='KNOWN'; Symbol='BRCA2')
Find genes matching a description
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We look for breast cancer related genes that are estrogen induced.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> genes = human.getGenesMatching(Description='breast cancer anti-estrogen')
>>> for gene in genes:
... print gene
Gene(Species='Homo sapiens'; BioType='lincRNA'; Description='breast cancer anti-estrogen...'; StableId='ENSG00000262117'; Status='NOVEL'; Symbol='BCAR4')...
We can also require that an exact (case insensitive) match to the word(s) occurs within the description by setting ``like=False``.
.. doctest::
>>> genes = human.getGenesMatching(Description='breast cancer anti-estrogen',
... like=False)
>>> for gene in genes:
... print gene
Gene(Species='Homo sapiens'; BioType='lincRNA'; Description='breast cancer anti-estrogen...'; StableId='ENSG00000262117'; Status='NOVEL'; Symbol='BCAR4')...
Get canonical transcript for a gene
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We get the canonical transcripts for *BRCA2*.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> transcript = brca2.CanonicalTranscript
>>> print transcript
Transcript(Species='Homo sapiens'; CoordName='13'; Start=32889610; End=32973347; length=83737; Strand='+')
Get the CDS for a transcript
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> transcript = brca2.CanonicalTranscript
>>> cds = transcript.Cds
>>> print type(cds)
<class 'cogent.core.sequence.DnaSequence'>
>>> print cds
ATGCCTATTGGATCCAAAGAGAGGCCA...
Look at all transcripts for a gene
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> for transcript in brca2.Transcripts:
... print transcript
Transcript(Species='Homo sapiens'; CoordName='13'; Start=32889610; End=32973347; length=83737; Strand='+')
Transcript(Species='Homo sapiens'; CoordName='13'; Start=32889641; End=32907428; length=17787; Strand='+')...
Get the first exon for a transcript
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We show just for the canonical transcript.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> print brca2.CanonicalTranscript.Exons[0]
Exon(StableId=ENSE00001184784, Rank=1)
Get the introns for a transcript
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We show just for the canonical transcript.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> for intron in brca2.CanonicalTranscript.Introns:
... print intron
Intron(TranscriptId=ENST00000380152, Rank=1)
Intron(TranscriptId=ENST00000380152, Rank=2)
Intron(TranscriptId=ENST00000380152, Rank=3)...
Inspect the genomic coordinate for a feature
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> print brca2.Location.CoordName
13
>>> print brca2.Location.Start
32889610
>>> print brca2.Location.Strand
1
Get repeat elements in a genomic interval
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We query the genome for repeats within a specific coordinate range on chromosome 13.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> repeats = human.getFeatures(CoordName='13', Start=32879610, End=32889610, feature_types='repeat')
>>> for repeat in repeats:
... print repeat.RepeatClass
... print repeat
... break
SINE/Alu
Repeat(CoordName='13'; Start=32879362; End=32879662; length=300; Strand='-', Score=2479.0)
Get CpG island elements in a genomic interval
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We query the genome for CpG islands within a specific coordinate range on chromosome 11.
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> islands = human.getFeatures(CoordName='11', Start=2150341, End=2170833, feature_types='cpg')
>>> for island in islands:
... print island
... break
CpGisland(CoordName='11'; Start=2158951; End=2162484; length=3533; Strand='-', Score=3254.0)
Get SNPs
--------
For a gene
^^^^^^^^^^
We find the genetic variants for the canonical transcript of *BRCA2*.
.. note:: The output is significantly truncated!
.. doctest::
>>> from cogent.db.ensembl import Genome
>>> human = Genome('human', Release=67, account=account)
>>> brca2 = human.getGeneByStableId(StableId='ENSG00000139618')
>>> transcript = brca2.CanonicalTranscript
>>> print transcript.Variants
(<cogent.db.ensembl.region.Variation object at ...
>>> for variant in transcript.Variants:
... print variant
... break
Variation(Symbol='rs55880202'; Effect=['2KB_upstream_variant', '5_prime_UTR_variant', '5KB_upstream_variant']; Alleles='C/T')...
Get a single SNP
^^^^^^^^^^^^^^^^
We get a single SNP and print it's allele frequencies.
.. doctest::
>>> snp = list(human.getVariation(Symbol='rs34213141'))[0]
>>> print snp.AlleleFreqs
=============================
allele freq sample_id
-----------------------------
A 0.0303 933
G 0.9697 933
G 1.0000 11208
G 1.0000 11519
A 0.0110 113559
G 0.9889 113559...
What alignment types available
------------------------------
We create a ``Compara`` instance for human, chimpanzee and macaque.
.. doctest::
>>> from cogent.db.ensembl import Compara
>>> compara = Compara(['human', 'chimp', 'macaque'], Release=67,
... account=account)
>>> print compara.method_species_links
Align Methods/Clades
===================================================================================================================
method_link_species_set_id method_link_id species_set_id align_method align_clade
-------------------------------------------------------------------------------------------------------------------
580 10 34468 PECAN 19 amniota vertebrates Pecan
548 13 34115 EPO 6 primates EPO
578 13 34466 EPO 12 eutherian mammals EPO
582 14 34697 EPO_LOW_COVERAGE 35 eutherian mammals EPO_LOW_COVERAGE
-------------------------------------------------------------------------------------------------------------------
Get genomic alignment for a gene region
---------------------------------------
We first get the syntenic region corresponding to human gene *BRCA2*.
