Nanocall: an open source basecaller for Oxford Nanopore sequencing data

Abstract

Motivation: The highly portable Oxford Nanopore MinION sequencer has enabled new applications of genome sequencing directly in the field. However, the MinION currently relies on a cloud computing platform, Metrichor (metrichor.com), for translating locally generated sequencing data into basecalls.

Results: To allow offline and private analysis of MinION data, we created Nanocall. Nanocall is the first freely available, open-source basecaller for Oxford Nanopore sequencing data and does not require an internet connection. Using R7.3 chemistry, on two E.coli and two human samples, with natural as well as PCR-amplified DNA, Nanocall reads have ∼68% identity, directly comparable to Metrichor '1D' data. Further, Nanocall is efficient, processing ∼2500 Kbp of sequence per core hour using the fastest settings, and fully parallelized. Using a 4 core desktop computer, Nanocall could basecall a MinION sequencing run in real time. Metrichor provides the ability to integrate the '1D' sequencing of template and complement strands of a single DNA molecule, and create a '2D' read. Nanocall does not currently integrate this technology, and addition of this capability will be an important future development. In summary, Nanocall is the first open-source, freely available, off-line basecaller for Oxford Nanopore sequencing data.

Availability and implementation: Nanocall is available at github.com/mateidavid/nanocall, released under the MIT license.

Contact: matei.david@oicr.on.caSupplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1

Fraction aligned versus identity using a base-10 logarithmic density plot, for all datasets (rows) and all read types (columns). Only showing reads where all 1D basecalls are mapped (Color version of this figure is available at Bioinformatics online.)

Fig. 2

Comparison of Metrichor and Nanocall (2ss) mapped reads using a base-10 logarithmic density plot, for template reads from the human PCR dataset. We show Metrichor versus Nanocall: identity (row 1), read length (row 2) and fraction aligned (row 3). We compare Nanocall template reads with Metrichor template reads (column 1) and Metrichor 2D reads (column 2) (Color version of this figure is available at Bioinformatics online.)

Fig. 3

Nanocall (2ss) versus Metrichor scaling parameters for mapped reads using a base-10 logarithmic density plot, for the human PCR dataset. We show Metrichor vs Nanocall scale (row 1) and shift (row 2), for template reads (col 1) and complement reads (col 2). Only showing reads where both Metrichor and Nanocall detected a hairpin and picked the same complement model (Color version of this figure is available at Bioinformatics online.)

Fig. 4

Nanocall versus Metrichor hairpin position using a base-10 logarithmic density plot, for reads where Nanocall detected a hairpin, for each dataset (Color version of this figure is available at Bioinformatics online.)