A rendered version of the docs is available at: http://pythonhosted.org/cruzdb/
A paper describing cruzdb is in Bioinformatics: https://doi.org/10.1093/bioinformatics/btt534
Genomes Database is a great resource for annotations, regulation
and variation and all kinds of data for a growing number of taxa.
This library aims to make utilizing that data simple so that we can do
sophisticated analyses without resorting to
As motivation, here's an example of some of the capabilities::
from cruzdb import Genome g = Genome(db="hg18") muc5b = g.refGene.filter_by(name2="MUC5B").first() muc5b refGene(chr11:MUC5B:1200870-1239982) muc5b.strand '+' # the first 4 introns muc5b.introns[:4] [(1200999L, 1203486L), (1203543L, 1204010L), (1204082L, 1204420L), (1204682L, 1204836L)] # the first 4 exons. muc5b.exons[:4] [(1200870L, 1200999L), (1203486L, 1203543L), (1204010L, 1204082L), (1204420L, 1204682L)] # note that some of these are not coding because they are < cdsStart muc5b.cdsStart 1200929L # the extent of the 5' utr. muc5b.utr5 (1200870L, 1200929L) # we can get the (first 4) actual CDS's with: muc5b.cds[:4] [(1200929L, 1200999L), (1203486L, 1203543L), (1204010L, 1204082L), (1204420L, 1204682L)] # the cds sequence from the UCSC DAS server as a list with one entry per cds muc5b.cds_sequence #doctest: +ELLIPSIS ['atgggtgccccgagcgcgtgccggacgctggtgttggctctggcggccatgctcgtggtgccgcaggcag', ...] transcript = g.knownGene.filter_by(name="uc001aaa.2").first() transcript.is_coding False # convert a genome coordinate to a local coordinate. transcript.localize(transcript.txStart) 0L # or localize to the CDNA position. print transcript.localize(transcript.cdsStart, cdna=True) None
with cruzdb 0.5.4+ installed, given a file
input.bed you can do::
python -m cruzdb hg18 input.bed refGene cpgIslandExt
to have the intervals annotated with the
tables from version
... are so in. We can get one from a table as::
df = g.dataframe('cpgIslandExt') df.columns #doctest: +ELLIPSIS Index([chrom, chromStart, chromEnd, name, length, cpgNum, gcNum, perCpg, perGc, obsExp], dtype=object)
All of the above can be repeated using knownGene annotations by changing 'refGene' to 'knownGene'. And, it can be done easily for a set of genes.
k-nearest neighbors, upstream, and downstream searches are available. Up and downstream searches use the strand of the query feature to determine the direction:
>>> nearest = g.knearest("refGene", "chr1", 9444, 9555, k=6) >>> up_list = g.upstream("refGene", "chr1", 9444, 9555, k=6) >>> down_list = g.downstream("refGene", "chr1", 9444, 9555, k=6)
The above uses the mysql interface from UCSC. It is now possible to mirror any tables from UCSC to a local sqlite database via:
import os if os.path.exists("/tmp/u.db"): os.unlink('/tmp/u.db')
g = Genome('hg18')
gs = g.mirror(['chromInfo'], 'sqlite:////tmp/u.db')
and then use as:
gs.chromInfo <class 'cruzdb.sqlsoup.chromInfo'>
Most of the per-row features are implemented in
cruzdb/models.py in the
Feature class. If you want to add something to a feature (like the existing
feature.utr5) add it here.
The tables are reflected using
sqlalchemy_ and mapped in the
__getattr__\ method of the
Genome class in
So a call like::
calls the __getattr__ method with the table arg set to 'knownGene'
that table is then reflected and an object with parent classes of
and sqlalchemy's declarative_base is returned.
To start coding, it is probably polite to grab your own copy of some of the UCSC tables so as not to overload the UCSC server. You can run something like::
Genome('hg18').mirror(["refGene", "cpgIslandExt", "chromInfo", "knownGene", "kgXref"], "sqlite:////tmp/hg18.db")
Then the connection would be something like::
g = Genome("sqlite:////tmp/hg18.db")
If you have a feature you like to use/implement, open a ticket on github for discussion. Below are some ideas.