2-AIN-505, 2-AIN-251: Seminár z bioinformatiky (1) a (3)
Zima 2019

Ryan R. Wick, Louise M. Judd, Kathryn E. Holt. Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutionalneural networks. PLoS Comput Biol, 14(11):e1006583. 2018.

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Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a
single flow cell, has made Oxford Nanopore sequencing cost-effective for small
genomes. However, it depends on the ability to sort the resulting sequencing
reads by barcode, and current demultiplexing tools fail to classify many reads.
Here we present Deepbinner, a tool for Oxford Nanopore demultiplexing that uses a
deep neural network to classify reads based on the raw electrical read signal.
This 'signal-space' approach allows for greater accuracy than existing
'base-space' tools (Albacore and Porechop) for which signals must first be
converted to DNA base calls, itself a complex problem that can introduce noise
into the barcode sequence. To assess Deepbinner and existing tools, we performed 
multiplex sequencing on 12 amplicons chosen for their distinguishability. This
allowed us to establish a ground truth classification for each read based on
internal sequence alone. Deepbinner had the lowest rate of unclassified reads
(7.8%) and the highest demultiplexing precision (98.5% of classified reads were
correctly assigned). It can be used alone (to maximise the number of classified
reads) or in conjunction with other demultiplexers (to maximise precision and
minimise false positive classifications). We also found cross-sample chimeric
reads (0.3%) and evidence of barcode switching (0.3%) in our dataset, which
likely arise during library preparation and may be detrimental for quantitative
studies that use multiplexing. Deepbinner is open source (GPLv3) and available at