BioFlow 0.2.3
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BioFlow 0.2.3
[](https://github.com/chiffa/BioFlow/blob/master/License-new_BSD.txt)
[](https://www.python.org/downloads/release/python-2715/)
[](https://www.python.org/downloads/release/python-2715/)
BioFlow Project
Information Flow Analysis in biological networks
[](https://travis-ci.org/chiffa/BioFlow)
[](https://bioflow.readthedocs.io/en/latest/?badge=latest)
[](https://coveralls.io/github/chiffa/BioFlow?branch=master)
[](http://clonedigger.sourceforge.net/)
[](https://landscape.io/github/chiffa/BioFlow/master)
Description:
This project’s goal is to predict a systemic effect of massive gene
perturbation, whether triggered by a drug, causative mutation or a
disease (such as cancer or disease with complex genetic background).
It’s main intended uses are the reduction of high-throughput experiments
hit lists, in-silico prediction of de-novo drug toxicity based on their
protein binding profile and retrieval of most likely pathways explaining
a phenotype of interest from a complex genotype.
Its main advantage is the integration of quantitative computational
predictions with prior biological knowledge and ability to integrate
such diverse source of knowledge as databases, simulation, publication
data and expert knowledge.
Unlike similar solutions, it provides several levels of access to the
underlying data (integrated database instance with graph visualization,
courtesy of [neo4j graph platform](https://neo4j.com/), as well as
python [numpy](http://www.numpy.org/)/[scikits](https://www.scipy.org/)
sparse adjacency and laplacian graphs.
The application is currently under development (alpha), hence the API is
unstable and can be changed at any point without notice. If you are
using it, please pin the version/commit number. If you run into issues,
please fill the github ticket.
The license is BSD 3-clause, in case of academic usage, please cite the
url of this repository (publication is in preparation). The full API
documentation is available at
[readthedocs.org](http://bioflow.readthedocs.org/en/latest/).
Installation walk-through:
### Ubuntu desktop:
Install the Anaconda python 2.7 and make it your default python. The
full process is explained
[here](https://docs.anaconda.com/anaconda/install/linux/)
Isnstall libsuitesparse:
> apt-get -y install libsuitesparse-dev
Install neo4j:
> wget -O - https://debian.neo4j.org/neotechnology.gpg.key | sudo apt-key add -
> echo ‘deb https://debian.neo4j.org/repo stable/’ | sudo tee /etc/apt/sources.list.d/neo4j.list
> sudo apt-get update
> sudo apt-get install neo4j
Install MongDB (Assuming Linux 18.04 - if not, see
[here](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/)):
> sudo apt-key adv –keyserver hkp://keyserver.ubuntu.com:80 –recv 9DA31620334BD75D9DCB49F368818C72E52529D4
> echo “deb [ arch=amd64 ] https://repo.mongodb.org/apt/ubuntu bionic/mongodb-org/4.0 multiverse” | sudo tee /etc/apt/sources.list.d/mongodb-org-4.0.list
> sudo apt-get update
> sudo apt-get install -y mongodb-org
For more information, refer to the [installation
guide](http://bioflow.readthedocs.org/en/latest/guide.html#installation-and-requirements)
Finally, install BioFlow :
> pip install BioFlow
Or, alternatively, in case command line interface is not desired:
> git clone https://github.com/chiffa/BioFlow.git
> cd <installation directory/BioFlow>
> pip install -r requirements.txt
### Docker:
If you want to build locally (notice you need to issue docker commands
with the actual docker-enabled user; usually prepending sudo to the
commands):
> cd <BioFlow installation folder>
> docker build -t
> docker run bioflow
> docker-compose build
> docker-compose up -d
If you want to pull from dockerhub or don’t have access to BioFlow
installation directory:
> wget https://github.com/chiffa/BioFlow/blob/master/docker-compose.yml
> docker-compose build
> docker-compose up -d
Usage walk-through:
> warning
>
> While BioFlow provides an interface to download the databases
> programmatically, the databases are subject to Licenses and Terms that
> it’s up to the end users to respect
For more information about data and config files, refer to the [data and
database
guide](http://bioflow.readthedocs.org/en/latest/guide.html#data-and-databases-setup)
### Python scripts:
This is the recommended method for using BioFlow.
