The trans-omics landscape of COVID-19

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1. Version published to 10.1038/s41467-021-24482-1

   Jul 27, 2021
2. 

   Version published to 10.21203/rs.3.rs-59060/v1 on Research Square

   Aug 31, 2020
3. 

   ScreenIT

   Jul 25, 2020

   SciScore for 10.1101/2020.07.17.20155150: (What is this?)

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

   Table 1: Rigor

   Institutional Review Board Statement	IRB: Ethics Statement: This study was reviewed and approved by the Institutional Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20200405). Consent: All the enrolled patients signed an informed consent form, and all the blood samples were collected using the rest of the standard diagnostic tests, with no burden to the patients.
   Randomization	Sequencing Library Construction and Data Generation: The whole genome data was generated through the following steps: 1) DNA was randomly fragmented by Covaris.
   Blinding	not detected.
   Power Analysis	not detected.
   Sex as a biological variable	The mean age of the patients …

   SciScore for 10.1101/2020.07.17.20155150: (What is this?)

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

   Table 1: Rigor

   Institutional Review Board Statement	IRB: Ethics Statement: This study was reviewed and approved by the Institutional Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20200405). Consent: All the enrolled patients signed an informed consent form, and all the blood samples were collected using the rest of the standard diagnostic tests, with no burden to the patients.
   Randomization	Sequencing Library Construction and Data Generation: The whole genome data was generated through the following steps: 1) DNA was randomly fragmented by Covaris.
   Blinding	not detected.
   Power Analysis	not detected.
   Sex as a biological variable	The mean age of the patients was 46.7 years old (Standard Deviation=13.5), and the ratio of male to female was 1.12:1.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Sequencing Library Construction and Data Generation: The whole genome data was generated through the following steps: 1) DNA was randomly fragmented by Covaris.	Covaris suggested: None
   Sequencing reads were mapped to hg38 reference genome using BWA algorithm (Li and Durbin, 2009).	BWA suggested: (BWA, RRID:SCR_010910)
   Variant Quality Score Recalibration was performed using Genome Analysis Toolkit (GATK version 4.1.2)	Genome Analysis Toolkit suggested: None GATK suggested: (GATK, RRID:SCR_001876)
   Genotype-Phenotype Association Analysis: PCA was performed using PLINK (v1.9) (Chang et al., 2015).	PLINK suggested: (PLINK, RRID:SCR_001757)
   Gene Expression Analysis: RNA-seq raw sequencing reads were filtered by SOAPnuke (Li et al., 2008) to remove reads with sequencing adapter, with low-quality base ratio (base quality < 5) > 20%, and with unknown base (’N’ base) ratio > 5%	SOAPnuke suggested: (SOAPnuke, RRID:SCR_015025)
   After data filtering, the clean reads were mapped to the reference genome and other sRNA database including miRbase	miRbase suggested: (miRBase, RRID:SCR_017497)
   siRNA, piRNA and snoRNA using Bowtie2 (Langmead and Salzberg, 2012).	Bowtie2 suggested: (Bowtie 2, RRID:SCR_016368)
   Particularly, cmsearch (Nawrocki and Eddy, 2013) was performed for Rfam mapping.	Rfam suggested: (Rfam, RRID:SCR_007891)
   Differential expression analysis was performed using DESeq2 (v1.4.5) (Love et al., 2014) with gender and age as confounders to control for the additional variation and the detection cutoff was set as adjusted P < 0.05 and log2 of fold change ≥ 1.	DESeq2 suggested: (DESeq, RRID:SCR_000154)
   , gene ontology (GO) enrichment analysis were performed to obtain the enriched GO Biological Process terms of proteins in different clusters by clusterProfiler (Yu et al., 2012).	GO Biological suggested: None clusterProfiler suggested: (clusterProfiler, RRID:SCR_016884)
   And the 7 lists of metabolites in KEGG ID were classified into pathways by the Kyoto encyclopedia of genes and genomes (KEGG) database.	KEGG suggested: (KEGG, RRID:SCR_012773)
   Correlation Network Analysis: Pairwise Spearman’s rank correlations were calculated using the r package ‘Hmisc’ and weighted, undirected networks were plotted with Cytoscape.	Cytoscape suggested: (Cytoscape, RRID:SCR_003032)
   Data Preprocessing for Machine Learning: Extreme gradient boosting (XGBoost) (Chen and Guestrin, 2016), an ensemble algorithm of decision trees, was developed to predict patient severity status based on multi-omics data of mRNA transcripts (n=13323, mRNAs with FPKM >1 in at least one sample were retained), proteins (n=634), metabolites (n=814), lipids (n=742) from 135 patients (asymptomatic n=53, mild n=39, severe n=27, and critical n=16)using the open-sourced Python package (https://xgboost.readthedocs.io/en/latest/, version=1.0.0).	Python suggested: (IPython, RRID:SCR_001658)

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   Results from OddPub: Thank you for sharing your data.

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   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.

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 41. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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   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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4. 

   Version published to 10.1101/2020.07.17.20155150v1 on medRxiv

   Jul 22, 2020