| Website: | http://pmgenomics.ca/hoffmanlab/proj/genomedata/ |
|---|---|
| Author: | Michael M. Hoffman <mhoffman at uhnresearch dot ca> |
| Organization: | Princess Margaret Cancer Centre |
| Address: | Toronto Medical Discovery Tower 11-311, 101 College St, M5G 1L7, Toronto, Ontario, Canada |
| Copyright: | 2009-2014 Michael M. Hoffman |
For a broad overview, see the paper:
Hoffman MM, Buske OJ, Noble WS. (2010). The Genomedata format for storing large-scale functional genomics data. Bioinformatics, 26(11):1458-1459; doi:10.1093/bioinformatics/btq164
Please cite this paper if you use Genomedata.
Both Python (2.6 or 2.7) and the Python package PyTables is required to be installed before you can install Genomedata.
To install PyTables the HDF5 libraries and prerequisite python packages need to be installed manually.
Ubuntu/Debian:
sudo apt-get install libhdf5-serial-dev hdf5-tools
CentOS/RHEL/Fedora:
sudo yum -y install hdf5 hdf5-devel
OpenSUSE:
sudo zypper in hdf5 hdf5-devel libhdf5
With Python 2.6 or 2.7 installed:
pip install numpy
pip install numexpr
pip install cython
pip install git+https://github.com/PyTables/PyTables.git@1fd2bf#egg=tables
Note
As of the time of writing this document, the latest PyTables release (3.1.1) currently doesn’t not install with later versions of Cython. The command listed above is a known prerelease commit that works with later versions of Cython and with Genomedata.
If you would rather wish to install the latest (3.1.1) release of PyTables with an older compatable release of Cython you can run the following instead when installing Cython:
pip install cython==0.20.2
pip install tables
For more troubleshooting or more detailed instructions on how to configure and install PyTables for various systems see the official webpage:
After PyTables has been installed, Genomedata can be installed with pip by entering the following command at a prompt:
pip install genomedata
Note
Genomedata is only supported on 64 bit systems.
Note
The following are prerequisites:
This software has been tested on Linux and Mac OS X systems. We would love to add support for other systems in the future and will gladly accept any contributions toward this end.
Zlib
Note
For questions, comments, or troubleshooting, please refer to the support section.
Genomedata provides a way to store and access large-scale functional genomics data in a format which is both space-efficient and allows efficient random-access. Genomedata archives are currently write-once, although we are working to fix this.
Under the surface, Genomedata is implemented as one or more HDF5 files, but Genomedata provides a transparent interface to interact with your underlying data without having to worry about the mess of repeatedly parsing large data files or having to keep them in memory for random access.
The Genomedata hierarchy:
- Each Genome contains many Chromosomes
- Each Chromosome contains many Supercontigs
- Each Supercontig contains one continuous data set
- Each continuous data set is a numpy.array of floating point numbers with a column for each data track and a row for each base in the data set.
Genomedata archives are implemented as one or more HDF5 files. The API handles both single-file and directory archives transparently, but the implementation options exist for several performance reasons.
Implementing the archive as a directory makes it easier to parallelize access to the data. In particular, it makes it easy to create the archives in parallel with one chromosome on each machine. It also reduces the likelihood of running into the 2 GB file limit applicable to older applications and older versions of 32-bit UNIX. We are currently using an 81-track Genomedata archive for our research which has a total size of 18 GB, but the largest single file (chr1) is only 1.6 GB.
A directory-based Genomedata archive is not ideal for all circumstances, however, such as when working with genomes with many chromosomes, contigs, or scaffolds. In these situations, a single file implementation would be much more efficient. Additionally, having the archive as a single file allows the archive to be distributed much more easily (without tar/zip/etc).
Note
The default behavior is to implement the Genomedata archive as a directory if there are fewer than 100 sequences being loaded and as a single file otherwise.
