Data and databases setup:

Assembling the files required for the database creation:

In order to build the main knowledge repository, BioFlow will go and look for the following data repositories specified in the $BIOFLOWHOME/configs/main_configs.yaml file:

  • OBO 1.2 file of GO terms and relations,

    • dowloaded from: here
    • will download/look for for go.obo file at $DB_HOME$/GO/

  • UNIPROT-SWISSPROT .txt text database file

    • downloaded from here
    • will store/look for the master tab file at $DB_HOME$/Uniprot/uniprot_sprot.dat
    • will load the information specified by the NCBI tax id for the organism currently loaded

  • Reactome.org “Events in the BioPax level 3” file

    • downloaded from here
    • will store/look for the files relevant to the organism at $DB_HOME$/Reactome/

  • HiNT PPI binary interaction files for the organisms of interest

    • dowloaded from here, here and here
    • will store/look for the files SaccharomycesCerevisiaeS288C_binary_hq.txt, HomoSapiens_binary_hq.txt, MusMusculus_binary_hq.txt at the path $DB_HOME$/HiNT/

  • BioGRID ALL_ORGANISMS PPI file in the tab2 format

    • dowloaded from here
    • will store/look for the files at $DB_HOME$/BioGRID/

  • TRRUST literature-curated TF-target interaction files in tsv format

    • downloaded from here and here
    • will store/look for the files at $DB_HOME$/TFs/TRRUST

  • IntAct ComplexPortal tsv files

    • dowloaded from here and here
    • will store/look for the files at $DB_HOME$/ComplexPortal

  • Phosphosite protein kinase-subrstrate tsv files

    • downloaded from here
    • will store/look for the files at $DB_HOME$/PhosphoSite

It is possible to specify the file locations and identifiers manually, and then download and install them. This is to be used when the download locations for the files move.

Similarly, the configs file also controls the organism selection. Three organisms have provided configurations (human, mouse, S. Cerevisiae). Using the same pattern, other organisms can be configured, also the lack of data can be a problem (this is already the case for mouse - we recommend mapping the genes to human if the mouse is used as a model for the organism).

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

Adding new data to the main knowledge repository:

The easiest way to add new information to the main knowledge repository is by finding the nodes to which new knowledge will attach (provided by the convert_to_internal_ids function from the bioflow.neo4j_db.db_io_routines module for a lot of x-ref identifiers for physical entity nodes), and then process to add new relationships and nodes using the functions DatabaseGraph.link to add a connection between nodes and DatabaseGraph .create to add a new node. DatabaseGraph.attach_annotation_tag can be used in order to attach annotation tags to new nodes that can be searcheable from the outside. All functions can be batched (cf API documentation).

A new link will have a call signature of type link(node_id, node_id, link_type, {param: val}) ``, where node_ids are internal database ids for the nodes provided by the ``convert_to_internal_ids function, link_type is a link type that would be handy for you to remember (preferably in the snake_case). Two parameters are expected: source and parse_type. parse_type can only take a value in ['physical_entity_molecular_interaction', 'identity', 'refines', 'annotates', 'annotation_relationship', 'xref'], with 'xref' being reserved for the annotation linking.

A new node will have a call signature of type create(node_type, {param:val}) and return the internal id of the created node. node_type is a node type that would be handy for you to remember (preferably in the snake_case). Four paramters are expected: 'parse_type', 'source', 'legacyID' and 'displayName'. 'parse_type' can take only values in ['physical_entity', 'annotation', 'xref'], with 'xref' being reserved for the annotation linking. legacyID is the identifier of the node in the source database and displayName is the name of the biological knowledge node that that will be shown to the end user.

Main knowledge graph parsing:

Given the difference in the topology and potential differences in the underlying assumptions, we pull the interactome knowledge network (where all nodes map to molecular entities and edges - to physical/chemical interaction between them) and teh annotome knowledge network (where some nodes might be concepts used to understand the biological systems - such as ontology terms or pathways) separately.

