KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing.
KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing.
复制标题
DOI：
10.3389/fbioe.2022.1016408
复制
发表时间：
2022
影响因子：
5.7
通讯作者：
Prosperi, Mattia
中科院分区：
文献类型：
--
作者：
Barquero, Alexander;Marini, Simone;Boucher, Christina;Ruiz, Jaime;Prosperi, Mattia
关键词：
纳米孔技术可以对临床和生态样品中的微生物种群进行便携式实时测序。Nanopore的新兴医疗保健应用包括即时护理，及时识别抗生素耐药基因（ARG），以帮助开发细菌感染的靶向治疗，并监测环境中的耐药爆发。虽然存在几种用于从测序数据分类ARG的计算工具，但迄今为止（2022年）还没有开发出用于移动的设备的计算工具。我们在这里介绍了KARGAMPLY，一个移动的应用程序，用于便携式，实时，易于解释的纳米孔测序ARG分析。KARGAMSTRUCTURE是一个名为KARGA的现有ARG识别工具的移植;它保留了相同的算法结构，但它针对移动的设备进行了优化。具体来说，KARGAMMK采用压缩的ARG参考数据库和不同的内部数据结构来节省RAM的使用。KARGAMPEGTM应用程序具有友好的图形用户界面，可指导文件浏览、加载、参数设置和过程执行。更重要的是，对输出文件进行后处理，以创建可视、可打印和可共享的报告，帮助用户解释ARG调查结果。KARGAMITRA和KARGA之间的分类性能差异很小（在具有已知电阻地面真实值的100万个读数的半合成数据集上，96.2% vs. 96.9%的f-测量）。使用真实的纳米孔实验，KARGAMTM平均每23-48分钟处理1 GB数据（靶向测序-宏基因组学），峰值RAM使用低于500 MB，与输入文件大小无关，连续数据处理1小时后平均温度为49°C。KARGAMITLE是用Java编写的，可以在https://github.com/Ruiz-HCI-Lab/KargaMobile上获得，并使用MIT许可证。
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.
登录
查看更多内容
DOI：
10.3390/molecules23040795
发表时间：
2018-03-30
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Manyi-Loh C;Mamphweli S;Meyer E;Okoh A
通讯作者：
Okoh A
DOI：
10.1093/jac/dkaa345
发表时间：
2020-12-01
期刊：
The Journal of antimicrobial chemotherapy
影响因子：
--
作者：
Bortolaia V;Kaas RS;Ruppe E;Roberts MC;Schwarz S;Cattoir V;Philippon A;Allesoe RL;Rebelo AR;Florensa AF;Fagelhauer L;Chakraborty T;Neumann B;Werner G;Bender JK;Stingl K;Nguyen M;Coppens J;Xavier BB;Malhotra-Kumar S;Westh H;Pinholt M;Anjum MF;Duggett NA;Kempf I;Nykäsenoja S;Olkkola S;Wieczorek K;Amaro A;Clemente L;Mossong J;Losch S;Ragimbeau C;Lund O;Aarestrup FM
通讯作者：
Aarestrup FM
影响因子：
9.2
作者：
Nicholls, Samuel M.;Quick, Joshua C.;Loman, Nicholas J.
通讯作者：
Loman, Nicholas J.
影响因子：
7.7
作者：
Evans, Daniel R.;Griffith, Marissa P.;Van Tyne, Daria
通讯作者：
Van Tyne, Daria
影响因子：
14.9
作者：
Doster, Enrique;Lakin, Steven M.;Morley, Paul S.
通讯作者：
Morley, Paul S.