Evidence map›Paper›PMID 36324897›Full record

ArticleFrontiers in bioengineering and biotechnology2022

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

Alexander Barquero, Simone Marini, Christina Boucher, Jaime Ruiz, Mattia Prosperi

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alexander BarqueroDepartment of Computer Science and Information and Engineering, University of Florida, Gainesville, FL, United States.
Simone MariniDepartment of Epidemiology, University of Florida, Gainesville, FL, United States.
Christina BoucherDepartment of Computer Science and Information and Engineering, University of Florida, Gainesville, FL, United States.
Jaime RuizDepartment of Computer Science and Information and Engineering, University of Florida, Gainesville, FL, United States.
Mattia ProsperiDepartment of Epidemiology, University of Florida, Gainesville, FL, United States.

Funding

Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning frameworkR01AI145552 · NIAID · UNIVERSITY OF FLORIDA · PI Simone Marini, Mattia Prosperi · 2020 to 2026
$3.6M
Developing Computational Methods for Surveillance of Antimicrobial Resistant AgentsR01AI141810 · NIAID · UNIVERSITY OF FLORIDA · PI BOUCHER, CHRISTINA, PROSPERI, MATTIA · 2019 to 2023
$2.1M
NIAID NIH HHS R01 AI141810NIAID NIH HHS R01 AI145552
6 · The paper itself

Abstract

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%

Indexed as

antimicrobial resistancebacterial genesbioinformatics and computational biologymetagenomicsmobile appssequencing analyses

Identifiers

PMID36324897
PMCPMC9618647

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.