Computational Biology
@ Comenius University in Bratislava

Vladimir Boza, Peter Peresini, Brona Brejova, Tomas Vinar. Dynamic Pooling Improves Nanopore Base Calling Accuracy. IEEE/ACM Trans Comput Biol Bioinform, 19(6):3416-3424. 2022.

Download preprint: not available

Download from publisher: https://doi.org/10.1109/tcbb.2021.3128366

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Bibliography entry: BibTeX

Abstract:

In nanopore sequencing, electrical signal is measured as DNA molecules pass
through the sequencing pores. Translating these signals into DNA bases (base
calling) is a highly non-trivial task, and its quality has a large impact on the 
sequencing accuracy. The most successful nanopore base callers to date use
convolutional neural networks (CNN) to accomplish the task. Convolutional layers 
in CNNs are typically composed of filters with constant window size, performing
best in analysis of signals with uniform speed. However, the speed of nanopore
sequencing varies greatly both within reads and between sequencing runs. Here, we
present dynamic pooling, a novel neural network component, which addresses this
problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness 
of dynamic pooling, we developed two base callers: Heron and Osprey. Heron
improves the accuracy beyond the experimental high-accuracy base caller Bonito
developed by Oxford Nanopore. Osprey is a fast base caller that can compete in
accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and
achieves a near real-time speed on common desktop CPUs. Availability:
https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron.