Algorithms and Methods for small RNA studies

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The emergence of Next Generation Sequencing provides billions of DNA elements from short sequence to full genomes. In fact, astronomic data have been and will be produced from whole genome, expressed genes from genomes regarding to their implication to either experimental, environmental, geospatial, and/or disease related conditions. The rapid and systematic functional classification, as well as the identification of the biological roles of such sequences, constitutes a major issue in bioinformatics and computational biology. My discovery research concerns the design of methods to predict from omics data the functional classification as well as evolutionary patterns of small RNAs. Such prediction methods rely on two main characteristics: the powerfulness of comparative omics methods and integrative approaches combining omics data to several related non-omics data. With the deep genomic knowledge present in the literature and the massive heterogeneous produced data, I plan to represent the knowledge, to automate the reasoning for drawing conclusions, to learn machines to adapt at new characteristics such as ancestral sequences and hidden patterns, and to recognize novel small RNA patterns. This discovery grant will permit innovative methods exploiting artificial intelligence, machine learning, graphical models, comparative genomics and integrative approaches for omics studies. The ultimate goal consisting of providing bioinformatics tools that predict and classify with high accuracy the function and regulatory mechanisms of these small RNA classes. My discovery program will focus on three main objectives: 1) design accurate methods for the identification of different classes of sRNAs; 2) develop method exploring hidden patterns and features from RNAseq data to improve the classification; 3) design methods exploiting comparative genomics and evolutionary origin of sRNA classes to improve classification. This proposal will provide new highly expected tools to be included in module constructed for main omics platforms. The outcome of this discovery program will allow a better understanding of sRNA biological functions that affect more than 60% of protein transcribed genes; improve our understanding of pathway regulation mechanisms and molecular marker development that help plant breeder to select better crops with greater tolerance to biotic and abiotic stresses; develop sRNA cancer biomarker; develop different therapy for several diseases that involved genes regulated by sRNAs such as Cardiac, inflammatory and autoimmune diseases. SRNAs will remain a main strategic target for pharmaceutical agricultural industries for this decade due to its huge economical values. This discovery proposal will provide training of high-qualified personals in bioinformatics, artificial intelligence, molecular biology as well as several papers in high impact open access journals.