Welcome to MuG DMP API’s documentation!

Requirements and Installation

Requirements

Software

  • Mongo DB 3.2
  • Python 2.7.10+

Python Modules

  • pymongo
  • mongomock
  • h5py
  • numpy
  • pyBigWig
  • pysam

Installation

Directly from GitHub:

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git clone https://github.com/Multiscale-Genomics/mg-dm-api.git

Using pip:

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pip install git+https://github.com/Multiscale-Genomics/mg-dm-api.git

Documentation

To build the documentation:

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pip install Sphinx
pip install sphinx-autobuild
cd docs
make html

Data Management Plan API

Methods

class dmp.dmp.dmp(cnf_loc=u'', test=False)[source]

API for management of files within the VRE

add_file_metadata(user_id, file_id, key, value)[source]

Add a key value pair to the meta data for a file

This way a user is able to add extra information to the meta data to better describe the file.

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_id (str) – ID of the file. This is the value returned when a file is loaded into the DMP or is the _id for a given file when the files have been retrieved.
  • key (str) – Unique key for the identification of the extra meta data. If the key matches a value already in the meta data then it over-writes the current value.
  • value – Value to be stored for the given key. This can be a str, int, list or dict.
Returns:

This is an id for that file within the system and can be used for tracing this file and where it is used and where it has come from.
Return type:

str
get_file_by_file_path(user_id, file_path, rest=False)[source]

Get a list of the file dictionary objects given a user_id and file_path

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_path (str) – File path (see validate_file)
Returns:

file_path : str

Location of the file in the file system

file_type : str

File format (see validate_file)

data_type : str

The type of information in the file (RNA-seq, ChIP-seq, etc)

taxon_id : int

Taxon ID that the species that the file has been derived from

compressed : str

Type of compression (None, gzip, zip)

source_id : list

List of IDs of files that were processed to generate this file

meta_data : dict

Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

creation_time : list

Time at which the file was loaded into the system
Return type:

dict

Example

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from dmp import dmp
da = dmp()
da.get_files_by_file_path(<user_id>, <file_type>)
get_file_by_id(user_id, file_id, rest=False)[source]

Returns files data based on the unique_id for a given file

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_id (str) – Location of the file in the file system
Returns:

file_path : str

Location of the file in the file system

path_type : str

File or Folder

file_type : str

File format (see validate_file)

size : int

Size of the file

parent_dir : str

Location of the parent dir

data_type : str

The type of information in the file (RNA-seq, ChIP-seq, etc)

taxon_id : int

Taxon ID that the species that the file has been derived from

compressed : str

Type of compression (None, gzip, zip)

source_id : list

List of IDs of files that were processed to generate this file

meta_data : dict

Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

creation_time : list

Time at which the file was loaded into the system
Return type:

dict

Example

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from dmp import dmp
da = dmp()
da.get_file_by_id(<unique_file_id>)
get_file_history(user_id, file_id)[source]

Returns the full path of file_ids from the current file to the original file(s)

Needs work to define the format for how declaring the history is best

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_id (str) – ID of the file. This is the value returned when a file is loaded into the DMP or is the _id for a given file when the files have been retrieved.
Returns:

List of lists representing the adjancency of child and parent files.
Return type:

list

Example

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from dmp import dmp
da = dmp()
history = da.get_file_history("aLongString")
print history

Output: [['aLongString', 'parentOfaLongString'], ['parentOfaLongString', 'parentOfParent']]

These IDs can then be requested to ruturn the meta data and locations with the get_file_by_id method.

get_files_by_assembly(user_id, assembly, rest=False)[source]

