Toolbox is a repository encapsulating various scripts used in my research on the analysis of disease and drug related biological data sets. It contains generic utilities for data processing (e.g., parsing, network-based analysis, proximity, etc, ...).

Contents

• Background
• Parsers
• Wrappers
• Proximity
• Citation

The code here has been developed during the analysis of data in various projects such as

• BIANA (@javigx2 was the lead developer): Biological data analysis and network integration
• GUILD: Network-based disease-gene prioritization
• Proximity: A method to calculate distances between two groups of nodes in the network while correcting for degree biases (e.g., incompleteness or study bias)

The package mainly consists of two types of files:

• parser_{resource_name_to_be_parsed}.py
• {type_of_data/software}_utilities.py

For instance, parse_drugbank.py contains methods to parse DrugBank data base (v.3) XML dump and network_utilities.py contains methods related to network generation and analysis.

Parsers available for the following resources:

• Clinical trials
• CMAP
• CTD
• DailyMed
• DrugBank
• Drugrepurposing.info
• GAD
• GeneCards
• GDSC
• GO
• KEGG API
• KEGG Brite
• LabeledIn
• LINCS API
• MEDI
• Metab2Mesh
• MeSH
• MSigDB
• NCBI Gene
• NDFRT API
• OpenFDA API
• Orphanet
• OMIM
• SIDER
• SNOMEDCT
• STITCH
• STRING
• UMLS
• UniProt

The parsers are provided "as is" and might not work due to updates on the data format of these resources. Please contact me for suggestions, bug reports and enquiries.

wrappers.py provides an easy to use interface to various methods I commonly use. It is continuously under development. Currently it contains methods to

• Map UniProt, ENTREZ ids and gene symbols
• Creating networkx network from file
• Calculating proximity
• Calculating functional enrichment using FuncAssociate API

See below for python interface to run GUILD (assumes it is properly compiled and accessible at executable_path) using A and C as seeds and a toy network:

>>> from toolbox import wrappers
>>> file_name = "toy.sif"
>>> network = wrappers.get_network(file_name, only_lcc = True)
>>> nodes = set(network.nodes())
>>> seeds = ["A", "C"]
>>> node_to_score = dict((node, 1) for node in seeds)
>>> name = "sample_run"
>>> output_dir = "./"
>>> wrappers.run_guild(name, node_to_score, nodes, file_name, output_dir, executable_path)

After this command input node score file "sample_run.node" and output node score file "sample_run.ns" will be created in the current directory.

To replicate the analysis in the paper please refer to proximity repository.

See calculate_proximity method in wrappers.py for calculating proximity:

calculate_proximity(network, nodes_from, nodes_to, nodes_from_random=None, nodes_to_random=None, n_random=1000, min_bin_size=100, seed=452456)

For instance, to calculate the proximity from (A, C) to (B, D, E) in a toy network (given below):

>>> from toolbox import wrappers
>>> file_name = "toy.sif"
>>> network = wrappers.get_network(file_name, only_lcc = True)
>>> nodes_from = ["A", "C"]
>>> nodes_to = ["B", "D", "E"]
>>> d, z, (mean, sd) = wrappers.calculate_proximity(network, nodes_from, nodes_to, min_bin_size = 2)
>>> print (d, z, (mean, sd))
(1.0, 0.97823676194805476, (0.75549999999999995, 0.24993949267772786))
>>>

Toy network (toy.sif):

A 1 B
A 1 C
A 1 D
A 1 E
A 1 F
A 1 G
A 1 H
B 1 C
B 1 D
B 1 I
B 1 J
C 1 K
D 1 E
D 1 I
E 1 F

The inputs are the two groups of nodes and the network. The nodes in the network are binned such that the nodes in the same bin have similar degrees. For real networks, use a larger min_bin_size (e.g., 100). The random nodes matching the number and the degree of the nodes in the node sets are chosen using these bins. The average distance from the nodes in one set to the other is then calculated and compared to the random expectation (the distances observed in random groups).

• If you use biomedical data base parsers or proximity related methods please cite: Guney E, Menche J, Vidal M, Barabási AL. Network-based in silico drug efficacy screening. Nat. Commun. 7:10331 doi: 10.1038/ncomms10331 (2016). link

• If you use GUILD related methods please cite: Guney E, Oliva B. Exploiting Protein-Protein Interaction Networks for Genome-Wide Disease-Gene Prioritization. PLoS ONE 7(9): e43557 (2012). link