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2016 ; 7
(1
): 41
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Functional coherence metrics in protein families
#MMPMID27338101
Bastos HP
; Sousa L
; Clarke LA
; Couto FM
J Biomed Semantics
2016[Jun]; 7
(1
): 41
PMID27338101
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BACKGROUND: Biological sequences, such as proteins, have been provided with
annotations that assign functional information. These functional annotations are
associations of proteins (or other biological sequences) with descriptors
characterizing their biological roles. However, not all proteins are fully (or
even at all) annotated. This annotation incompleteness limits our ability to make
sound assertions about the functional coherence within sets of proteins.
Annotation incompleteness is a problematic issue when measuring semantic
functional similarity of biological sequences since they can only capture a
limited amount of all the semantic aspects the sequences may encompass. METHODS:
Instead of relying uniquely on single (reductive) metrics, this work proposes a
comprehensive approach for assessing functional coherence within protein sets.
The approach entails using visualization and term enrichment techniques anchored
in specific domain knowledge, such as a protein family. For that purpose we
evaluate two novel functional coherence metrics, mUI and mGIC that combine
aspects of semantic similarity measures and term enrichment. RESULTS: These
metrics were used to effectively capture and measure the local similarity cores
within protein sets. Hence, these metrics coupled with visualization tools allow
an improved grasp on three important functional annotation aspects: completeness,
agreement and coherence. CONCLUSIONS: Measuring the functional similarity between
proteins based on their annotations is a non trivial task. Several metrics exist
but due both to characteristics intrinsic to the nature of graphs and extrinsic
natures related to the process of annotation each measure can only capture
certain functional annotation aspects of proteins. Hence, when trying to measure
the functional coherence of a set of proteins a single metric is too reductive.
Therefore, it is valuable to be aware of how each employed similarity metric
works and what similarity aspects it can best capture. Here we test the behaviour
and resilience of some similarity metrics.
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