scholarly journals Advancing quality control of food samples by Next Generation Sequencing compared to culture-dependent techniques

Author(s):  
Maria-Eleni Dimitrakopoulou ◽  
Chrysoula Kotsalou ◽  
Venia Stavrou ◽  
Apostolos Vantarakis
2016 ◽  
Vol 145 (3) ◽  
pp. 308-315 ◽  
Author(s):  
Patrick C. Mathias ◽  
Emily H. Turner ◽  
Sheena M. Scroggins ◽  
Stephen J. Salipante ◽  
Noah G. Hoffman ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0139868 ◽  
Author(s):  
Mohan A. V. S. K. Katta ◽  
Aamir W. Khan ◽  
Dadakhalandar Doddamani ◽  
Mahendar Thudi ◽  
Rajeev K. Varshney

2014 ◽  
Vol 5 ◽  
Author(s):  
Urmi H. Trivedi ◽  
Timothée Cézard ◽  
Stephen Bridgett ◽  
Anna Montazam ◽  
Jenna Nichols ◽  
...  

2017 ◽  
Vol 34 (10) ◽  
pp. 1799-1800 ◽  
Author(s):  
Nilesh R Tawari ◽  
Justine Jia Wen Seow ◽  
Dharuman Perumal ◽  
Jack L Ow ◽  
Shimin Ang ◽  
...  

Author(s):  
Dragana Dudić ◽  
Bojana Banović Đeri ◽  
Vesna Pajić ◽  
Gordana Pavlović-Lažetić

Next Generation Sequencing (NGS) analysis has become a widely used method for studying the structure of DNA and RNA, but complexity of the procedure leads to obtaining error-prone datasets which need to be cleansed in order to avoid misinterpretation of data. We address the usage and proper interpretations of characteristic metrics for RNA sequencing (RNAseq) quality control, implemented in and reported by FastQC, and provide a comprehensive guidance for their assessment in the context of total RNAseq quality control of Illumina raw reads. Additionally, we give recommendations how to adequately perform the quality control preprocessing step of raw total RNAseq Illumina reads according to the obtained results of the quality control evaluation step; the aim is to provide the best dataset to downstream analysis, rather than to get better FastQC results. We also tested effects of different preprocessing approaches to the downstream analysis and recommended the most suitable approach.


2021 ◽  
Author(s):  
Alisen Ayitewala ◽  
Isaac Ssewanyana ◽  
Charles Kiyaga

Abstract BackgroundHIV genotyping has had a significant impact on care and treatment of HIV/AIDS. At clinical level, the test guides physicians on the choice of treatment regimens. At surveillance level, it informs policy on consolidated treatment guidelines and microbial resistance control strategies. Until recently, the conventional test has utilized Sanger sequencing (SS) method. Unlike Next Generation Sequencing (NGS), SS is limited by low data throughput and the inability of detecting low abundant drug resistant variants. NGS has the capacity to improve sensitivity and quantitatively identify low-abundance variants; in addition, it has the potential to improve efficiency as well as lowering costs when samples are batched. Despite the NGS benefits, its utilization in clinical drug resistance profiling is faced with mixed reactions. These are largely based on lack of a consensus regarding the quality control strategy. Nonetheless, transitional views suggest validating the method against the gold-standard SS. Therefore, we present a validation report of an NGS-based in-house HIV genotyping method against SS method in Uganda. ResultsSince there were no established proficiency test panels for NGS-based HIV genotyping, fifteen (15) clinical plasma samples for routine care were utilized. The use of clinical samples allowed for accuracy and precision studies. The workflow involved four (4) main steps; viral RNA extraction, targeted amplicon generation, amplicon sequencing and data analysis. Accuracy of 98% with an average percentage error of 3% was reported for the NGS based assay against the SS platform demonstrating similar performance. The coefficient of variation (CV) findings for both the inter-run and inter-personnel precision showed no variability (CV ≤0%) at the relative abundance of ≥20%. For both inter-run and inter-personnel, variation that affected the precision was observed at 1% frequency. Overall, for all the frequencies, CV registered a small range of (0-2%).Conclusion The NGS-based in-house HIV genotyping method fulfilled the minimum requirements that support its utilization for drug resistance profiling in a clinical setting of a low-income country. For more inclusive quality control studies, well characterized wet panels need to be established.


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