Abstract
Whole-genome comparisons among mammalian and other eukaryotic organisms have revealed that they contain large quantities of conserved non—protein-coding sequence. Although some of the functions of this non-coding DNA have been identified, there remains a large quantity of conserved genomic sequence that is of no known function. Moreover, the task of delineating the conserved sequences is non-trivial, particularly when some sequences are conserved in only a small number of lineages. Sequence segmentation is a statistical technique for identifying putative functional elements in genomes based on atypical sequence characteristics, such as conservation levels relative to other genomes, GC content, SNP frequency, and potentially many others. The publicly available program changept and associated programs use Bayesian multiple change-point analysis to delineate classes of genomic segments with similar characteristics, potentially representing new classes of non-coding RNAs (contact web site: http://silmaril.math.sci.qut.edu.au/~keith/).
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Acknowledgments
The author thanks Peter Adams for assistance in running simulations; Stuart Stephen for assisting in the development of much of the code; Benjamin Goursaud and Rachel Crehange for assisting in the generalization of the code for multiple data types; and John Mattick, Kerrie Mengersen, Chris Ponting, and Mark Borodovski for helpful discussions. This work was partially funded by Australian Research Council (ARC) Discovery Grants DP0452412 and DP0556631 and a National Health and Medical Research Council (NHMRC) grant entitled “Statistical methods and algorithms for analysis of high-throughput genetics and genomics platforms” (389892).
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Keith, J.M. (2008). Sequence Segmentation. In: Keith, J.M. (eds) Bioinformatics. Methods in Molecular Biology™, vol 452. Humana Press. https://doi.org/10.1007/978-1-60327-159-2_11
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DOI: https://doi.org/10.1007/978-1-60327-159-2_11
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