Get a list of the file dictionary objects given a user_id and assembly

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • assembly (str) – Assembly that the species that the file has been derived from
Returns:

file_path : str

Location of the file in the file system

file_type : str

File format (see validate_file)

data_type : str

The type of information in the file (RNA-seq, ChIP-seq, etc)

taxon_id : int

Taxon ID that the species that the file has been derived from

compressed : str

Type of compression (None, gzip, zip)

source_id : list

List of IDs of files that were processed to generate this file

meta_data : dict

Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

creation_time : list

Time at which the file was loaded into the system
Return type:

dict

Example

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from dmp import dmp
da = dmp()
da.get_files_by_taxon_id(<user_id>, <taxon_id>)
get_files_by_data_type(user_id, data_type, rest=False)[source]

Get a list of the file dictionary objects given a user_id and data_type

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • data_type (str) – The type of information in the file (RNA-seq, ChIP-seq, etc)
Returns:

file_path : str

Location of the file in the file system

file_type : str

File format (see validate_file)

data_type : str

The type of information in the file (RNA-seq, ChIP-seq, etc)

taxon_id : int

Taxon ID that the species that the file has been derived from

compressed : str

Type of compression (None, gzip, zip)

source_id : list

List of IDs of files that were processed to generate this file

meta_data : dict

Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

creation_time : list

Time at which the file was loaded into the system
Return type:

dict

Example

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from dmp import dmp
da = dmp()
da.get_files_by_data_type(<user_id>, <data_type>)
get_files_by_file_type(user_id, file_type, rest=False)[source]

Get a list of the file dictionary objects given a user_id and file_type

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_type (str) – File format (see validate_file)
Returns:

file_path : str

Location of the file in the file system

file_type : str

File format (see validate_file)

data_type : str

The type of information in the file (RNA-seq, ChIP-seq, etc)

taxon_id : int

Taxon ID that the species that the file has been derived from

compressed : str

Type of compression (None, gzip, zip)

source_id : list

List of IDs of files that were processed to generate this file

meta_data : dict

Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

creation_time : list

Time at which the file was loaded into the system
Return type:

dict

Example

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from dmp import dmp
da = dmp()
da.get_files_by_file_type(<user_id>, <file_type>)
get_files_by_taxon_id(user_id, taxon_id, rest=False)[source]

Get a list of the file dictionary objects given a user_id and taxon_id

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • taxon_id (int) – Taxon ID that the species that the file has been derived from
Returns:

file_path : str

Location of the file in the file system

file_type : str

File format (see validate_file)

data_type : str

The type of information in the file (RNA-seq, ChIP-seq, etc)

taxon_id : int

Taxon ID that the species that the file has been derived from

compressed : str

Type of compression (None, gzip, zip)

source_id : list

List of IDs of files that were processed to generate this file

meta_data : dict

Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

creation_time : list

Time at which the file was loaded into the system
Return type:

dict

Example

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from dmp import dmp
da = dmp()
da.get_files_by_taxon_id(<user_id>, <taxon_id>)
get_files_by_user(user_id, rest=False)[source]

Get a list of the file dictionary objects given a user_id

Parameters:user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
Returns:List of dict objects for each file that has been loaded by a user.
Return type:list

Example

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from dmp import dmp
da = dmp()
da.get_files_by_user(<user_id>)
modify_column(user_id, file_id, key, value)[source]

Update a key value pair for the record

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_id (str) – ID of the file. This is the value returned when a file is loaded into the DMP or is the _id for a given file when the files have been retrieved.
  • key (str) – Unique key for the identification of the extra meta data. If the key matches a value already in the meta data then it over-writes the current value.
  • value – Value to be stored for the given key. This can be a str, int, list or dict.
Returns:

This is an id for that file within the system and can be used for tracing this file and where it is used and where it has come from.
Return type:

str
remove_file(user_id, file_id)[source]

Removes a single file from the directory. Returns the ID of the file that was removed

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_id (str) – ID of the file. This is the value returned when a file is loaded into the DMP or is the _id for a given file when the files have been retrieved.
Returns:

The file_id of the removed file.
Return type:

str

Example

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from dmp import dmp
da = dmp()
da.remove_file(<file_id>)
remove_file_metadata(user_id, file_id, key)[source]

Remove a key value pair from the meta data for a given file

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_id (str) – ID of the file. This is the value returned when a file is loaded into the DMP or is the _id for a given file when the files have been retrieved.
  • key (str) – Unique key for the identification of the extra meta data to be removed
Returns:

This is an id for that file within the system and can be used for tracing this file and where it is used and where it has come from.
Return type:

str
set_file(user_id, file_path, path_type, file_type=u'', size=0, parent_dir=u'', data_type=u'', taxon_id=u'', compressed=None, source_id=None, meta_data=None, **kwargs)[source]

Adds a file to the data management API.

