Reliable and accurate diagnostics from highly multiplexed sequencing assays

  1. A. Sina Booeshaghi
  2. Nathan B. Lubock
  3. Aaron R. Cooper
  4. Scott W. Simpkins
  5. Joshua S. Bloom
  6. Jase Gehring
  7. Laura Luebbert
  8. Sri Kosuri
  9. Lior Pachter

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Abstract

Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA.

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  1. Version published to 10.1038/s41598-020-78942-7
  2. ScreenIT

    SciScore for 10.1101/2020.05.13.20100131: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The BUS file was converted to a text file and processed in Python to count the number of reads per sample that map uniquely to a specific target gene.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The default with bcl2fastq is error correction of 1 mismatch, so this step serves to error correct each index separately.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)

    Results from OddPub: Thank you for sharing your code.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.05.13.20100131v1 on medRxiv