New in version 1.1: Single-file-based Genomedata archives
A Genomedata archive contains sequence and may also contain numerical data associated with that sequence. You can easily load sequence and numerical data into a Genomedata archive with the genomedata-load command (see command details additional details):
genomedata-load [-t trackname=signalfile]... [-s sequencefile]... GENOMEDATAFILE
This command is a user-friendly shortcut to the typical workflow. The underlying commands are still installed and may be used if more fine-grained control is required (for instance, parallel data loading or adding additional tracks later). The commands and required ordering are:
Entire data tracks can later be replaced with the following pipeline:
New in version 1.1: The ability to replace data tracks.
Additional data tracks can be added to an existing archive with the following pipeline:
New in version 1.2: The ability to add data tracks.
As of the current version, Genomedata archives must include the underlying genomic sequence and can only be created with genomedata-load-assembly. A Genomedata archive can be created without any tracks, however, using the following pipeline:
New in version 1.2: The ability to create an archive without any data tracks.
Note
A call to h5repack after genomedata-close-data may be used to transparently compress the data.
The following is a brief example for creating a Genomedata archive from sequence and signal files.
Given the following two sequence files:
A text file, chr1.fa:
>chr1
taaccctaaccctaaccctaaccctaaccctaaccctaaccctaacccta
accctaaccctaaccctaaccctaaccct
A compressed text file, chrY.fa.gz:
>chrY
ctaaccctaaccctaaccctaaccctaaccctaaccctCTGaaagtggac
and the following two signal files:
signal_low.wigFix:
fixedStep chrom=chr1 start=5 step=1
0.372
-2.540
0.371
-2.611
0.372
-2.320
signal_high.bed.gz:
chrY 0 12 4.67
chrY 20 23 9.24
chr1 1 3 2.71
chr1 3 6 1.61
chr1 6 24 3.14
A Genomedata archive (genomedata.test) could then be created with the following command:
genomedata-load -s chr1.fa -s chrY.fa.gz -t low=signal_low.wigFix \
-t high=signal_high.bed.gz genomedata.test
or the following pipeline:
genomedata-load-assembly genomedata.test chr1.fa chrY.fa.gz
genomedata-open-data genomedata.test low high
genomedata-load-data genomedata.test low < signal_low.wigFix
zcat signal_high.bed.gz | genomedata-load-data genomedata.test high
genomedata-close-data genomedata.test
Note
chr1.fa and chrY.fa.gz could also be combined into a single sequence file with two sequences.
Warning
It is important that the sequence names (chrY, chr1) in the signal files match the sequence identifiers in the sequence files exactly.
The data in Genomedata is accessed through the hierarchy described in Overview. A full Python API is also available.
Note
The Python API expects that a genomedata archive has already been created. This can be done manually via the genomedata-load command. Alternatively, this can be done programmatically using :_load_seq:load_seq.
To appreciate the full benefit of Genomedata, it is most easily used as a contextmanager:
from genomedata import Genome
[...]
gdfilename = "/path/to/genomedata/archive"
with Genome(gdfilename) as genome:
[...]
Note
If Genome is used as a context manager, it will clean up any opened Chromosomes automatically. If not, the Genome object (and all opened chromosomes) should be closed manually with a call to Genome.close().
Genomedata is designed to make it easy to get to the data you want.
