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Namespaces

Overview

This document describes how the Namespaces are processed. Most Gene/RNA/Protein Namespaces also include orthology equivalences.

The namespaces are collected from their original databases, e.g. the ChEBI database. They are then converted into terminology and orthology JSON files as described in the BELBio Schema Repository <https://github.com/belbio/schemas/tree/master/schemas>.

The namespace individual entries are stored in Elasticsearch to provide strong, scalable, and fast search capabilities. The namespace equivalents and orthologies if available are stored in ArangoDB for graph queries.

Processing Overview

  1. Run the Namespace script (for download and source file processing) to generate each Namespace <prefix>.jsonl.gz files in tools/namespaces
    1. First downloads original Namespace database files such as Entrez Gene and compresses them using gzip if not already gzipped.
      1. Only if it is newer than the prior download if determinable (some FTP servers don't make filedates available)
      2. If source file modification dates are indeterminable, it will download if the local file is older than 7 days (configurable in belbio_conf.yml <http://bel.readthedocs.io/en/latest/configuration.html>) - this is also adjustable per namespace script.
    2. Namespace script then processes the original source files to create the term data, equivalence and hierarchy
    3. Namespace script writes out each term into a gzipped JSONL file using the terminology schema
  2. Load namespaces into Elasticsearch
  1. After setting up Elasticsearch index
  2. Run tools/load/load_elasticsearch.py -a
  1. Load equivalence files
  1. Generate equivalence files to load into ArangoDB from <term>.jsonl.gz files
  2. Load equivalence files into ArangoDB using tools/load/load_arango.py
  1. Run Orthology scripts in tools/orthologs
  1. First Download Original Orthology database files and compress using gzip
  1. If it is newer than prior download if determinable (some FTP servers don't make file modification dates available) (orthology and terminology source files use same download location so will only download once if file(s) is used by both terminology and orthology scripts)
  2. If source file modification dates are indeterminable, it will download if the local file is older than 7 days (configurable in belbio_conf.yml <http://bel.readthedocs.io/en/latest/configuration.html>) - this is also adjustable per namespace script.
  1. Orthology script then processes the original source files to create the orthologous relationships
  2. Orthology script writes out each orthology into a gzipped JSONL file using the orthologs schema
  3. Load orthology datasets into ArangoDB using tools/load/load_arango.py

Namespace Scripts

Each namespace script is an independent script. Most do utilize some utility functions from a utility library supporting the namespace and orthology scripts.

Note

Namespace values (NSArg) need to be quoted if they contain whitespace, comma or ')'. This is due to how BEL is parsed. An NSArg (namespace:term, e.g. namespace argument of a BEL function) is parsed by looking for an ALLCAPS namespace prefix, colon and then a term name. The parsing continues for the term name until we find a space, comma or end parenthesis ')'. If the term contains any of those characters, it has to be quoted using double-quotes.

Note

Any character except an un-escaped double quote can be in the NSArg if it is quoted including spaces, commas and ')'.

Generally the namespace scripts do two main things:

  1. Download the namespace source datafiles
  2. Build the <term>.jsonl.gz file

All of the namespace scripts will be stored in the resource_tools/namespace directory. Any *.py files in that directory will be run to (re-)create the <namespace>.jsonl.gz files. The namespace scripts will create the <namespace>.jsonl.gz files in the resource_tools/data/namespace directory. Any *.jsonl.gz files will be loaded into Elasticsearch into the namespace index.

Taxonomy Terminology

Taxonomy IDs are based on the NCBI Taxonomy. Taxonomy is treated just like other terminologies with additional features of taxonomy_name object and taxonomy_rank (kingdom, ..., genus, species). The Taxonomy terminology script has to be run first as it creates the taxonomy_labels.json.gz file which is used by all terminologies that stores species_id and species_label in the <term>.jsonl.gz files.

The taxonomy_labels.json.gz file is a map (dictionary/hash) of all of the TAX:<int> versus labels but only for taxonomy entries with taxonomy_rank: "species". Note: It may be necessary to add labels to this file for entries with non-species taxonomy_rank as several EntrezGene and SwissProt namespace entries do not have labels in this file.

The Taxonomy Namespace prefix is 'TAX'. Humans have the taxonomy id of TAX:9606 with a custom label of 'human'.

Custom labels for specific species are sourced from the taxonomy_labels.yaml file adjacent to the taxonomy.py terminology script. Custom labels file looks like:

# Override taxonomy label
# taxonomy_src_id: label
---
9606: human
10090: mouse
10116: rat
7955: zebrafish

Orthology Scripts

Orthology Gene/Protein IDs collected from their source files need to be converted to the canonical Namespace for Genes/Proteins (currently Entrez Gene, prefix EG) prior to loading into ArangoDB TODO. This will save time in processing through the equivalence edges.

Terminology and Orthology Schemas

Schemas for terminologies and orthologies are kept in the BELBio Schema Repository.

Elasticsearch Index

The Elasticsearch index map is in the es_mapping_term.yaml file and the index is created using the setup_es.py script. This setup_es.py script must be run before loading the terminologies the first time. It will delete the terms index if it already exists. Note: Need to setup an A/B index option so that we can switch the index alias to a new terms index.

ArangoDB

A 'bel' database is created and the following collections are added and loaded:

  1. ortholog_nodes
  2. ortholog_edges
  3. equivalence_nodes
  4. equivalence_edges

These collections of nodes and edges allow equivalence and orthology queries to be run against the bel ArangoDB database.

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