AIMC Topic: Molecular Sequence Annotation
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A Gene Ontology Tutorial in Python.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
This chapter is a tutorial on using Gene Ontology resources in the Python programming language. This entails querying the Gene Ontology graph, retrieving Gene Ontology annotations, performing gene enrichment analyses, and computing basic semantic sim...
Gene Ontology: Pitfalls, Biases, and Remedies.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its s...
Semantic Similarity in the Gene Ontology.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Gene Ontology-based semantic similarity (SS) allows the comparison of GO terms or entities annotated with GO terms, by leveraging on the ontology structure and properties and on annotation corpora. In the last decade the number and diversity of SS me...
Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The Gene Ontology Consortium (GOC) produces a wealth of resources widely used throughout the scientific community. In this chapter, we discuss the different ways in which researchers can access the resources of the GOC. We here share details about th...
Community-Wide Evaluation of Computational Function Prediction.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experiments to determine the function of every single gene product. Therefore, ...
Evaluating Functional Annotations of Enzymes Using the Gene Ontology.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of int...
Evaluating Computational Gene Ontology Annotations.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computati...
How Does the Scientific Community Contribute to Gene Ontology?
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Collaborations between the scientific community and members of the Gene Ontology (GO) Consortium have led to an increase in the number and specificity of GO terms, as well as increasing the number of GO annotations. A variety of approaches have been ...
Computational Methods for Annotation Transfers from Sequence.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have...
Best Practices in Manual Annotation with the Gene Ontology.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The Gene Ontology (GO) is a framework designed to represent biological knowledge about gene products' biological roles and the cellular location in which they act. Biocuration is a complex process: the body of scientific literature is large and selec...
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