Import the minimal dependencies:
> from bioflow.annotation_network.knowledge_access_analysis import auto_analyze as knowledge_analysis
> from bioflow.molecular_network.interactome_analysis import auto_analyze as interactome_analysis
> from bioflow.utils.io_routines import get_source_bulbs_ids
> from bioflow.utils.top_level import map_and_save_gene_ids, rebuild_the_laplacians
Set static folders and urls for the databases & pull the online
databases:
> set_folders(‘~/support’) # script restart here is required to properly update all the folders
> pull_online_dbs()
Set the organism (human, S. Cerevisiae):
> build_source_config(‘human’) # script restart here is required to properly update all the folders
Map the hits and the background genes (available through an experimental
technique) to internal IDs:
> map_and_save_gene_ids(‘path_to_hits.csv’, ‘path_to_background.csv’)
BioFlow expects the csv files to contain one gene per line. It is
capable of mapping genes based on the following ID types:
Gene names
HGCN symbols
PDB Ids
ENSEMBL Ids
RefSeq IDs
Uniprot IDs
Uniprot accession numbers
Preferred mapping approach is through HGCN symbols and Gene names.
Rebuild the laplacians (not required unless background Ids List has been
changed):
> rebuild_the_laplacians(all_detectable_genes=background_bulbs_ids)
Launch the analysis itself for the information flow in the interactome:
> interactome_analysis([hits_ids],
desired_depth=9,
processors=3,
background_list=background_bulbs_ids,
skip_sampling=False,
from_memoization=False)
Launch the analysis itself for the information flow in the annotation
network (experimental):
> knowledge_analysis([hits_ids],
desired_depth=20,
processors=3,
skip_sampling=False)
Where:
hits_ids
: list of hits
desired_depth
: how many samples we would like to generate to compare against
processors
: how many threads we would like to launch in parallel (in general 3/4 works best)
background_list
: list of background Ids
skip_sampling
: if true, skips the sampling of background set and retrieves stored ones instead
from_memoization
: if true, assumes the information flow for the hits sample has already been computed
BioFlow will print progress to the StdErr from then on and will output
to the user’s home directory, in a folder called ‘outputs_YYYY-MM_DD
<launch time>’:
.gdf file with the flow network and relevance statistics
(Interactome_Analysis_output.gdf)
visualisation of information flow through nodes in the null vs hits
sets based on the node degree
list of strongest hits (interactome_stats.tsv)
The .gdf file can be further analysed with more appropriate tools.
### Command line:
> warning
>
> Command line interface is currently unstable and is susceptible to
> throw opaque errors.
Setup environment (likely to take a while top pull all the online
databases): :
> bioflow initialize –~/data_store
> bioflow downloaddbs
> bioflow setorg human
> bioflow loadneo4j
Set the set of perturbed proteins on which we would want to base our
analysis :
> bioflow setsource /home/ank/source_data/perturbed_proteins_ids.csv
Build network interfaces :
> bioflow extractmatrix –interactome
> bioflow extractmatrix –annotome
Perform the analysis:
> bioflow analyze –matrix interactome –depth 24 –processors 4
> bioflow analyze –matrix annotome –depth 24 –processors 4
The results of analysis will be available in the output folder, and
printed out to the standard output.
### Post-processing:
The .gdf file format is one of the standard format for graph exchange.
It contains the following columns for the nodes:
node ID
information current passing through the node
node type
legacy_id (most likely Uniprot ID)
degree of the node
whether it is present or not in the hits list (source)
p-value, comparing the information flow through the node to the flow
expected for the random set of genes
-log10(p_value) (p_p-value)
rel_value (information flow relative to the flow expected for a
random set of genes)
std_diff (how many standard deviations above the flow for a random
set of genes the flow from a hits list is)
The most common pipleine involves using [Gephi open graph visualization
platform](https://gephi.org/):
Load the gdf file into gephy
Filter out all the nodes with information flow below 0.05 (Filters
> Atrributes > Range > current)
Perform clustering (Statistics > Modularity > Randomize & use
weights)
Filter out all the nodes below a significance threshold (Filters >
Attributes > Range > p-value)
Set Color nodes based on the Modularity Class (Nodes > Colors >
Partition > Modularity Class)
Set node size based on p_p-value (Nodes > Size > Ranking >
p_p-value )
Set text color based on whether the node is in the hits list (Nodes
> Text Color > Partition > source)
Set text size based on p_p-value (Nodes > Text Size > Ranking >
p_p-value)
Show the lables (T on the bottom left)
Set labes to the legacy IDs (Notepad on the bottom)
Perform a ForeAtlas Node Separation (Layout > Force Atlas 2 >
Dissuade Hubs & Prevent Overlap)
Adjust label size
Adjust labels position (Layout > LabelAdjust)
For more details or usage as a library, refer to the [usage
guide](http://bioflow.readthedocs.org/en/latest/guide.html#basic-usage).
License:
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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