Sequencing-based methods and resources to study antimicrobial resistance.
TLDR
Focusing on sequence-based discovery of antibiotic resistance genes, this Review discusses computational strategies and resources for resistance gene identification in genomic and metagenomic samples, including recent deep-learning approaches.Abstract:
Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.read more
Citations
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SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)
TL;DR: SPAdes as mentioned in this paper is a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler and on popular assemblers Velvet and SoapDeNovo (for multicell data).
Journal ArticleDOI
CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database
Brian Alcock,Amogelang R. Raphenya,Tammy T. Y. Lau,Kara K. Tsang,Mégane Bouchard,Arman Edalatmand,William Huynh,Anna-Lisa V. Nguyen,Annie A. Cheng,Sihan Liu,Sally Y. Min,Anatoly Miroshnichenko,Hiu-Ki R Tran,Rafik El Werfalli,Jalees A. Nasir,Martins Oloni,David Speicher,Alexandra Florescu,Bhavya Singh,Mateusz Faltyn,Anastasia Hernández-Koutoucheva,Arjun N. Sharma,Emily Bordeleau,Andrew C. Pawlowski,Haley L. Zubyk,Damion M. Dooley,Emma Griffiths,Finlay Maguire,Geoffrey L. Winsor,Robert G. Beiko,Fiona S. L. Brinkman,William W. L. Hsiao,William W. L. Hsiao,Gary Van Domselaar,Gary Van Domselaar,Andrew G. McArthur +35 more
TL;DR: A new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes, able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants.
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Antibiotic resistance in Pseudomonas aeruginosa - Mechanisms, epidemiology and evolution
TL;DR: This review provides a comprehensive description of the main strategies involved in AR in P. aeruginosa and the leading drivers of HGT in this species.
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Using Genomics to Track Global Antimicrobial Resistance.
Rene S. Hendriksen,Valeria Bortolaia,Heather Tate,Gregory H. Tyson,Frank Møller Aarestrup,Patrick F. McDermott +5 more
TL;DR: A scientific literature review is conducted and a description of examples of available tools and databases for antimicrobial resistance (AMR) detection and future perspectives and recommendations are presented.
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Innovative and rapid antimicrobial susceptibility testing systems.
Alex van Belkum,Carey-Ann D. Burnham,John W. A. Rossen,Frédéric Mallard,Olivier Rochas,William Michael Dunne,William Michael Dunne +6 more
TL;DR: The current state of AST systems in the broadest technical, translational and implementation-related scope is discussed, including new emerging technologies as well as genomic and gene-based antimicrobial resistance detection methods.
References
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Basic Local Alignment Search Tool
TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li,Richard Durbin +1 more
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Fast gapped-read alignment with Bowtie 2
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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