Here are a few examples:
Get arbitrary sequence (10-bp sequence starting at chr2:1423):
>>> chromosome = genome["chr2"]
>>> seq = chromosome.seq[1423:1433]
>>> seq
array([116, 99, 99, 99, 99, 103, 103, 103, 103, 103], dtype=uint8)
>>> seq.tostring()
'tccccggggg'
Get arbitrary data (data from first 3 tracks for region chr8:999-1000):
>>> chromosome = genome["chr8"]
>>> chromosome[999:1001, 0:3] # Note the half-open, zero-based indexing
array([[ NaN, NaN, NaN],
[ 3. , 5.5, 3.5], dtype=float32)
Get data for a specific track (specified data in first 5-bp of chr1):
>>> chromosome = genome["chr1"]
>>> data = chromosome[0:5, "sample_track"]
>>> data
array([ 47., NaN, NaN, NaN, NaN], dtype=float32)
Only specified data:
>>> from numpy import isfinite
>>> data[isfinite(data)]
array([ 47.], dtype=float32)
Note
Specify a slice for the track to keep the data in column form:
>>> col_index = chromosome.index_continuous("sample_track")
>>> data = chromosome[0:5, col_index:col_index+1]
Genomedata archives can be created and loaded from the command line with the genomedata-load command.
This is a convenience script that will do everything necessary to create a Genomedata archive. This script takes as input:
assembly files in either FASTA (.fa or .fa.gz) format (where the sequence identifiers are the names of the chromosomes/scaffolds to create), or assembly files in AGP format (when used with --assembly). This is mandatory, despite having an option interface.
the name of the Genomedata archive to create
See the full example for more details.
Command-line usage information:
Usage: genomedata-load [OPTIONS] GENOMEDATAFILE
--track and --sequence may be repeated to specify multiple trackname=trackfile
pairings and sequence files, respectively
Options:
--version show program's version number and exit
-h, --help show this help message and exit
-s SEQFILE, --sequence=SEQFILE
Add the sequence data in the specified file
-t TRACK, --track=TRACK
Add data for the given track. TRACK should be
specified in the form: NAME=FILE, such as: -t
signal=signal.dat
Alternately, as described in Overview, the underlying Python and C load scripts are also accessible for more finely-grained control. This can be especially useful for parallelizing Genomedata loading over a cluster.
You can use wildcards when specifying sequence files, such as in genomedata-load-assembly -s 'chr*.fa'. You must be sure to quote the wildcards so that they are not expanded by your shell. For most shells, this means using single quotes ('chr*.fa') instead of double quotes ("chr*.fa").
If you aren’t going to use the sequence later on, loading the assembly from an AGP file will be faster and take less memory during loading, and disk space afterward.
This command adds the provided sequence files to the specified Genomedata, archive creating it if it does not already exist. Sequence files should be in FASTA (.fa or .fa.gz) format. Gaps of >= 100,000 base pairs (specified as gap-length) in the reference sequence, are used to divide the sequence into supercontigs. The FASTA definition line will be used as the name for the chromosomes/scaffolds created within the Genomedata archive and must be consistent between these sequence files and the data loaded later with genomedata-load-data. See this example for details.
Usage: genomedata-load-assembly [OPTION]... GENOMEDATAFILE SEQFILE...
Options:
-g, --gap-length XXX: Implement this.
--version show program's version number and exit
-h, --help show this help message and exit
This command opens the specified tracks in the Genomedata archive, allowing data for those tracks to be loaded with genomedata-load-data.
Usage: genomedata-open-data [OPTION]... GENOMEDATAFILE TRACKNAME...
Options:
--version show program's version number and exit
-h, --help show this help message and exit
This command loads data from stdin into Genomedata under the given trackname. The input data must be in one of these supported datatypes: WIG, BED3+1, bedGraph. The chromosome/scaffold references in these files must match the sequence identifiers in the sequence files loaded with genomedata-load-assembly. See this example for details. A chunk-size can be specified to control the size of hdf5 chunks (the smallest data read size, like a page size). Larger values of chunk-size can increase the level of compression, but they also increase the minimum amount of data that must be read to access a single value.
BED3+1 format is interpreted the same ways as bedGraph, except that the track definition line is not required.
