Mass molecular testing for COVID19 using NGS-based technology and a highly scalable workflow
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Abstract
Since the first reported case of the new coronavirus infection in Wuhan, China, researchers and governments have witnessed an unseen rise in the number of cases. Thanks to the rapid work of Chinese scientists, the pathogen now called SARS-CoV-2 has been identified and its whole genome was deposited in public databases by early January 2020. The availability of the genome has allowed researchers to develop Reverse Transcription—Polymerase Chain Reaction (RT-PCR) assays, which are now the gold-standard for molecular diagnosis of the respiratory syndrome COVID19. Because of the rising number of cases and rapid spreading, the world has been facing a shortage of RT-PCR supplies, especially the ones involved in RNA extraction. This has been a major bottleneck to increase testing capacity in many countries that do not significantly manufacture these supplies, such as Brazil. Additionally, RT-qPCR scalability is highly dependent on equipment that usually performs testing of 96 samples at a time. In this work, we describe a cost-effective molecular NGS-based test for diagnosis of COVID19, which uses a single-step RNA extraction and presents high scalability and accuracy when compared to the gold-standard RT-qPCR. A single run of the NGS-based test using the Illumina NextSeq 550 mid-end sequencing equipment is able to multiplex 1,536 patient’s samples, providing individual semi-qualitative results (detected, not detected). Detected results are provided with fragments per million (FPM) values, which was demonstrated to correlate with RT-qPCR Cycle Threshold (CT) values. Besides, usage of the high-end Illumina Novaseq platform may yield diagnostic for up to 6144 samples in a single run. Performance results when compared with RT-qPCR show general accuracy of 96%, and 98% when only samples with CT values (gene N) lower than 30 are considered. We have also developed an online platform, termed VarsVID, to help test executors to easily scale testing numbers. Sample registering, wet-lab worksheets generation, sample sheet for sequencing and results’ display are all features provided by VarsVID. Altogether, these results will contribute to control COVID19 pandemics.
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SciScore for 10.1101/2020.08.10.20172106: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Clinical samples presented in this work were used according to approval of the Hospital Ethical Committee under report #4.178.076. 2.3. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources More specifically, raw sequencing data is demultiplexed using Illumina’s tool blc2fastq with barcode mismatches set to 0 and other parameters set to default. Illumina’ssuggested: NoneReads aligned to target were assessed using Bedtools and qualitative results were generated based on a threshold of reads (≥ 10) and on the decision-table shown in Table 1. B…SciScore for 10.1101/2020.08.10.20172106: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Clinical samples presented in this work were used according to approval of the Hospital Ethical Committee under report #4.178.076. 2.3. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources More specifically, raw sequencing data is demultiplexed using Illumina’s tool blc2fastq with barcode mismatches set to 0 and other parameters set to default. Illumina’ssuggested: NoneReads aligned to target were assessed using Bedtools and qualitative results were generated based on a threshold of reads (≥ 10) and on the decision-table shown in Table 1. Bedtoolssuggested: (BEDTools, RRID:SCR_006646)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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