Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation

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Abstract

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  1. SciScore for 10.1101/2021.02.17.431554: (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 eQTL associations and chromatin-state information and Hi-C interactions were processed and plotted using the R Bioconductor package gviz in R version 4.0.3 [29].
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Cell trajectory and transcriptional program analysis in TRM cells: We used the R package Slingshot [34] to infer cell transition and pseudotime from the scRNA-seq data.
    Slingshot
    suggested: (Slingshot, RRID:SCR_017012)
    DNA motif recognition analysis of genome-wide significant SNPs: We used the function “variation-scan” of the online tool RSAT (http://rsat.sb-roscoff.fr/index.php, accessed on 01/15/2020) [40] to predict the binding effect of all the significant SNPs at the 3p21.31 locus.
    RSAT
    suggested: None
    The position weight matrices (PWMs) for all the TFs were downloaded from cis-BP Database (http://cisbp.ccbr.utoronto.ca/) version 2019-06_v2.00) [41] and sequence logos representing motif binding sites were generated using R package seqLogo version 1.54.3 in R version 3.5.2.
    http://cisbp.ccbr.utoronto.ca/
    suggested: (CIS-BP, RRID:SCR_017236)
    seqLogo
    suggested: None

    Results from OddPub: Thank you for sharing your data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Another limitation is that the scRNA-seq data only had nine COVID-19 patient samples (six severe and three moderate samples), which might not provide enough statistical power at the sample level as it is commonly considered each scRNA-seq data acts like a population. Finally, the TF binding site affinity alterations were assessed based on computational prediction, therefore, the in vivo effects require experimental validation. We anticipate more and larger datasets will be released in the near future. We will apply our integrative analysis approach to such new data.

    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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