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Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications by Albert Y. Zomaya, Mourad Elloumi

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CHAPTER 15

FILTERS AND SEEDS APPROACHES FOR FAST HOMOLOGY SEARCHES IN LARGE DATASETS

Nadia Pisanti, Mathieu Giraud, and Pierre Peterlongo

15.1 INTRODUCTION

15.1.1 Homologies and Large Datasets

Homologies inside large sequences or a large set of sequences are the key to several molecular biology studies. Similarities between genomic sequences are often traces of common ancestry, and the study of distances between species teaches us about the history of the evolution. Conserved elements between distant species are genes, transcription factors binding sites, transposable elements, or other functional elements.

Basically, homology-finding algorithms aim to detect in nucleic sequences more or less similar fragments, called simply repeats. Such fragments can be found within one sequence or in a set of several sequences. The selection pressure is not focused on the only nucleic sequences; for proteins, comparisons on the proteic sequences are often more relevant, and for RNA, the secondary structure can be more conserved than the nucleic sequence [14]. Similarities between sequences are often a first step to other more specific tools applied to the study of particular conserved elements.

On the other hand, the amount of data that biologists are dealing with are growing exponentially. Another recent reason for this relies on next generation sequencers [56]; they enable faster sequencing of DNA and with lower costs (several orders of magnitude cheaper) than using the original Sanger et ...

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