Parameters:
  • user_id (str) – Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
  • file_path (str) – Location of the file in the file system
  • path_type (str) –
  • parent_dir (str) – _id of the parent directory
  • file_type (str) – File format (see validate_file)
  • size (int) – File size in bytes
  • data_type (str) – The type of information in the file (RNA-seq, ChIP-seq, etc)
  • taxon_id (int) – Taxon ID that the species that the file has been derived from
  • compressed (str) – Type of compression (None, gzip, zip)
  • source_id (list) – List of IDs of files that were processed to generate this file
  • meta_data (dict) –

    Dictionary object containing the extra data related to the generation of the file or describing the way it was processed

    assembly : string
    Dependent paramenter. If the sequence has been aligned at some point during the production of this file then the assembly must be recorded.
Returns:

This is an id for that file within the system and can be used for tracing this file and where it is used and where it has come from.
Return type:

str

Example

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from dmp import dmp
da = dmp()
unique_file_id = da.set_file(
    'user1', '/tmp/example_file.fastq', 'fastq', 'RNA-seq', 9606, None)

If there is a processed result of 1 or more files then these can be specified using the file_id:

>>> da.set_file(
    'user1', '/tmp/example_file.fastq', 'fastq', 'RNA-seq', 9606, None,
    source_id=[1, 2])

Meta data about the file can also be included to provide extra information about the file, origins or how it was generated:

>>> da.set_file('user1', '/tmp/example_file.fastq', 'fastq', 'RNA-seq',
    9606, None, meta_data={'assembly' : 'GCA_0000nnnn',
    'downloaded_from' : 'http://www.', })
static validate_file(entry)[source]

Validate that the required meta data for a given entry is present. If there is missing data then a ValueError excepetion is raised. This function checks that all required paths are defined and that when various selections are made then the correct matching data is also present

Parameters:entry (dict) –
user_id : str
Identifier to uniquely locate the users files. Can be set to “common” if the files can be shared between users
file_path : str
Location of the file in the file system
path_type : str
File or folder
file_type : str
File format (“amb”, “ann”, “bam”, “bb”, “bed”, “bt2”, “bw”, “bwt”, “cpt”, “csv”, “dcd”, “fa”, “fasta”, “fastq”, “gem”, “gff3”, “gz”, “hdf5”, “json”, ‘lif’, “pac”, “pdb”, “pdf”, “png”, “prmtop”, “sa”, “tbi”, “tif”, “tpr”, “trj”, “tsv”, “txt”, “wig”)
size : int
Size of the file in bytes
data_type : str
The type of information in the file (RNA-seq, ChIP-seq, etc)
taxon_id : int
Taxon ID that the species that the file has been derived from
compressed : str
Type of compression (None, gzip, zip)
source_id : list
List of IDs of files that were processed to generate this file
meta_data : dict
Dictionary object containing the extra data related to the generation of the file or describing the way it was processed assembly : string
Returns:
  • bool – Returns True if there are no errors with the entry
  • If there are issues with the entry then a ValueError is raised.

