Gene Cluster Prediction and Its Application to Genome Annotation

Chapter

Abstract

Improvements in sequencing technology have made whole-genome sequencing a lot more accessible to researchers in the life sciences. There has been a huge explosion in genomic sequence data over recent years and automated genome-wide function annotation has become a great challenge. The most popular approaches for gene function assignment have been based on sequence similarity. However, homology-based methods are limited in cases where novel sequences show no significant sequence similarity to known genes. This has led to the exploration of innovative methods that make use of additional information such as co-localization, co-evolution and fusion to assign functions computationally. In the case of prokaryotic genomes, functionally related genes tend to be physically clustered together due to evolutionary pressure. Thus, such gene clusters provide effective clues for gene function assignment in prokaryotes. In this chapter, we survey a few of the prominent techniques in this area of research. We also perform simple experiments to detect gene clusters across a given set of genomes. Finally, we provide a few examples from the results of these experiments to show how gene cluster information can be applied to genome annotation and can resolve ambiguities in function assignment.

Keywords

Gene Cluster Gene Pair Pattern Mining Conservation Score Multiple Genome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.School of Informatics and Computing, Indiana UniversityBloomingtonUSA

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