Stochastic point process models for Next Generation Sequencing

Abstract

<p>The Next Generation Sequencing (NGS) revolutionized the quality and quantity of the genetic data delivered. To extract all the benefits of the new technique there is an urge of precise inference rules built from a strong theoretical basis. In the presentation I will provide a novel, extended way of looking at NGS data. The NGS experiment can<br>be interpreted as a process of mapping short fragments of sequences (short reads) to a genome region of interest (exon , gene, gene family or even whole chromosome) and the activity of a region, is derived from the number of successful mappings. The increased reliability and the design of the NGS experiments allows for a more sophisticated<br>mathematical framework which uses not only the intensity of expression but also the position of particular reads aligned to the genomic region. To account for both aspects, in my presentation I introduce the Poisson point process framework for the NGS experiments. In this approach the reference genome coordinate information of the mapped reads implies that the differences in activity can arise also in changes of read positioning. Using the<br>inference tools for stochastic point processes combined with functional data analysis I provide a method to quantify the activity differences in terms of both - the intensity and positioning - through the phase-amplitude separation. As a consequence I  revisit the problem of the variability in NGS data and indicate, how it can be understood through the phase-amplitude dichotomy. Finally I will show that the new approach can reveal additional<br>information in the genetic data. The proposed method can be effectively utilized in detecting events of alternative splicing, exon blocking, exon skipping, can be also thought of as a new setting for inference on NGS data.</p

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