iGenomics: Comprehensive DNA sequence analysis on your Smartphone.


Journal

GigaScience
ISSN: 2047-217X
Titre abrégé: Gigascience
Pays: United States
ID NLM: 101596872

Informations de publication

Date de publication:
07 12 2020
Historique:
received: 10 03 2020
revised: 29 09 2020
accepted: 10 11 2020
entrez: 7 12 2020
pubmed: 8 12 2020
medline: 26 10 2021
Statut: ppublish

Résumé

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. 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. iGenomics is available open source (https://github.com/stuckinaboot/iGenomics) and for free on Apple's App Store (https://apple.co/2HCplzr).

Sections du résumé

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).

Identifiants

pubmed: 33284326
pii: 6025149
doi: 10.1093/gigascience/giaa138
pmc: PMC7720420
pii:
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press GigaScience.

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Auteurs

Aspyn Palatnick (A)

Cold Spring Harbor High School, 82 Turkey Lane, Cold Spring Harbor, NY 11724, USA.
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA.
Networked and Social Systems Engineering, University of Pennsylvania, 220 South 33rd Street, Philadelphia, PA 19104, USA.

Bin Zhou (B)

Department of Biology, New York University, 100 Washington Square, New York, NY 10003, USA.

Elodie Ghedin (E)

Department of Biology, New York University, 100 Washington Square, New York, NY 10003, USA.
Department of Epidemiology, New York University School of Global Public Health, 665 Broadway St, New York, NY 10003, USA.

Michael C Schatz (MC)

Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA.
Departments of Computer Science and Biology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21211, USA.

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Classifications MeSH