Metadata-Version: 1.1
Name: BioFlow
Version: 0.2.3
Summary: Information Flow Analysis in biological networks
Home-page: https://github.com/chiffa/BioFlow
Author: Andrei Kucharavy
Author-email: andrei.chiffa136@gmail.com
License: BSD
Description: [![License
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        BioFlow Project
        ===============
        
        Information Flow Analysis in biological networks
        
        [![Build
        Status](https://travis-ci.org/chiffa/BioFlow.svg?branch=master)](https://travis-ci.org/chiffa/BioFlow)
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        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:
        
        1)  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/)
        2)  Isnstall libsuitesparse:
        
                > apt-get -y install libsuitesparse-dev
        
        3)  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
        
        4)  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)
        
        5)  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).
        
Keywords: network analysis,systems biology,interactome,computational biology
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: Implementation :: CPython