Usage: genomedata-load-data [OPTION...] GENOMEDATAFILE TRACKNAME
Loads data into Genomedata format
Takes track data in on stdin
-c, --chunk-size=NROWS Chunk hdf5 data into blocks of NROWS. A higher
value increases compression but slows random
access. Must always be smaller than the max size
for a dataset. [default: 10000]
-?, --help Give this help list
--usage Give a short usage message
-V, --version Print program version
Mandatory or optional arguments to long options are also mandatory or optional
for any corresponding short options.
Closes the specified Genomedata arhive.
Usage: genomedata-close-data [OPTION]... GENOMEDATAFILE
Options:
--version show program's version number and exit
-h, --help show this help message and exit
Erases all data associated with the specified tracks, allowing the data to then be replaced. The pipeline for replacing a data track is:
Usage: genomedata-erase-data [OPTION]... GENOMEDATAFILE TRACKNAME...
Erase the specified tracks from the Genomedata archive in such a way that
the track can be replaced (via genomedata-load-data).
Options:
--version show program's version number and exit
-h, --help show this help message and exit
-v, --verbose Print status updates and diagnostic messages
This command displays information about a genomedata archive. Running the following command:
genomedata-info tracknames_continuous genomedata
displays the list of continous tracks. Running:
genomedata-info contigs genomedata
displays the list of contigs in BED format (0-based, half-open indexing).
Usage: genomedata-info [OPTION]... CMD ARCHIVE
Options:
--version show program's version number and exit
-h, --help show this help message and exit
Prints data from a genomedata archive, for the track TRACKNAME, on CHROM, in the region BEGIN-END (0-based, half-open indexing). Intended as a convenience function only; this is much slower than the Python interface, so it should not be used for large regions.
Usage: genomedata-query [OPTION]... ARCHIVE TRACKNAME CHROM BEGIN END
Options:
--version show program's version number and exit
-h, --help show this help message and exit
The Genomedata package is designed to be used from a variety of scripting languages, but currently only exports the following Python API.
The root level of the genomedata object hierarchy.
If you use this as a context manager, it will keep track of any open Chromosomes and close them (and the Genome object) for you later when the context is left:
with Genome("/path/to/genomedata") as genome:
chromosome = genome["chr1"]
[...]
If not used as a context manager, you are responsible for closing the Genomedata archive once you are done:
>>> genome = Genome("/path/to/genomedata")
>>> chromosome = genome["chr1"]
[...]
>>> genome.close()
Create a Genome object from a genomdata archive.
| Parameters: |
|
|---|
Example:
>>> genome = Genome("./genomedata.ctcf.pol2b/")
>>> genome
Genome("./genomedata.ctcf.pol2b/")
[...]
>>> genome.close()
>>> genome = Genome("./cat_chipseq.genomedata", mode="r")
[...]
>>> genome.close()
Return next chromosome, in sorted order, with memoization.
Example:
for chromosome in genome:
print chromosome.name
for supercontig, continuous in chromosome.itercontinuous():
[...]
Return a reference to a chromosome of the given name.
| Parameters: | name (string) – name of the chromosome (e.g. “chr1” if chr1.genomedata is a file in the Genomedata archive or chr1 is a top-level group in the single-file Genomedata archive) |
|---|---|
| Returns: | Chromosome |
Example:
>>> genome["chrX"]
<Chromosome 'chrX', file='/path/to/genomedata/chrX.genomedata'>
>>> genome["chrZ"]
KeyError: 'Could not find chromosome: chrZ'
Add a new track
The Genome object must have been created with :param mode:=”r+”. Behavior is undefined if this is not the case.
Currently sets the dirty bit, which can only be erased with genomedata-close-data
Close this Genomedata archive and any open chromosomes
If the Genomedata archive is a directory, this closes all open chromosomes. If it is a single file, this closes that file. This should only be used if Genome is not a context manager (see Genome). The behavior is undefined if this is called while Genome is being used as a context manager.
Erase all data for the given track across all chromosomes
The Genome object must have been created with :param mode:=”r+”. Behavior is undefined if this is not the case.
