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bionetcomp-1.1
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BioNetComp: A Python package for biological network comparison from STRING database.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bionetcomp-1.1 |
| نام | bionetcomp |
| نسخه کتابخانه | 1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Luca Miguel de Carvalho |
| ایمیل نویسنده | lucasmigueel@gmail.com |
| آدرس صفحه اصلی | https://github.com/lmigueel/bionetcomp/ |
| آدرس اینترنتی | https://pypi.org/project/bionetcomp/ |
| مجوز | GNU General Public License v3.0 |
# BioNetComp
[](https://pypi.org/project/bionetcomp/)
BioNetComp: a Python package for biological network generation and comparison
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version 1.1
- [Installation](#installing)
- [Overview](#overview)
- [Workflow](#workflow)
- [Command-line interface](#command-line-interface)
- [Python library usage](#python-library-usage)
- [Examples](#examples)
# Installing
To build and install from source, run
```shell
python setup.py install
```
You can also install from pip with
```shell
pip install bionetcomp
```
# Overview
BioNetComp compares two biologial networks from a list of genes/proteins. It provides both an easy-to-use object-oriented Python API and a command-line interface (CLI) for network comparison and post-analysis.
# Workflow
BioNetComp contains a flowchart designed to provide a structured comparative approach between two biological networks through the STRING database, as well as metrics and comparative reports, in addition to network visualizations.
BioNetComp features include:
1. A text file containing the list of nodes and total edges, differentiated by color and presence and absence on the network;
2. A text file containing exclusive nodes of each network and those in common;
3. Exclusive networks and a final network plot, containing comparative information;
4. Network plot generated only by exclusive edges of each biological network;
5. Comparative graphics of the number of nodes and edges;
6. Exclusive comparison charts, such as the betweenness and closeness centrality boxplots;
7. Degree histogram chart and its boxplot for each network;
8. Enrichment of the entire network, but also exclusive and common nodes.
9. Betweenness and closeness centrality gene ranking;
10. Jaccard coefficient between networks applied to nodes and edges for dissimilarity observations.
# Command-line interface
BioNetComp can be executed from the command line using the bionetcomp command. It takes two list files, output folder name and organism taxid (based on STRING) as input and outputs a several analysis of the biological network generated. You also may change the STRING interaction score threshold and enrichment cutoff.
```
usage: bionetcomp [-h] --in1 IN1 --in2 IN2 --taxid TAXID --output_folder
OUTPUT_FOLDER [--fdr FDR] [--threshold THRESHOLD]
optional arguments:
-h, --help show this help message and exit
--in1 IN1 File containing a first gene list.
--in2 IN2 File containing a second gene list.
--taxid TAXID STRING taxonomy ID. Ex: 9606
--output_folder OUTPUT_FOLDER Output folder
--fdr FDR FDR cutoff for enrichment analysis
--threshold THRESHOLD Threshold for STRING interaction score
example: python3 bionetcomp.py --in1 list1.txt --in2 list2.txt --output output_folder --taxid 9606
```
In the output_folder, BioNetComp generates the following outputs:
Remember: 'network1' and 'network2' labels are associated to --in1 and --in2 list of genes, respectively. If there are no edges for network plots, or any nodes for enrichment, a warning will be shown for the user.
1. node_report.txt all nodes of both networks divided by network
2. edge_report.txt all edges of both networks divided by network
3. nodes_only_network1.txt list of nodes present only in network1
4. nodes_only_network2.txt list of nodes present only in network2
5. nodes_intersection_network12.txt list of nodes present in both networks
6. enrichr_network1_exclusive_nodes_enrichment.txt nodes_only_network1.txt enrichment
7. enrichr_network2_exclusive_nodes_enrichment.txt nodes_only_network2.txt enrichment
8. enrichr_intersection_nodes_enrichment.txt intersection nodes enrichment
9. enrichment_network1.txt enrichment for all nodes in network1
10. enrichment_network2.txt enrichment for all nodes in network2
11. Betweeness_Centrality_values_network1.txt betweeness centrality value for all nodes of network1
12. Betweeness_Centrality_values_network2.txt betweeness centrality value for all nodes of network2
13. Closeness_Centrality_values_network1.txt closeness centrality value for all nodes of network1
14. Closeness_Centrality_values_network2.txt closeness centrality value for all nodes of network2
15. comparative_metrics_BioNetComp.txt basic network metrics for both networks
16. betweenness_centrality_boxplot.png betweeness centrality boxplot
17. closeness_centrality_boxplot.png closeness centrality boxplot
18. network1_plot.png network1 plot
19. network2_plot.png network2 plot
20. network1_degree_hist.png degree histogram of network1
21. network2_degree_hist.png degree histogram of network2
22. only_network1_edges.png network developed from edges only present in network1
23. only_network2_edges.png network developed from edges only present in network2
24. nodes_edges.png barplot of comparative numbers of nodes and edges
25. jaccard_coefficient_nodes_and_edges.png jaccard coefficient plot for nodes and edges
26. final_network_plot.png final network plot colored by nodes and edges presence
Colors of the plots:
(i) In the final network, nodes in red, blue, and yellow represent the intersection nodes, exclusive nodes of network1, and exclusive nodes of network2, respectively.
(ii) Edges in red represent the intersection ones.
(iii) In basics network plots, the degree of each node delimits the size of the node in the final network plot, allowing visualization of the hub nodes. The viridis pallete was used.
# Python library usage
BioNetComp generates a folder in currently folder with output_folder name.
To use as a Python library
```python
import bionetcomp
import os
# BioNetComp arguments
input1 = '/opt/data/input1.txt'
input2 = '/opt/data/input2.txt'
output_folder = 'bionetcomp_output'
taxid = 4932
threshold = 0.05
fdr = 0.05
#create the output folder
os.system("mkdir %s"%output_folder)
#develoment
network1 = bionetcomp.network_development(input1,taxid,threshold)
network2 = bionetcomp.network_development(input2,taxid,threshold)
#comparative metrics
g1, g2 = bionetcomp.comparative_metrics(network1,network2,output_folder)
#enrichment
bionetcomp.network_enrichment(input1,taxid,fdr,output_folder,"network1")
bionetcomp.network_enrichment(input2,taxid,fdr,output_folder,"network2")
#network comparison
bionetcomp.compare_networks(network1,network2,g1,g2,output_folder,fdr,taxid)
```
# Examples
A main folder contains two lists with 83 and 75 genes of *S. cerevisiae* (in1.txt and in2.txt files)
```shell
bionetcomp --in1 in1.txt --in2 in2.txt --taxid 4932 --output_folder bionetcomp_teste
```
نیازمندی
| مقدار | نام |
|---|---|
| - | matplotlib |
| - | numpy |
| - | pandas |
| >=2.5 | networkx |
| - | gseapy |
| - | requests |
| >=1.6.1 | scipy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3 | Python |
نحوه نصب
نصب پکیج whl bionetcomp-1.1:
pip install bionetcomp-1.1.whl
نصب پکیج tar.gz bionetcomp-1.1:
pip install bionetcomp-1.1.tar.gz