Meta-analysis of MERS, SARS and COVID-19 in vitro Infection Datasets Reveals Common Patterns in Gene and Protein Expression
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Abstract
Three lethal lower respiratory tract coronavirus epidemics have occurred over the past 20 years. This coincided with major developments in genome-wide gene and protein expression analysis, resulting in a wealth of datasets in the public domain. Seven such in vitro studies were selected for comparative bioinformatic analysis through the VirOmics Playground, a visualisation and exploration platform we developed. Despite the heterogeneous nature of the data sets, several commonalities could be observed across studies and species. Differences, on the other hand, reflected not only variations between species, but also other experimental variables, such as cell lines used for the experiments, infection protocols and viral strains. The datasets analysed here are available online through our platform ( https://public.bigomics.ch/app/omicsplayground_viromics ).
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SciScore for 10.1101/2020.09.25.313510: (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 Sentences Resources Datasets were obtained from the Gene Expression Omnibus (GEO) repository, except for the proteomic dataset, which was obtained from the Proteomics Identification Database (PRIDE). Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)PRIDEsuggested: (Pride-asap, RRID:SCR_012052)Unless otherwise stated, significant differential expression (q<0.05) was calculated using the intersection between three algorithms, namely edgeR, DEseq2 and limma [26–28] (Robinson et al, 2010; Love et al, 2014; Ritchie et al, 2015), and with a minimum logFC of 0.5. edgeRsuggested: …SciScore for 10.1101/2020.09.25.313510: (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 Sentences Resources Datasets were obtained from the Gene Expression Omnibus (GEO) repository, except for the proteomic dataset, which was obtained from the Proteomics Identification Database (PRIDE). Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)PRIDEsuggested: (Pride-asap, RRID:SCR_012052)Unless otherwise stated, significant differential expression (q<0.05) was calculated using the intersection between three algorithms, namely edgeR, DEseq2 and limma [26–28] (Robinson et al, 2010; Love et al, 2014; Ritchie et al, 2015), and with a minimum logFC of 0.5. edgeRsuggested: (edgeR, RRID:SCR_012802)DEseq2suggested: (DESeq2, RRID:SCR_015687)limmasuggested: (LIMMA, RRID:SCR_010943)The same approach was also used when identifying significantly altered KEGG pathways, when performing a drug connectivity analysis or when performing a word cloud analysis, with the three algorithms being camera, GSVA and fGSEA [29–31](Wu and Smyth 2012; Hänzelmann et al, 2013; Korotkevich et al, 2019). KEGGsuggested: (KEGG, RRID:SCR_012773)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.
- No funding statement was detected.
- No protocol registration statement was detected.
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