TIC Seminar: Processing of Basecall Nanopore Sequencing Signal with Convolutional Recurrent Neural Network (CRNN)

Deciphering the DNA sequence from the noisy and complex signals is challenging. This session will explore how deep learning models can be used to achieve end-to-end basecalling: directly translating raw signal to DNA sequence without the error-prone segmentation step.

schedule Date & time
9 Mar 2018 2:30pm - 9 Mar 2018 3:30pm
person Speaker
Teng Haotian, The University of Queensland
contact_support Contact
Marcin Adamski
marcin.adamski@anu.edu.au

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Description

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging.
I will talk about the first deep learning model to achieve end-to-end basecalling: Directly translating raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4000 reads, we show that our model provides state-of-the-art basecalling accuracy even on previously unseen species.

TIC Techniques In Computational Genomics

  • A community of researchers engaged in, or dependent on computational analysis of genomic data
  • Weekly seminars by volunteers
  • Drop-in sessions/round-table discussions convened by ANU Bioinformatics Consultancy (ABC)
  • Venue alternates between JCSMR and RSB, ANU.

Location

Slatyer Seminar Room (100), RN Robertson Bldg. (#46)