iGenomics: Comprehensive DNA sequence analysis on your Smartphone
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Abstract
Background
Following the miniaturization of integrated circuitry and other computer hardware over the past several decades, DNA sequencing is on a similar path. Leading this trend is the Oxford Nanopore sequencing platform, which currently offers the hand-held MinION instrument and even smaller instruments on the horizon. This technology has been used in several important applications, including the analysis of genomes of major pathogens in remote stations around the world. However, despite the simplicity of the sequencer, an equally simple and portable analysis platform is not yet available.
Results
iGenomics is the first comprehensive mobile genome analysis application, with capabilities to align reads, call variants, and visualize the results entirely on an iOS device. Implemented in Objective-C using the FM-index, banded dynamic programming, and other high-performance bioinformatics techniques, iGenomics is optimized to run in a mobile environment. We benchmark iGenomics using a variety of real and simulated Nanopore sequencing datasets of viral and bacterial genomes and show that iGenomics has performance comparable to the popular BWA-MEM/SAMtools/IGV suite, without necessitating a laptop or server cluster.
Conclusions
iGenomics is available open source (https://github.com/stuckinaboot/iGenomics) and for free on Apple's App Store (https://apple.co/2HCplzr).
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Now published in GigaScience doi: 10.1093/gigascience/giaa138
Aspyn Palatnick 1Cold Spring Harbor High School, Cold Spring Harbor, NY 117242Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 117243University of Pennsylvania, Philadelphia, PA 19104Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteBin Zhou 4Department of Biology, New York University, New York, NY 10003Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteElodie Ghedin 4Department of Biology, New York University, New York, NY 100035Department of Epidemiology, School of Global Public Health, New York, NY 10003Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMichael C. Schatz 2Simons Center for …
Now published in GigaScience doi: 10.1093/gigascience/giaa138
Aspyn Palatnick 1Cold Spring Harbor High School, Cold Spring Harbor, NY 117242Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 117243University of Pennsylvania, Philadelphia, PA 19104Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteBin Zhou 4Department of Biology, New York University, New York, NY 10003Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteElodie Ghedin 4Department of Biology, New York University, New York, NY 100035Department of Epidemiology, School of Global Public Health, New York, NY 10003Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMichael C. Schatz 2Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 117246Departments of Computer Science and Biology, Johns Hopkins University, Baltimore MD, 21211Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Michael C. SchatzFor correspondence: mschatz@cshl.edu
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa138 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102518 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102519 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.102520
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