Currently sets the dirty bit, which can only be erased with genomedata-close-data
Genomedata format version
None means there are no chromosomes in it already.
Return the column index of the trackname in the continuous data.
| Parameters: | trackname (string) – name of data track |
|---|---|
| Returns: | integer |
This is used for efficient indexing into continuous data:
>>> col_index = genome.index_continuous("sample_track")
>>> data = genome["chr3"][100:150, col_index]
although for typical use, the track can be indexed directly:
>>> data = genome["chr3"][100:150, "sample_track"]
Return the number of datapoints in each track.
| Returns: | a numpy.array vector with an entry for each track. |
|---|
The Genomedata object corresponding to data for a given chromosome.
Usually created by keying into a Genome object with the name of a chromosome, as in:
>>> with Genome("/path/to/genomedata") as genome:
... chromosome = genome["chrX"]
... chromosome
...
<Chromosome 'chrX', file='/path/to/genomedata/chrX.genomedata'>
Return next supercontig in chromosome.
New in version 1.2: Supercontigs are ordered by start index
Seldom used in favor of the more direct: Chromosome.itercontinuous()
Example:
>>> for supercontig in chromosome:
... supercontig # calls repr()
...
<Supercontig 'supercontig_0', [0:66115833]>
<Supercontig 'supercontig_1', [66375833:90587544]>
<Supercontig 'supercontig_2', [94987544:199501827]>
Return the continuous data corresponding to this bp slice
| Parameters: | key – base_key must index or slice bases track_key specify data tracks with index, slice, string, list of strings, list of indexes, or array of indexes |
|---|
| Returns: | numpy.array |
|---|
If slice is taken over or outside a supercontig boundary, missing data is filled in with NaN’s automatically and a warning is printed.
Typical use:
>>> chromosome = genome["chr4"]
>>> chromosome[0:5] # Get all data for the first five bases of chr4
>>> chromosome[0, 0:2] # Get data for first two tracks at chr4:0
>>> chromosome[100, "ctcf"] # Get "ctcf" track value at chr4:100
Return the attributes for this Chromosome.
This may also include Genome-wide attributes if the archive is implemented as a directory.
Close the current chromosome file.
This only needs to be called when Genomedata files are manually opened as Chromosomes. Otherwise, Genome.close() should be called to close any open chromosomes or Genomedata files. The behavior is undefined if this is called on a Chromosome accessed through a Genome object. Using Genomedata as a context manager makes life easy by memoizing chromosome access and guaranteeing the proper cleanup. See Genome.
Return the index past the last base in this chromosome.
For Genome.format_version > 0, this will be the number of bases of sequence in the chromosome. For == 0, this will be the end of the last supercontig.
This is the end in half-open coordinates, making slicing simple:
>>> chromosome.seq[chromosome.start:chromosome.end]
Return the column index of the trackname in the continuous data.
| Parameters: | trackname (string) – name of data track |
|---|---|
| Returns: | integer |
This is used for efficient indexing into continuous data:
>>> chromosome = genome["chr3"]
>>> col_index = chromosome.index_continuous("sample_track")
>>> data = chromosome[100:150, col_index]
although for typical use, the track can be indexed directly:
>>> data = chromosome[100:150, "sample_track"]
Return a generator over all supercontig, continuous pairs.
New in version 1.2: Supercontigs are ordered by increasing supercontig.start.
This is the best way to efficiently iterate over the data since all specified data is in supercontigs:
for supercontig, continuous in chromosome.itercontinuous():
print supercontig, supercontig.start, supercontig.end
[...]
See Genome.maxs
See Genome.mins
Return the genomic sequence of this chromosome.
If the index or slice spans a non-supercontig range, N’s are inserted in place of the missing data and a warning is issued.
Example:
>>> chromosome = genome["chr1"]
>>> for supercontig in chromosome:
... print repr(supercontig)
...
