4.3 Review

Review of automated DNA extraction systems for sequencing-based solutions for drug-resistant tuberculosis detection

Journal

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.diagmicrobio.2020.115096

Keywords

DNA extraction; LMIC; Sequencing; Drug resistant; Tuberculosis

Funding

  1. Department for International Development from the United Kingdom award [204074-101]
  2. Department of Foreign Affairs and Trade Australian Government award [70957]

Ask authors/readers for more resources

Robust clinical specimen nucleic acid extraction instrumentation and methods arc critical to the performance of downstream molecular diagnostics for the diagnosis of drug-resistant tuberculosis (DR-TB). Currently, there is a high level of interest in sequencing-based solutions for rapid and comprehensive DR-TB testing from primary specimens (i.e., sputum). However, there is no standardized or fully automated sputum extraction system that has been widely implemented for use with Mycobacterium tuberculosis complex-containing sputum specimens. For sequencing-based technologies to be widely adopted in clinical laboratory settings in low- and middle-income countries, automated extraction technologies will be important to enhance scalability and reliability and to standardize performance of the downstream assays. Additionally, the ease of automatic technologies allows for faster uptake in laboratories currently without the expertise or infrastructure to perform manual extractions at the same automated throughput. This work is intended to provide an initial specification comparison of available automated DNA extraction systems that could serve as front-end components for existing and future sequencing approaches and provide the framework for future evaluations. (C) 2020 The Authors. Published by Elsevier Inc.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available