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2014 ; 30
(12
): i34-42
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Inferring gene ontologies from pairwise similarity data
#MMPMID24932003
Kramer M
; Dutkowski J
; Yu M
; Bafna V
; Ideker T
Bioinformatics
2014[Jun]; 30
(12
): i34-42
PMID24932003
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MOTIVATION: While the manually curated Gene Ontology (GO) is widely used,
inferring a GO directly from -omics data is a compelling new problem. Recognizing
that ontologies are a directed acyclic graph (DAG) of terms and hierarchical
relations, algorithms are needed that: analyze a full matrix of gene-gene
pairwise similarities from -omics data; infer true hierarchical structure in
these data rather than enforcing hierarchy as a computational artifact; and
respect biological pleiotropy, by which a term in the hierarchy can relate to
multiple higher level terms. Methods addressing these requirements are just
beginning to emerge-none has been evaluated for GO inference. METHODS: We
consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that
uniquely satisfy these requirements, compared with methods including standard
clustering. CliXO is a new approach that finds maximal cliques in a network
induced by progressive thresholding of a similarity matrix. We evaluate each
method's ability to reconstruct the GO biological process ontology from a
similarity matrix based on (a) semantic similarities for GO itself or (b) three
-omics datasets for yeast. RESULTS: For task (a) using semantic similarity, CliXO
accurately reconstructs GO (>99% precision, recall) and outperforms other
approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO
outperforms other methods using two -omics datasets and achieves ?30% precision
and recall using YeastNet v3, similar to an earlier approach (Network Extracted
Ontology) and better than LocalFitness or standard clustering (20-25% precision,
recall). CONCLUSION: This study provides algorithmic foundation for building gene
ontologies by capturing hierarchical and pleiotropic structure embedded in
biomolecular data.
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