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Author(s): I. Kavakiotis, A. Triantafyllidis, P. Samaras, A. Voulgaridis, N. Karaiskou, E. Konstantinidis, I. Vlahavas.
Title: “Pattern discovery for microsatellite genome analysis”.
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Keywords:
Genome Analysis, Bioinformatics software, Mining methods, Pattern Discovery, Microsatellites, Simple sequence repeats.
Appeared in:
Computers in Biology and Medicine, Edward John Ciaccio (Ed.), Elsevier, 2014. Abstract: Microsatellite loci comprise an important part of eukaryotic genomes. Their applications in biology as genetic markers are related to numerous fields ranging from paternity analyses to construction of genetic maps and linkage to human disease. Existing software which offer pattern discovery algorithms for the correct identification and downstream analysis of microsatellites are scarce and are proving to be inefficient to analyse the large, exponentially increasing, sequenced genomes. Moreover, such analyses can be very difficult for bioinformatically inexperienced biologists. In this paper we present MiGA (Microsatellite Genome Analysis) software for the detection of all microsatellite loci in genomic data through a user friendly interface. The algorithm searches exhaustively and rapidly for most microsatellites. Contrary to other applications, MiGA takes into consideration the following three most important aspects: The efficiency of the algorithm, the usability of the software and the plethora of offered summary statistics. All the above, help biologists to obtain basic quantitative and qualitative information regarding the presence of microsatellites in genomic data as well as downstream processes, such as selection of specific microsatellite loci for primer design and comparative genome analysis.
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