.. doctest::
>>> from cogent.db.ensembl import Compara
>>> compara = Compara(['human', 'chimp', 'macaque'], Release=67,
... account=account)
>>> human_brca2 = compara.Human.getGeneByStableId(StableId='ENSG00000139618')
>>> regions = compara.getSyntenicRegions(region=human_brca2, align_method='EPO', align_clade='primates')
>>> for region in regions:
... print region
SyntenicRegions:
Coordinate(Human,chro...,13,32889610-32973805,1)
Coordinate(Macaque,chro...,17,11686607-11778803,1)
Coordinate(Chimp,chro...,13,31957346-32040817,1)...
We then get a cogent ``Alignment`` object, requesting that sequences be annotated for gene spans.
.. doctest::
>>> aln = region.getAlignment(feature_types='gene')
>>> print repr(aln)
3 x 98805 dna alignment: Homo sapiens:chromosome:13:3288...
Parsing syntenic regions
------------------------
Not all regions in a given genome have a syntenic alignment, and some have more than one alignment.
To illustrate these cases, we can consider an alignment between mouse and human, using the ``PECAN``
alignment method in the vertebrates clade:
.. doctest::
>>> species = ["mouse", "human"]
>>> compara = Compara(species, Release=67, account=account)
>>> clade = "vertebrates"
>>> chrom, start, end, strand = "X", 165754928, 165755079, "-"
>>> regions = compara.getSyntenicRegions(Species="mouse", CoordName=chrom,
... Start=start, End=end, align_method="PECAN",
... align_clade=clade, Strand=strand)
>>> aligned_pairs = [r for r in regions]
>>> alignment = aligned_pairs[0]
>>> aligned_regions = [m for m in alignment.Members
... if m.Region is not None]
>>> source_region, target_region = aligned_regions
>>> print source_region.Location.CoordName, source_region.Location.Start, source_region.Location.End
X 165754928 165755079
>>> print target_region.Location.CoordName, target_region.Location.Start, target_region.Location.End
X 11132954 11133105
.. note:: We took the aligned regions from the ``regions`` generator and put them in a list for convenience.
If there are no regions returned (i.e. ``num_pairs`` is zero), then no alignment could be found. In the case of
the above region, an exon in the *Hccs* gene, there is only one alignment. We then accessed the coordinates of the
alignment using the ``Members`` attribute of the region. Each element of ``aligned_regions`` is a ``SyntenicRegion``
instance, whose coordinates can be pulled from the ``Location`` attribute.
This example shows that mouse region ``X:165754928-165755079`` aligns only to human region ``X:11132954-11133105``.
.. note:: Sometimes, the genomic coordinates given to ``getSyntenicRegions`` will contain multiple alignments between the pair of genomes, in which case two or more regions will be returned in ``aligned_pairs``.
Getting related genes
---------------------
What gene relationships are available
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. doctest::
>>> from cogent.db.ensembl import Compara
>>> compara = Compara(['human', 'chimp', 'macaque'], Release=67,
... account=account)
>>> print compara.getDistinct('relationship')
[u'ortholog_one2many', u'contiguous_gene_split', u'ortholog_one2one',...
Get one-to-one orthologs
^^^^^^^^^^^^^^^^^^^^^^^^
We get the one-to-one orthologs for *BRCA2*.
.. doctest::
>>> from cogent.db.ensembl import Compara
>>> compara = Compara(['human', 'chimp', 'macaque'], Release=67,
... account=account)
>>> orthologs = compara.getRelatedGenes(StableId='ENSG00000139618',
... Relationship='ortholog_one2one')
>>> print orthologs
RelatedGenes:
Relationships=ortholog_one2one
Gene(Species='Macaca mulatta'; BioType='protein_coding'; Description=...
We iterate over the related members.
.. doctest::
>>> for ortholog in orthologs.Members:
... print ortholog
Gene(Species='Macaca mulatta'; BioType='protein_coding'; Description=...
We get statistics on the ortholog CDS lengths.
.. doctest::
>>> print orthologs.getMaxCdsLengths()
[10008, 10257, 10257]
We get the sequences as a sequence collection, with annotations for gene.
.. doctest::
>>> seqs = orthologs.getSeqCollection(feature_types='gene')
Get CDS for all one-to-one orthologs
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We sample all one-to-one orthologs for a group of species, generating a FASTA formatted string that can be written to file. We check all species have an ortholog and that all are translatable.
.. doctest::
>>> from cogent.core.alphabet import AlphabetError
>>> common_names = ["mouse", "rat", "human", "opossum"]
>>> latin_names = set([Species.getSpeciesName(n) for n in common_names])
>>> latin_to_common = dict(zip(latin_names, common_names))
>>> compara = Compara(common_names, Release=67, account=account)
>>> for gene in compara.Human.getGenesMatching(BioType='protein_coding'):
... orthologs = compara.getRelatedGenes(gene,
... Relationship='ortholog_one2one')
... # make sure all species represented
... if orthologs is None or orthologs.getSpeciesSet() != latin_names:
... continue
... seqs = []
... for m in orthologs.Members:
... try: # if sequence can't be translated, we ignore it
... # get the CDS without the ending stop
... seq = m.CanonicalTranscript.Cds.withoutTerminalStopCodon()
... # make the sequence name
... seq.Name = '%s:%s:%s' % \
... (latin_to_common[m.genome.Species], m.StableId, m.Location)
... aa = seq.getTranslation()
... seqs += [seq]
... except (AlphabetError, AssertionError):
... seqs = [] # exclude this gene
... break
... if len(seqs) == len(common_names):
... fasta = '\n'.join(s.toFasta() for s in seqs)
... break
Get within species paralogs
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. doctest::
>>> paralogs = compara.getRelatedGenes(StableId='ENSG00000164032',
... Relationship='within_species_paralog')
>>> print paralogs
RelatedGenes:
Relationships=within_species_paralog
Gene(Species='Homo sapiens'; BioType='protein_coding'; Description='H2A histone...
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