The parse for interactome is performed by retrieving all the nodes and edges whose parse_type is physical_entity for nodes and physical_entity_molecular_interaction, identity or refines. The giant component of the interactome is then extracted and two graph matrices - adjacency and laplacian - are build for it. Weights between the nodes are set in an additive manner according to the policy supplied as the argument to the InteractomeInterafce .full_rebuild function or, in a case a more granular approach is needed to the InteractomeInterafce.create_val_matrix function. By default the active_default_<adj/lapl>_weighting_policy functions are used from the bioflow.algorithms_bank.weigting_policies module. Resulting matrices are stored in the InteractomeInterface.adjacency_matrix and InteractomeInterface.laplacian_matrix instance variables, whears the maps between the matrix indexes and maps are stored in the .neo4j_id_2_matrix_index and .matrix_index_2_neo4j_id variables.

The parse for the annotome is performed in the same way, but matching parse_type for nodes to physical_entity and annotation. In case of a proper graph build, this will result only in the edges of types annotates and annotation_relationship to be pulled. Weighting functions are used in the similar manner, as well as the mappings storage.

Custom weighting function:

In order to account for different possible considerations when deciding which nodes and connections are more likely to be included in hypothesis generation, we provide a possibility for the end user to use their own weight functions for the interactome and the annotome.

The provided functions are stored in bioflow.algorithms_bank.weighting_policies module. An expected signature of the function is starting_node, ending_node, edge > float, where starting_node and ending_node are of <neo4j-driver>.Node type, whereas edge is of the <neo4j-driver>.Edge type. Any properties available stored in the main knowledge repository (neo4j database) will be available as dict-like properties of node/edge objects (<starting/ending>_node['<property>']/edge['property']).

The functions are to be provided to the bioflow.molecular_network .InteractomeInterface.InteractomeInterface.create_val_matrix() method as <adj/lapl>_weight_policy_function for the adjacency and laplacian matrices respectively.

Custom flow calculation function:

In case a specific algorithms to generate pairs of nodes between which to calculate the information flow is needed, it can be assigned to the InteractomeInterface ._flow_calculation_method. It’s call signature should conform to the list, list, int -> list signature, where the return list is the list of pairs of (node_idx, weight) tuples. By default, the general_flow method from bioflow.algorithms_bank.flow_calculation_methods will be used. It will try to match the expected flow calcualtion method based on the parameters provided (connex within a set if the secondary set is empty/None, star-like if the secondary set only has one element, biparty if the secondary set has more than one element).

Similarly, methods to evaluate the number of operations and to reduce their number to a maximum ceiling with the optional int argument sparse_rounds needs to be assigned to the InteractomeInterface._ops_evaluation_method and InteractomeInterface ._ops_reduction_method. By default, the are evaluate_ops and reduce_ops from bioflow.algorithms_bank.flow_calculation_methods.

Custom random set sampling strategy:

In case a custom algorithm for the generation of the background sample needs to be implemented, it should be supplied to the InteractomeInterace.randomly_sample method as the sampling_policy argument.

It is expected to accept the an example of sample and secondary sample to match, background from which to sample, number of samples desired and finally a single string parameter modifying the way it works (supplied by the sampling_policy_options parameter of the InteractomeInterace.randomly_sample method).

By default, this functions implemented by the matched_sampling fundion in the bioflow.algorithms_bank.sampling_policies module.

Custom significance evaluation:

by default, auto_analyze functions for the interactome and the annotome will use the default compare_to_blank functions and seek to determine the significance of flow based on comparison of the flow achieved by nodes of a given degree in the real sample compared to the random “mock” samples. The comparison will be performed using Gumbel_r function fitted to the highest flows achieved by the “mock” runs.

As of now, to change the mode of statistical significance evaluation, a user will need to re-implement the compare_to_blank functions and mokey-patch them in the modules containing the auto_analyze function.