Data Management RESTful API

Methods

class dmp.rest.rest(cnf_loc='', test=False)[source]

API for management of files within the VRE

add_service(name, url, description, status=None)[source]

Add a service to the registry

Parameters:
  • name (str) – Unique name for the service
  • description (str) – Description defined by the service
  • url (str) – Base URL for the REST service.
  • status (str) – Service HTTP status code - up or down
Returns:

Entry ID
Return type:

str
get_available_services()[source]

List all services

Returns:List of dict objects for each service
Return type:list
get_down_services()[source]

List services that are NOT returning HTTP code 200

Returns:List of dict objects for each service
Return type:list
get_service(name)[source]

Retreive the full details about a service

Parameters:name (str) – Unique name for the service
Returns:
name: str
Unique name for the service
description: str
Description defined by the service
url: str
Base URL for the RESET service.
status: str
Service HTTP status code - up or down
Return type:dict
get_up_services()[source]

List services that are returning HTTP code 200

Returns:List of dict objects for each service
Return type:list
is_service(name)[source]

Identify if a service is already present in the registry

Parameters:name (str) – Unique name for the service
set_service_status(name, status)[source]

Update the status of the service if it is already present in the db.

Parameters:
  • name (str) – Unique name for the service
  • status (str) – Service HTTP status code - up or down
Returns:

True when done
Return type:

bool
update_service_url(name, url)[source]

Update the url of the service if it is already present in the db.

Parameters:
  • name (str) – Unique name for the service
  • url (str) – Base URL for the REST service.
Returns:

True when done
Return type:

bool

Custom Reader APIs

HDF5 Files

Hi-C Adjacency Files

class reader.hdf5_adjacency.adjacency(user_id, file_id, resolution=None, cnf_loc='')[source]

Class related to handling the functions for interacting directly with the HDF5 files. All required information should be passed to this class.

close()[source]

Close the HDF5 data file handle

get_chromosome_from_array_index(index)[source]

Identify the chromosome based on either the x or y coordinate in the array.

Parameters:index (int) – Location within the array
Returns:chr_id – Identity of the chromosome
Return type:str

Example

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from reader import adjacency
r = adjacency('test', '', 10000)
cid = r.get_chromosome_from_array_index(1234567890)
get_chromosome_parameters()[source]

Return a list of the available resolutions in a given HDF5 file

Returns:chromosomes : list chr_param : dict resolitions
Return type:dict

Example

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from reader import adjacency
r = adjacency('test', '', 10000)
value = r.get_chromosome_parameters()
get_chromosomes()[source]

List of chromosomes that have models at a given resolution

Returns:chromosomes – List of chromosomes at the set resolution
Return type:list
get_details()[source]

Return a list of the available resolutions in a given HDF5 file

get_range(chr_id, start, end, limit_chr=None, limit_start=None, limit_end=None, value_url='/api/getValue', no_links=None)[source]

Get the interactions that happen within a defined region on a specific chromosome. Returns inter and intra interactions with the defined region.

Parameters:
  • chr_id (str) – Chromosomal name
  • start (int) – Start position within the chromosome
  • end (int) – End position within the chromosome
  • limit_chr (str (Optional)) – Limit the results to a particular chromosome
  • limit_start (int (Optional)) – Limit the range start position on the limit_chr paramter
  • limit_end (int (Optional)) – Limit the range end position on the limit_chr parameter
  • value_url (str (Optional)) – Define a custom URL snippet for the location of the file if different from the defaul
  • no_links (bool (Optional)) – Will return the URL links to the individual points within the adjacency matrix. In cases where this generates a large number of points it is possible to turn off generating these links. Set this value to 1.
Returns:

log : list

List of messages about the state for debugging

results : list

List of values for given positions within the adjacency matrix
Return type:

dict

Example

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from reader import adjacency
r = adjacency('test', '', 10000)
value = r.get_range(2000000, 1000000)
get_resolution()[source]

List the current level of rseolution

Returns:resolution – Current level of resolution
Return type:int
get_resolutions()[source]

List resolutions that models have been generated for

Returns:list – Available levels of resolution that can be set
Return type:str
get_value(bin_i, bin_j)[source]

