diffupath 0.0.1

Compute diffusion scores over networks

DiffuPath is an analytic tool for biological networks that connects the generic label propagation algorithms from DiffuPy to biological networks encoded in several formats such as Simple Interaction Format (SIF) or Biological Expression Language (BEL). For example, in the application scenario presented in the paper, we use three pathway databases (i.e., KEGG, Reactome and WikiPathways) and their integrated network retrieved from PathMe [1] to analyze three multi-omics datasets. However, other biological networks can be imported from the Bio2BEL ecosystem [2].

Installation

1. diffupath can be installed with the following commands:

$ python3 -m pip install git+https://github.com/multipaths/DiffuPath.git@master

2. or in editable mode with:

$ git clone https://github.com/multipaths/DiffuPath.git
$ cd diffupath
$ python3 -m pip install -e .

Requirements

diffupath requires the following libraries:

networkx (>=2.1)
pybel (0.13.2)
biokeen (0.0.14)
click (7.0)
tqdm (4.31.1)
numpy (1.16.3)
scipy (1.2.1)
scikit-learn (0.21.3)
pandas (0.24.2)
openpyxl (3.0.2)
plotly (4.5.3)
matplotlib (3.1.2)
matplotlib_venn (0.11.5)
bio2bel (0.2.1)
pathme
diffupy

Command Line Interface

The following commands can be used directly use from your terminal:

1. Download a database for network analysis.

The following command generates a BEL file representing the network of the given database.

$ python3 -m diffupath database network --database=<database-name>

To check the available databases, run the following command:

$ python3 -m diffupath database ls

2. Run a diffusion analysis

The following command will run a diffusion method on a given network with the given data

$ python3 -m diffupath diffusion run --network=<path-to-network-file> --input=<path-to-data-file> --method=<method>

Networks

You can choose networks to run diffusion methods in the following ways:

• Select a network representing an individual biological database

• Select multiple databases to generate a harmonized network

• Select from one of four predefined collections of biological databases representing a harmonized network

• Submit your own network from one of the accepted formats

Network Dumps

Because of the high computational cost of generating the kernel, we provide links to precalculated kernels for a set of networks representing biological databases:

• DrugBank [3]: drugbank.json

• Gene Ontology [4]: go.json

• HSDN [5]: hsdn.json

• KEGG [6]: kegg.json

• miRTarBase [7]: mirtarbase.json

• Reactome [8]: reactome.json

• SIDER [9]: sider.json

• WikiPathways [10]: wikipathways.json

• Collection #1 (KEGG, Reactome, WikiPathways)

• Collection #2 (KEGG, Reactome, WikiPathways and DrugBank)

• Collection #3 (KEGG, Reactome, WikiPathways, and MirTarBase)

Database	Description	Reference
DrugBank	Interactions between drugs and drug targets with over 10,000 drugs	[3]
Gene Ontology	Flexible hierarchy of tens of thousands of biological processes	[4]
HSDN	Associations between thousands of diseases with hundreds of symptoms	[5]
KEGG	Multi-omics interactions present in hundreds of biological pathways	[6]
miRTarBase	Experimentally validated interactions between miRNA and their targets	[7]
Reactome	Multi-omics interactions present in thousands of biological pathways	[8]
SIDER	Associations between over a thousand drugs and side effects	[9]
WikiPathways	Multi-omics interactions present in hundreds of biological pathways	[10]

Disclaimer

DiffuPath is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.

References

[1]

Domingo-Fernandez, D., Mubeen, S., Marin-Llao, J., Hoyt, C., et al. Hofmann-Apitius, M. (2019). PathMe: Merging and exploring mechanistic pathway knowledge.. BMC Bioinformatics, 20:243.

[2]

Hoyt, C. T., et al. (2019). Integration of Structured Biological Data Sources using Biological Expression Language. bioRxiv, 631812.

[3]

Wishart, D. S., et al. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.

[4]

Ashburner, M., et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics, 25(1), 25–9.

[5]

Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature communications, 5(1), 1-10.

[6]

Kanehisa, et al. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs.. Nucleic Acids Res. 45,D353-D361.

[7]

Huang, H. Y., et al. (2020). miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic acids research, 48(D1), D148-D154.

[8]

Fabregat, A et al. (2016). The Reactome Pathway Knowledgebase. Nucleic Acids Research 44. Database issue: D481–D487.

[9]

Kuhn, M., et al. (2016). The SIDER database of drugs and side effects. Nucleic Acids Research, 44(D1), D1075–D1079.

[10]

Slenter, D.N., et al. (2017). WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research, 46(D1):D661-D667.