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Genome Analysis

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Distributed and Sequential Algorithms for Bioinformatics

Part of the book series: Computational Biology ((COBO,volume 23))

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Abstract

This chapter provides an aggregation of three problems associated with the coarse analysis of biological sequences at subsequence level: gene finding, genome rearrangement, and haplotype inference. Detecting the location of genes in DNA is needed to analyze them efficiently. Gene finding algorithms for this purpose can be classified as statistical and comparison-based methods. The first scheme searches statistically more frequently appearing sequences such as the start and terminating codons, and sequence repeats in or around the location of the genes. In comparison-based methods, our aim is to compare sequences and find their similar structures assuming genes are more conserved throughout the evolution. Subsequences of a genome undergo mutations and discovery of these chain of mutations provides information about the evolutionary process and also the disease state of an organism as some of these arrangements are assumed to be the causes of certain diseases. We review few algorithms that discover the rearrangement events in a genome. The last problem we study is the extraction of data of a single chromosome from the two chromosome data, called haplotype inference. We describe a simple algorithm based on maximum parsimony and a probabilistic algorithm for this purpose. Unfortunately, there are hardly any distributed algorithms for these three important tasks and we propose a distributed algorithm for genome rearrangement and two distributed algorithms for haplotype inference.

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  1. Computer Engineering Department, Izmir University, Uckuyular, Izmir, Turkey

    K. Erciyes

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  1. K. Erciyes
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Erciyes, K. (2015). Genome Analysis. In: Distributed and Sequential Algorithms for Bioinformatics. Computational Biology, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-24966-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-24966-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24964-3

  • Online ISBN: 978-3-319-24966-7

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