Get a specific value for a given dataset, resolution

Parameters:
  • bin_i (int) – Array position in the first dimension
  • bin_j (int) – Array position in the second dimension
Returns:

value – Value for a given cell in the adjacency array
Return type:

int

Example

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from reader import adjacency
r = adjacency('test', '', 10000)
value = r.get_value(2000000, 1000000)
set_resolution(resolution)[source]

Set, or change, the resolution level

Parameters:resolution (int) – Level of resolution

Hi-C Coordinate Files

class reader.hdf5_coord.coord(user_id, file_id, resolution=None, cnf_loc='')[source]

Class related to handling the functions for interacting directly with the HDF5 files. All required information should be passed to this class.

close()[source]

Tidy function to close file handles

get_centroids(region_id)[source]

List the centroid models for each cluster

Returns:centroids – List of the centroid models for each cluster
Return type:list
get_chromosomes()[source]

List of chromosomes that have models at a given resolution

Returns:chromosomes – List of chromosomes at the set resolution
Return type:list
get_clusters(region_id)[source]

List all clusters of models

Returns:clusters – List of models in each cluster
Return type:list
get_model(region_id, model_ids=None, page=0, mpp=10)[source]

Get the coordinates within a defined region on a specific chromosome. If the model_id is not returned the the consensus models for that region are returned

Parameters:
  • region_id (str) – Region ID
  • model_ids (list) – List of model IDs for the models that are required
  • page (int) – Page number
  • mpp (int) – Number of models per page (default: 10; max: 100)
Returns:

array –

model : dict
metadata : dict

Relevant extra meta data added by TADbit

object : dict

Key value pair of information about the region

models : list

List of dictionaries for each model

clusters : list

List of models for each cluster

centroids : list

List of all centroid models

restraints : list

List of retraints for each position

hic_data : dict

Hi-C model data

metadata : dict
model_count : int

Count of the number of models for the defined region ID

page_count : int

Number of pages
Return type:

list
get_models(region_id)[source]

List all models for a given region

Returns:model_id : int cluster_id : int
Return type:List
get_object_data(region_id)[source]

Prepare the object header data structure ready for printing

Parameters:region_id (int) – Region that is getting downloaded
Returns:objectdata – All headers and values required for the JSON output
Return type:dict
get_region_order(chr_id=None, region=None)[source]

List the regions on a given chromosome ID or region ID in the order that they are located on the chromosome

Parameters:
  • chr_id (str) – Chromosome ID
  • region (str) – Region ID
Returns:

region_id : str

List of the region IDs
Return type:

list
get_regions(chr_id, start, end)[source]

List regions that are within a given range on a chromosome

Parameters:
  • chr_id (str) – Chromosome ID
  • start (int) – Start position
  • end (int) – Stop position
Returns:

regions – List of region IDs whose parameters match those provided
Return type:

list
get_resolution()[source]

List the current level of rseolution

Returns:resolution – Current level of resolution
Return type:int
get_resolutions()[source]

List resolutions that models have been generated for

Returns:list – Available levels of resolution that can be set
Return type:str
set_resolution(resolution)[source]

Set, or change, the resolution level

Parameters:resolution (int) – Level of resolution

Text File Index

Lists all files that are available for a user in bed and wig formats and lists the files than have data in a given region so that only the required files are requested by the client

class reader.hdf5_reader.hdf5_reader(user_id, file_id, cnf_loc='')[source]

Class related to handling the functions for interacting directly with the HDF5 files. All required information should be passed to this class.

close()[source]

Tidy function to close file handles

Example

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from hdf5_reader import hdf5_reader
h5r = hdf5_reader('test')
h5r.close()
get_assemblies()[source]

List all assemblies for which there are files that have been indexed

Returns:assembly – List of assemblies in the index
Return type:list

Example

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from hdf5_reader import hdf5_reader
h5r = hdf5_reader('test')
h5r.assemblies()
get_chromosomes(assembly)[source]

List all chromosomes that are covered by the index

Parameters:assembly (str) – Genome assembly ID
Returns:chromosomes – List of the chromosomes for a given assembly in the index
Return type:list

