Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

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2007-01-01
Authors
Andorf, Carson
Honavar, Vasant
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Dobbs, Drena
University Professor Emeritus
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Computer Science

Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.

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The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.

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1969-present

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Genetics, Development and Cell Biology

The Department of Genetics, Development, and Cell Biology seeks to teach subcellular and cellular processes, genome dynamics, cell structure and function, and molecular mechanisms of development, in so doing offering a Major in Biology and a Major in Genetics.

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The Department of Genetics, Development, and Cell Biology was founded in 2005.

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Bioinformatics and Computational Biology
The Bioinformatics and Computational Biology (BCB) Program at Iowa State University is an interdepartmental graduate major offering outstanding opportunities for graduate study toward the Ph.D. degree in Bioinformatics and Computational Biology. The BCB program involves more than 80 nationally and internationally known faculty—biologists, computer scientists, mathematicians, statisticians, and physicists—who participate in a wide range of collaborative projects.
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Abstract

Background

Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.

Results

In a set of 211 previously annotated mouse protein kinases, we found that 201 of the GO annotations returned by AmiGO appear to be inconsistent with the UniProt functions assigned to their human counterparts. In contrast, 97% of the predicted annotations generated using a machine learning approach were consistent with the UniProt annotations of the human counterparts, as well as with available annotations for these mouse protein kinases in the Mouse Kinome database.

Conclusion

We conjecture that most of our predicted annotations are, therefore, correct and suggest that the machine learning approach developed here could be routinely used to detect potential errors in GO annotations generated by high-throughput gene annotation projects.

Comments

This article is from BMC Bioinformatics 8 (2007): 284, doi: 10.1186/1471-2105-8-284. Posted with permission.

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Mon Jan 01 00:00:00 UTC 2007
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