Dataset Information

TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations.

ABSTRACT:

Summary

TreeGrafter is a new software tool for annotating protein sequences using pre-annotated phylogenetic trees. Currently, the tool provides annotations to Gene Ontology (GO) terms, and PANTHER family and subfamily. The approach is generalizable to any annotations that have been made to internal nodes of a reference phylogenetic tree. TreeGrafter takes each input query protein sequence, finds the best matching homologous family in a library of pre-calculated, pre-annotated gene trees, and then grafts it to the best location in the tree. It then annotates the sequence by propagating annotations from ancestral nodes in the reference tree. We show that TreeGrafter outperforms subfamily HMM scoring for correctly assigning subfamily membership, and that it produces highly specific annotations of GO terms based on annotated reference phylogenetic trees. This method will be further integrated into InterProScan, enabling an even broader user community.

Availability and implementation

TreeGrafter is freely available on the web at https://github.com/pantherdb/TreeGrafter, including as a Docker image.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Tang H

PROVIDER: S-EPMC6361231 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations.

Tang Haiming H Finn Robert D RD Thomas Paul D PD

Bioinformatics (Oxford, England) 20190201 3

<h4>Summary</h4>TreeGrafter is a new software tool for annotating protein sequences using pre-annotated phylogenetic trees. Currently, the tool provides annotations to Gene Ontology (GO) terms, and PANTHER family and subfamily. The approach is generalizable to any annotations that have been made to internal nodes of a reference phylogenetic tree. TreeGrafter takes each input query protein sequence, finds the best matching homologous family in a library of pre-calculated, pre-annotated gene trees ...[more]

PMID: 30032202

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Project description:MotivationOntologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product's function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology's representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible.ResultsTo do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed.ConclusionThis standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse.AvailabilityThe GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal.

Project description:BackgroundVarious measures of semantic similarity of terms in bio-ontologies such as the Gene Ontology (GO) have been used to compare gene products. Such measures of similarity have been used to annotate uncharacterized gene products and group gene products into functional groups. There are various ways to measure semantic similarity, either using the topological structure of the ontology, the instances (gene products) associated with terms or a mixture of both. We focus on an instance level definition of semantic similarity while using the information contained in the ontology, both in the graphical structure of the ontology and the semantics of relations between terms, to provide constraints on our instance level description.Semantic similarity of terms is extended to annotations by various approaches, either though aggregation operations such as min, max and average or through an extrapolative method. These approaches introduce assumptions about how semantic similarity of terms relates to the semantic similarity of annotations that do not necessarily reflect how terms relate to each other.ResultsWe exploit the semantics of relations in the GO to construct an algorithm called SSA that provides the basis of a framework that naturally extends instance based methods of semantic similarity of terms, such as Resnik's measure, to describing annotations and not just terms. Our measure attempts to correctly interpret how terms combine via their relationships in the ontological hierarchy. SSA uses these relationships to identify the most specific common ancestors between terms. We outline the set of cases in which terms can combine and associate partial order constraints with each case that order the specificity of terms. These cases form the basis for the SSA algorithm. The set of associated constraints also provide a set of principles that any improvement on our method should seek to satisfy.ConclusionWe derive a measure of semantic similarity between annotations that exploits all available information without introducing assumptions about the nature of the ontology or data. We preserve the principles underlying instance based methods of semantic similarity of terms at the annotation level. As a result our measure better describes the information contained in annotations associated with gene products and as a result is better suited to characterizing and classifying gene products through their annotations.

Dataset Information

TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations.

Summary

Availability and implementation

Supplementary information

Publications

TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations.

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