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Title: A Markov chain Monte Carlo algorithm for change-point detection in nanopore sequencing data Authors:  Sophia Shen - Macquarie University (Australia)
Georgy Sofronov - Macquarie University (Australia) [presenting]
Abstract: Understanding the genetic makeup of organisms is a very important goal in bioinformatics. DNA sequencing, the process of determining the order of the nucleotide bases in DNA, is now able to be performed quickly and cheaply with commercially available devices no bigger than a USB stick. These third-generation nanopore sequencers are capable of capturing long, repetitive DNA structures; however, the reported reading accuracy needs improving. One main source of error occurs when the raw nanopore signals are being translated into genetic alphabets (A, C, G and T). This process is called base-calling. We present a novel base-calling algorithm using Bayesian methodologies and Markov chain Monte Carlo (MCMC) sampling techniques that allow transitions between different models. Since each base transition could be thought of as a change-point in the raw signals, change-point detection or segmentation methods are adopted. We use real and artificially simulated data to illustrate the usefulness of the proposed approach.