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Analyzing Genomes with a Smartphone


It could be possible to identify infectious diseases using a smartphone, according to a new study.

genome analysis

Photo and thumbnail have been modified. Courtesy of Hasindu Gamaarachchi.

In an effort to make genomic technologies more accessible to improve human health, researchers at the Garvan Institute of Medical Research and UNSW Sydney have developed an algorithm that could make it possible to identify infectious diseases using the computational memory of devices as small as a smartphone, according to a study published in Scientific Reports.

Through the incorporation of multi-index merging into a Minimap2 aligner — which aligns DNA sequencing ‘reads’ to a reference library of known genomes — the researchers found that long read alignment to the human genome can be performed on a system with 2 GB RAM with almost no impact on accuracy. Generally, it takes 16 GB of memory to align genomic sequences.

The reduction of memory makes it possible for analysis to be done on the spot using the memory available in a typical smartphone.

The research team reproduced 99.98 percent of the alignments and by using smaller index segments, could map an additional 1 percent of sequencing reads.

The reference library is usually sorted, which helps quickly map the sequencing reads to their corresponding positions in a reference genome. This requires a lot of computer memory.

So, the research team split the reference library into smaller segments and mapped the DNA reads. When the team finished mapping to smaller segments, they pooled the results together.

“What we did in this study was fine-tune parameters and select the best mappings across several small indexes,” said Martin Smith, Ph.D., team leader of genomic technologies at the Garvin Institute’s Kinghorn Centre for Clinical Genomics in Australia. “This approach gave us similar accuracy as current standard genomic analyses, which previously required the memory available in high performance computers.”

Long read sequence alignment should be performed on ultra-portable computing devices to accelerate the development and uptake of real-time genomic applications in point of care medical testing.

“The potential of lightweight, portable genomic analysis is vast — we hope that this technology will one day be applied in the context of point-of-care microbial infections in remote regions, or in doctors’ hands at the hospital bedside,” Smith said.

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