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Back-and-Forth on the MinIon

News about what Oxford Nanopore's MinIon, a pencil case-sized nanopore sequencer, can do has been slowly leaking out of its early-access users' labs this summer.

The University of Birmingham's Nick Loman presented a single 8.5-kilobase read from the Pseudomonas aeruginosa genome in June. "It's surpassed all my expectations," he told In Sequence at the time. "I am amazed that we are getting so much useful data within a week of getting started."

But a peer-reviewed opinion piece appearing online at Molecular Ecology Resources from Alexander Mikheyev and Mandy Tin from the Okinawa Institute of Science and Technology in Japan says that much of the sequences they generated was marred by indel and other errors.

After the burn-in phase in which users self-certify that their performance is up to snuff for their needs, Mikheyev and Tin resequenced the bacteriophage lambda genome, sequenced amplicons from a previously analyzed snake venom transcriptome, and attempted the de novo sequencing of an insect genome. They report that some 90 percent of the MinIon reads had no homology to the reference. The bits that did were small and exhibited a 20 percent error rate, they say.

The MinIon technology "is still very experimental, and it's perhaps years behind its competition," Mikheyev, tells IS, adding that he "can't think of any universal benefit to the MinIon at this point."

But Loman and others note that the data from the Okinawa team falls outside of the performance seen by other groups in the early-access program and is based on older version of the chemistry. At Omics! Omics!, Keith Robison says Oxford Nanopore was clear on that being an early-stage chemistry, and one that has since been replaced.

Robison also says that Mikheyev and Tin ignore in their piece the extent to which library preparation changes can improve sequence quality on this platform.

Mick Watson at Opiniomics is more blunt and calls the paper "lazy." He adds that he thinks nanopore sequence is "here to stay."

"Don't get me wrong, there's a long way to go; lots of improvements need to happen. And they will," he writes. "But the people that will make those improvements, both in the technology and in the bioinformatics algorithms to deal with the data, will be positive, forward-thinking people, people who approach science and data with optimism and an open mind."