Data files

This file provides a full list of the PubMed IDs used as input to our survey of SARS-CoV2 enrichment analysis results

A list of the search terms used for identifying SARS-CoV2 studies involving enrichment analysis. A random sample of 100 papers were used as input to the experiment.

The Results of the analysis are structured in three parts:

1. The results of the main analysis
2. The results with a broader prior (Sensitivity analysis)
3. The Results of the multiple period comparison

For each part, full posterior traces for all analysis and visualizations of the paper are avalable.

Furthermore the diagnostics and traces were added for the different analysis. The trace for the mulitple comparison was to large to upload it and is available on request.

The classification in reproducible and not reproducible models was made by Tiwari et al.

Citations were looked up in Scopus, Web of Science and Google Scholar.

The following journals had to be excluded, as Journal Impact Factors (JIF) were missing or papers were discontinued:

• Experientia was closed 1996 and continued as Cellular and Molecular Life Sciences 1997
• The American journal of physiology – split into fields 1977, further splits in 1980 and 1989
• IFAC Proceedings Volumes – last issue ...

An example template for MAGE-TAB compliant transcriptomics data. This example only covers the Investigation Description Format (IDF) and the Sample and Data Relationship Format (SDRF) worksheets (i.e. the metadata from a microarray experiment). This template was generated automatically using the ArrayExpress submission help tool (http://www.ebi.ac.uk/cgi-bin/microarray/magetab.cgi)

This code uses the PEtab problem (written in the yaml file) to perform MCMC sampling with pyPESTO. Afterwards an ensemble is created, that allows to compute the posterior predictive distribution and therefore the credibility intervals for the model output.

The sampling.py scipt of pyPESTO visulize was adjusted for an improved visualization. The used code was also uploaded.

A Conda environment file (.yml) containing the specific version of Python and installed packages with the according versions, ...

Complete posterior distributions for each drug and condition.

The files are in the hdf5 format and contain the complete information of the analysis for a FAIR data sharing.

The parameters A and B were estimated for the function f(x)=x⋅e^(B-Ax) and the AUC was calculated for the decay phase only. For OH-Midazolam the fourth replicate in the 2 weeks condition has an outlier in the measurement after six hours (more than 2SD above the mean of this condition) and was omitted for the AUC calculation as this could not be fitted to an exponential decay.

CYP2E1: Generic Classification 128

• MNT-021_Bl6J_J-20-0152_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-022_Bl6J_J-20-0154_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-023_Bl6J_J-20-0156_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-024_Bl6J_J-20-0158_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-025_Bl6J_J-20-0160_CYP2E1- 1/400_Run ...

• MNT-021_Bl6J_J-20-0152_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-022_Bl6J_J-20-0154_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-023_Bl6J_J-20-0156_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-024_Bl6J_J-20-0158_CYP2E1- 1/400_Run 011_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_006 >Control
• MNT-025_Bl6J_J-20-0160_CYP2E1- 1/400_Run ...

• MNT-021_Bl6J_J-20-0152_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-022_Bl6J_J-20-0154_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-023_Bl6J_J-20-0156_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-024_Bl6J_J-20-0158_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-025_Bl6J_J-20-0160_CYP2D6- 1/3000_Run ...

• MNT-021_Bl6J_J-20-0152_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-022_Bl6J_J-20-0154_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-023_Bl6J_J-20-0156_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-024_Bl6J_J-20-0158_CYP2D6- 1/3000_Run 14_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_004 > Control
• MNT-025_Bl6J_J-20-0160_CYP2D6- 1/3000_Run ...

CYP1A2: Generic Classification 128

• MNT-021_J-20-0152_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-022_J-20-0154_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-023_J-20-0156_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-024_J-20-0158_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-025_J-20-0160_Bl6J_CYP1A2-1/500_Run ...

• MNT-021_J-20-0152_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-022_J-20-0154_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-023_J-20-0156_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-024_J-20-0158_Bl6J_CYP1A2-1/500_Run 08_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_002 > Control
• MNT-025_J-20-0160_Bl6J_CYP1A2-1/500_Run ...

CYP3A4: Generic Classification 128

• MNT-021_J-20-0152_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-022_J-20-0154_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-023_J-20-0156_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-024_J-20-0158_CYP3A4 1/2000_Run 10_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_005 > Control
• MNT-025_J-20-0160_CYP3A4 1/2000_Run 10_LLL(green), RML(red), ...

• MNT-021_J-20-0152_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-022_J-20-0154_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-023_J-20-0156_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-024_J-20-0158_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-025_J-20-0160_HE_LLL(green), RML(red), RSL (black), ICL(yellow)_MAA_003 > Control
• MNT-026_J-20-0162_HE_LLL(green), RML(red), ...

For each drug and condition, there is one:

• Visualization of the marginals and the sampling traces
• The credibility intervals for the parameters with the median
• Visualization of the individual ensemble output predictions with boxplots of the underlying data

Furthermore all conditions of the ensemble output predictions for one drug are visulized in one plot.

One example of a ReStoRunT sheet.

This file is part of a tutorial for the Gesellschaft für Ökologie and the NFDI4Biodiversity.

The file describe the methodology of the used algorithm to calculate the sensitivities against knockout and over-expression perturbations. The sensitivity values were classified in macro values for every perturbation. The bitwise distances were calculated for each biomolecule in the same macro class

Complete list of differentially expressed proteins between sub-anatomical regions. Non significant results were filtered out.

Complete list of differentially expressed proteins between sub-anatomical regions. Non significant results were filtered out.