Example

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from hdf5_reader import hdf5_reader
h5r = hdf5_reader('test')
asm = h5r.assemblies()
chr_list = h5r.get_chromosomes(asm[0])
get_files(assembly)[source]

List all files for an assembly. If files are missing they can either get loaded or the search can be performed directly on the bigBed files

Parameters:assembly (str) – Genome assembly ID
Returns:file_ids – List of file ids for a given assembly in the index
Return type:list

Example

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from hdf5_reader import hdf5_reader
h5r = hdf5_reader('test')
asm = h5r.assemblies()
file_list = h5r.get_files(asm[0])
get_regions(assembly, chromosome_id, start, end)[source]

List files that have data in a given region.

Parameters:
  • assembly (str) – Genome assembly ID
  • chromosome_id (str) – Chromosome names as listed by the get_files function
  • start (int) – Start position for the region of interest
  • end (int) – End position for the region of interest
Returns:

file_ids – List of the file_ids that have sequence features within the region of interest
Return type:

list

Example

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from hdf5_reader import hdf5_reader
h5r = hdf5_reader('test')
asm = h5r.assemblies()
file_list = h5r.get_chromosomes(asm[0], 1, 1000000, 1100000)

BigBed Files

class reader.bigbed.bigbed_reader(user_id, file_id, cnf_loc='')[source]

Class related to handling the functions for interacting directly with the BigBed files. All required information should be passed to this class.

close()[source]

Tidy function to close file handles

Example

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from reader.bigbed import bigbed_reader
bbr = bigbed_reader('test')
bbr.close()
get_chromosomes()[source]

List the chromosome names and lengths

Returns:chromosomes – Key value pair of chromosome name and the value is the length of the chromosome.
Return type:dict
get_header()[source]

Get the bigBed header

Returns:header
Return type:dict
get_range(chr_id, start, end, file_type='bed')[source]

Get entries in a given range

Parameters:
  • chr_id (str) – Chromosome name
  • start (int) – Start of the region to query
  • end (int) – End of the region to query
  • file_type (string (OPTIONAL)) – bed format returning the whole file as a string is the default option. list will return the bed rows but as a list of lists.
Returns:

  • bed (str (DEFAULT)) – List of strings for the rows in a bed file
  • bed_array (list) – List of lists of each row for the bed file format

BigWig Files

class reader.bigwig.bigwig_reader(user_id, file_id, cnf_loc='')[source]

Class related to handling the functions for interacting directly with the BigBed files. All required information should be passed to this class.

get_chromosomes()[source]

List the chromosome names and lengths

Returns:chromosomes – Key value pair of chromosome name and the value is the length of the chromosome.
Return type:dict
get_header()[source]

Get the bigWig header

Returns:header
Return type:dict
get_range(chr_id, start, end, file_type='wig')[source]

Get entries in a given range

Parameters:
  • chr_id (str) – Chromosome name
  • start (int) – Start of the region to query
  • end (int) – End of the region to query
  • format (string (OPTIONAL)) – wig format returning the whole file as a string is the default option. list will return the wig rows but as a list of lists.
Returns:

  • wig (str (DEFAULT)) – List of strings for the rows in a wig file
  • wig_array (list) – List of lists of each row for the wig file format

Tabix Files

class reader.tabix.tabix(user_id, file_id, cnf_loc='')[source]

Class related to handling the functions for interacting directly with the BigBed files. All required information should be passed to this class.

get_range(chr_id, start, end, file_type='gff3')[source]

Get entries in a given range

Parameters:
  • chr_id (str) – Chromosome name
  • start (int) – Start of the region to query
  • end (int) – End of the region to query
  • format (string (OPTIONAL)) – gff3 format returning the whole file as a string is the default option. list will return the gff3 rows but as a list of lists.
Returns:

  • gff3 (str (DEFAULT)) – List of strings for the rows in a gff3 file
  • wig_array (list) – List of each row for the gff3 file format

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