FamPlex

FamPlex is a collection of resources for grounding protein families and complexes from text and describing their hierarchical relationships. Resources were developed by manual curation for use by natural language processing and biological modeling teams in the DARPA Big Mechanism and Communicating with Computers programs.

The open access publication describing FamPlex is available here:

• John A. Bachman, Benjamin M. Gyori and Peter K. Sorger, FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining. (2018) BMC Bioinformatics 19(1):248

Note: FamPlex used to be called Bioentities, and was renamed to better reflect the focus of the resource on protein families, complexes, and their lexical synonyms.

The repository contains the following files:

• relations.csv. Defines membership of specific genes/proteins in families and protein complexes. For example, PIK3CA isa PI3K_p110, where PI3K_p110 represents the class of catalytic subunits of PI3K; and PI3K_p110 partof PI3K, where PI3K represents a named complex consisting of a catalytic and regulatory subunit.

• equivalences.csv. Defines mappings between outside namespaces and the FamPlex namespace.

• entities.csv. A registry of the families and complexes defined in the FamPlex namespace.

• descriptions.csv. Descriptions and citations of some entities. Contains three columns: the FamPlex name, pipe separated reference CURIEs, and a textual description.

• grounding_map.csv. Explicit mapping of text strings to identifiers in biological databases.

• gene_prefixes.csv. Patterns of prefixes and suffixes on named entities.

• check_references.py. A script to check the integrity and completeness of the cross-references among the various files.

Entities, Relations and Equivalences

FamPlex contains resources for defining the relationships between genes/proteins and their membership in families and named complexes. Entities defined within the FamPlex namespace are listed in the entities.csv file. Cross-referencing the entries among the various files maintains consistency and prevents errors.

Relationships are defined in relations.csv as a triples using two relationships:

• isa, denoting membership in a family;

• partof, denoting membership in a protein complex.

These two relationships can be combined to capture complex hierarchical relationships, including sub-families (families within families) and complexes consisting of families of related subunits (e.g., PI3K, NF-kB).

The relations.csv file consists of five columns: (1) the namespace for the subject (e.g., HGNC for gene names, UP for Uniprot, or FPLX for the FamPlex namespace), (2) the identifier for the subject, (3) the relationship (isa or partof), (4) the namespace for the object, and (5) the identifier for the object.

The equivalences.csv file consists of three columns (1) the namespace of an outsite entity (e.g. BEL, PFAM), (2) the identifier of the outside entity in the namespace given in the first column, and (3) the equivalent entity in the FPLX namespace.

Grounding Map

Using mechanisms extracted from text mining to explain biological datasets requires that the entities in text are correctly grounded to the canonical names and IDs of genes, proteins, and chemicals. The problem is that simple lookups based on string matching often fail, particularly for protein families and named complexes, which appear frequently in text but lack corresponding entries in databases.

The grounding map addresses this by providing explicit grounding for frequently encountered entities in the biological literature. The text strings were drawn from a corpus of roughly 32,000 papers focused on growth factor signaling in cancer.

Entities are grounded to the following databases:

• Genes/proteins: Uniprot

• Chemicals: PubChem, CHEBI, and HMDB (for metabolites)

• Biological processes: GO and MeSH

• Protein families and named complexes: grounded to entities defined within the FamPlex repository in the entities.csv and relations.csv files, and to identifiers in PFAM and Interpro when possible.

Gene prefixes

The file gene_prefixes.csv enumerates prefixes and suffixes frequently appended to named entities. Some of these represent subtleties of experimental context (for example, that a protein of interest was tagged with a fluorescent protein in an experiment) that can safely be ignored when determining the logic of a sentence. However, others carry essential meaning: for example, a sentence describing the effect of 'AKT shRNA' on a downstream target has the opposite meaning of a sentence involving 'AKT', because 'AKT shRNA' represents inhibition of AKT by genetic silencing.

The patterns included in this file were found by manually reviewing 70,000 named entities extracted by the REACH parser from a corpus of roughly 32,000 papers focused on growth factor signaling.

Important note: the prefixes/suffixes may be applied additively, for example Myr-Flag-Akt1, indicating myristoylated, FLAG-tagged AKT1; or GFP-KRAS-G12V, indicating GFP-tagged KRAS with a G12V mutation.

The file contains three columns:

1. A case-sensitive pattern, e.g., mEGFP-{Gene name}, where {Gene name} represents a protein/gene name.
2. A category, described below.
3. Notes: spelling out acronyms, etc.

The category of the prefix/suffix determines whether it can be stripped off with minimal effect on the meaning, or whether it carries meaning that needs to be incorporated by a parser. The categories are as follows:

• experimental context. Protein tags, gene delivery techniques, etc. Can generally be ignored.

• species. Prefixes denoting human, mouse, primate, or mammalian versions of a gene. In most use cases can be ignored.

• generic descriptor. Additional words extracted by the entity recognizer that might designate that an entity is a "protein", a "protease", "transcription factor", etc. In most use cases can be ignored.

• mrna grounding. In most cases, entities can be grounded to proteins; in the case of {Gene name} mRNA, the entity must be explicitly grounded as an mRNA.

• protein state. Designate activation state, post-translational modification, cellular localization, etc. Must be captured by the parser.

• inhibition. Designate protein forms or interventions that represent an inhibition of the protein, that is, a loss-of-function experiment. Have the effect of switching the polarity of the extracted mechanism. For example, the sentence "DUSP6 silencing leads to MAPK1 phosphorylation" indicates that DUSP6 inhibits MAPK1 phosphorylation. Must be captured by the parser.

Python Package

FamPlex is available as a Python package on PyPi and can be installed with the command

$ pip install famplex

The package provides utilities for loading resource files and working with the graph of FamPlex relations. If the famplex repository has been cloned instead of pip installed, resource files will not be directly available to the package. Running the command

$ python update_resources.py

at the top level of the repo will copy the files to where they are needed. This should be run anytime the user has made changes to the top level resource files that they would like to be available in the package.

Contributing

Contributions are welcome! Please submit pull requests via the main sorgerlab/famplex repository: https://github.com/sorgerlab/famplex

If making additions or revisions to the CSV files take care to handle quotations and newlines correctly. This allows diffs to be handled correctly so changes can be reviewed. Please submit updates via pull requests on Github.

The CSV files in the FamPlex repo are set up to be edited natively using Microsoft Excel. The CSV files in the repo have Windows line terminators ('\r\n'), and are not ragged (i.e., missing entries in a row are padded out with empty strings to reach the full width of the longest row).

To preserve correct newlines, take the following steps:

1. If saving from Excel (Windows or Mac OS X), save to the "Windows Comma Separated (.csv)" format.

2. If reading (or writing) the files using a Python script, use the following set of csv format parameters::

   csvreader = csv.reader(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL, lineterminator='\r\n')

3. If editing the files on Linux, post-process files using unix2dos or a similar program.

When you're done, run tox to verify the integrity of the updates you've made and to generate new exports.