DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads

In genomics, Base Callers are codes that figure out the G, T, A and Cs of DNA molecules after those have been cut into pieces in order to reassemble all that information back into one long string.

The nanopore technology promises to revolutionize genomics because, quite simply, it makes a formerly NP-hard problem of putting this information back together into anot so hard problem (P). This is why the nanopore technology is one of the steamrollers ( Predicting the Future: The Steamrollers ). Today, the data coming out of these sensors seem amenable to better classification thanks to Deep Learning thereby reducing its error rate and slowly putting on a par with other technologies. Woohoo ! Time for a Miller wave.

Let us note that these readings might have been looked at from the standpoint of a regular signal processing issue but people seem to eagerly try deep learning first. This is another example of the Great Convergence. Without further ado:

DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads by Vladimír Boža, Broňa Brejová, Tomáš Vinař

Motivation: The MinION device by Oxford Nanopore is the first portable sequencing device. MinION is able to produce very long reads (reads over 100~kBp were reported), however it suffers from high sequencing error rate. In this paper, we show that the error rate can be reduced by improving the base calling process.
Results: We present the first open-source DNA base caller for the MinION sequencing platform by Oxford Nanopore. By employing carefully crafted recurrent neural networks, our tool improves the base calling accuracy compared to the default base caller supplied by the manufacturer. This advance may further enhance applicability of MinION for genome sequencing and various clinical applications.
Availability: DeepNano can be downloaded at this http URL

The website for the paper and code is here: http://compbio.fmph.uniba.sk/deepnano/

Let us also note that it looks like that even the nanopore sensor maker is also going that route.

@mattloose for example, at 150-180mv. 200mv adds a 1-2%. Looking at 250 and 300mv. Note 2D tidied up since talk. pic.twitter.com/YkqZvOdK6Q

— Clive G. Brown (@Clive_G_Brown) 31 mars 2016

 Previous blog entries:

• Sunday Morning Insight: Escaping Feynman's NP-Hard "Map of a Cat": Genomic Sequencing Edition),
• Sunday Morning Insight: The Second Inflection Point in Genome Sequencing Sunday Morning Insight: "Ca change tout" and the second inflection point in Genome Sequencing
• The Important Things after Commodity Sequencing.
• Sunday Morning Insight: What Happens When You Cross into P Territory ?
• Sunday Morning Insight: Crossing into P territory ). 
• Hamming's time: The Important Things after Commodity Sequencing
• Predicting the Future: The Steamrollers
• Of Well Logging and Nanopore Sequencing
• Structural Information in Nanopore Sequencing ?
• Quantum Biosystems Provides Raw Data Access to New Sequencing Technology
• Improving Pacific Biosciences' Single Molecule Real Time Sequencing Technology through Advanced Matrix Factorization ?
• DNA Sequencing, Information Theory, Advanced Matrix Factorization and all that...
• Sunday Morning Insight: A conversation on Nanopore Sequencing and Signal Processing
• Sunday Morning Insight: Thinking about a Compressive Genome Sequencer

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