Methods and Techniques for miRNA Data Analysis.

Genomic data analysis consists of techniques to analyze and extract information from genes. In particular, genome sequencing technologies allow to characterize genomic profiles and identify biomarkers and mutations that can be relevant for diagnosis and designing of clinical therapies. Studies often regard identification of genes related to inherited disorders, but recently mutations and phenotypes are considered both in diseases studies and drug designing as well as for biomarkers identification for early detection. Gene mutations are studied by comparing fold changes in a redundancy version of numeric and string representation of analyzed genes starting from macromolecules. This consists of studying often thousands of repetitions of gene representation and signatures identified by biological available instruments that starting from biological samples generate arrays of data representing nucleotides sequences representing known genes in an often not well-known sequence. High-performance platforms and optimized algorithms are required to manipulate gigabytes of raw data that are generated by the so far mentioned biological instruments, such as NGS (standing for Next-Generation Sequencing) as well as for microarray. Also, data analysis requires the use of several tools and databases that store gene targets as well as gene ontologies and gene-disease association. In this chapter we present an overview of available software platforms for genomic data analysis, as well as available databases with their query engines.

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