<Supercontig 'supercontig_0', [0:121186957]>
<Supercontig 'supercontig_1', [141476957:143422081]>
<Supercontig 'supercontig_2', [143522081:247249719]>
>>> chromosome.seq[0:10].tostring() # Inside supercontig
'taaccctaac'
>>> chromosome.seq[121186950:121186970].tostring() # supercontig boundary
'agAATTCNNNNNNNNNNNNN'
>>> chromosome.seq[121186957:121186960].tostring() # not in supercontig
UserWarning: slice of chromosome sequence does not overlap any supercontig (filling with 'N')
'NNN'
The entire sequence for a chromosome can be retrieved with:
>>> chromosome.seq[chromosome.start:chromosome.end]
Return the index of the first base in this chromosome.
For Genome.format_version > 0, this will always be 0. For == 0, this will be the start of the first supercontig.
See Genome.sums
Return the supercontig that contains this range if possible.
| Returns: | Supercontig |
|---|
Indexable with a slice or simple index:
>>> chromosome.supercontigs[100]
[<Supercontig 'supercontig_0', [0:66115833]>]
>>> chromosome.supercontigs[1:100000000]
[<Supercontig 'supercontig_0', [0:66115833]>, <Supercontig 'supercontig_1', [66375833:90587544]>, <Supercontig 'supercontig_2', [94987544:199501827]>]
>>> chromosome.supercontigs[66115833:66375833] # Between two supercontigs
[]
A container for a segment of data in one chromosome.
Implemented via a HDF5 Group
Return the underlying continuous data in this supercontig. To read the whole dataset into memory as a numpy.array, use continuous.read()
| Returns: | tables.EArray |
|---|
Return the index past the last base in this supercontig.
This is the end in half-open coordinates, making slicing simpler:
>>> supercontig.seq[supercontig.start:supercontig:end]
Project chromsomal coordinates to supercontig coordinates.
| Parameters: |
|
|---|---|
| Returns: | integer |
See Chromosome.seq.
If you find yourself creating many Genomedata archives on the same genome, it might be useful to save a copy of an archive after you load sequence, but before you load any data. Obviously, you can only do this if you use the fine-grained workflow of genomedata-load-assembly, genomedata-open-data, genomedata-load-data, and genomedata-close-data.
Genomedata uses an HDF5 data store. The data is stored in chunks. The chunk size is 10,000 bp and one data track of 32-bit single-precision floats, which makes the chunk 40 kB. Each chunk is gzip compressed so on disk it will be smaller. To read a single position you have to read its entire chunk off of the disk and then decompress it. There is a tradeoff here between latency and throughput. Larger chunk sizes mean more latency but better throughput and better compression.
The only disk storage overhead is that compression is slightly less efficient than compressing the whole binary data file when you break it into chunks. This is far outweighed by the efficient random access capability. If you have different needs, then it should be possible to change the chunk shape (genomedata.CONTINUOUS_CHUNK_SHAPE) or compression method (genomedata._util.FILTERS_GZIP).
The memory overhead is dominated by the chunk cache defined by PyTables. On the version of PyTables we use, this is 2 MiB. You can change this by setting tables.parameters.CHUNK_CACHE_SIZE.
There is currently an interaction between Genomedata and PyTables that can result in the emission of PerformanceWarnings when a Genomedata file is opened. These can be ignored. We would like to fix these at some point.
To stay informed of new releases, subscribe to the moderated genomedata-announce mailing list (mail volume very low):
For discussion and questions about the use of the Genomedata system, there is a genomedata-users mailing list:
For issues related to the use of Genomedata on Mac OS X, please use the above mailing list or contact Jay Hesselberth <jay dot hesselberth at ucdenver dot edu>.
If you want to report a bug or request a feature, please do so using our issue tracker:
For other support with Genomedata, or to provide feedback, please write contact the authors directly. We are interested in all comments regarding the package and the ease of use of installation and documentation.