Application-Oriented Succinct Data Structures for Big Data

Shibuya, Tetsuo

doi:10.1007/s12626-019-00045-1

Cited by 1 publication

(1 citation statement)

References 74 publications

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“…One thing to note is that KARGAMobile uses an in-memory database which is loaded at each program execution, without any data transforms or succinct structures, e.g., integer hashing of strings, Burrows-Wheeler transform, or FM-index ( Shibuya, 2019 ). ARGs are several orders of magnitude smaller in size than bacterial genes and genomes.…”

Section: Methodsmentioning

confidence: 99%

KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing

Barquero

Marini²,

Boucher³

et al. 2022

Front. Bioeng. Biotechnol.

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Nanopore technology enables portable, real-time sequencing of microbial populations from clinical and ecological samples. An emerging healthcare application for Nanopore includes point-of-care, timely identification of antibiotic resistance genes (ARGs) to help developing targeted treatments of bacterial infections, and monitoring resistant outbreaks in the environment. While several computational tools exist for classifying ARGs from sequencing data, to date (2022) none have been developed for mobile devices. We present here KARGAMobile, a mobile app for portable, real-time, easily interpretable analysis of ARGs from Nanopore sequencing. KARGAMobile is the porting of an existing ARG identification tool named KARGA; it retains the same algorithmic structure, but it is optimized for mobile devices. Specifically, KARGAMobile employs a compressed ARG reference database and different internal data structures to save RAM usage. The KARGAMobile app features a friendly graphical user interface that guides through file browsing, loading, parameter setup, and process execution. More importantly, the output files are post-processed to create visual, printable and shareable reports, aiding users to interpret the ARG findings. The difference in classification performance between KARGAMobile and KARGA is minimal (96.2% vs. 96.9% f-measure on semi-synthetic datasets of 1 million reads with known resistance ground truth). Using real Nanopore experiments, KARGAMobile processes on average 1 GB data every 23–48 min (targeted sequencing - metagenomics), with peak RAM usage below 500MB, independently from input file sizes, and an average temperature of 49°C after 1 h of continuous data processing. KARGAMobile is written in Java and is available at https://github.com/Ruiz-HCI-Lab/KargaMobile under the MIT license.

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Section: Methodsmentioning

confidence: 99%