This "IDSC" readme.txt file was generated on 20231020 by Daniel Spari ------------------- GENERAL INFORMATION ------------------- 1. Title of dataset: Intestinal Dysbiosis as an intraoperative predictor of Septic Complications: Evidence from Human Surgical Cohorts and Preclinical Models of Peritoneal Sepsis 2. Contributor information: Name: Daniel Spari Role/Function: Data creator, first author Institution: Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland Address: Murtenstrasse 35, 3008 Bern Email: daniel.spari@unibe.ch Name: Simone N. Zwicky Role/Function: Data creator, co-author Institution: Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland Address: Murtenstrasse 35, 3008 Bern Email: simonenora.zwicky@insel.ch Name: Bahtiyar Yilmaz Role/Function: Data creator, co-author Institution: Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland Address: Murtenstrasse 35, 3008 Bern Email: bahtiyar.yilmaz@unibe.ch Name: Lilian Salm Role/Function: Data creator, co-author Institution: Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland Address: Murtenstrasse 35, 3008 Bern Email: lilian.salm@unibe.ch Name: Daniel Candinas Role/Function: Project member, co-author Institution: Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland Address: Freiburgstrasse, 3010 Bern Email: daniel.candinas@insel.ch Name: Guido Beldi Role/Function: PI, project manager, last author, contact person Institution: Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland Address: Freiburgstrasse, 3010 Bern Email: guido.beldi@insel.ch 3. Date of data collection: 2017-2023 4. Geographic location of data collection: Inselspital, Bern, Switzerland 5. Keywords describing the subject of your dataset: gut microbiota, dysbiosis, surgery, surgical complications, septic complications, vancomycin 6. Information about funding sources that supported the collection of the data: Funding agency name: Swiss National Science Foundation Grant number: Project No. 166594 ------------------- SHARING/ACCESS INFORMATION ------------------- 1. Licenses/restrictions placed on the data: CC BY 2. Links to publications that cite or use the data: Paper under review "Intestinal Dysbiosis as an intraoperative predictor of Septic Complications: Evidence from Human Surgical Cohorts and Preclinical Models of Peritoneal Sepsis" Spari D., Zwicky S.N., Yilmaz B., Salm L., Candinas D., Beldi G. Scientific Reports 3. Links to other publicly accessible locations of the data: None 4. Links/relationships to additional data sets: Used samples are listed in this file: "rectal_resection_samples_used.xlsx" and can be found in this repository: https://figshare.com/s/077130437ca8ac314386 5. Was data derived from another source? yes/no. yes, see nb. 4 above --------------------- DATA & FILE OVERVIEW --------------------- 1. File List: The following files are in the repository: IDSC_readme.txt Chip186.fastq.gz Chip186.tsv Chip186_100_ubelix.sh Chip187.fastq.gz Chip187.tsv Chip187_100_ubelix.sh Chip3.fastq.gz Chip3.tsv Chip3_100_ubelix.sh BORIS_portal_final_code.R Supplementary_tables.xlsx rectal_resection_samples_used.xlsx figure1_Chip2_3.tsv figure1_proteos_transient.xlsx figure1_rectal_resection_content.xlsx figure1_rectal_resection_mucosa.xlsx figure1_relabundance_plots_rectal_duodenopancreatic.csv figure1_rooted-tree_Chip2_3.qza figure1_table_Chip2_3.qza figure1_taxonomy_Chip2_3.qza figure2_Chip2_3.tsv figure2_rooted-tree_Chip2_3.qza figure2_table_Chip2_3.qza figure2_taxonomy_Chip2_3.qza figure3_4_CTL799.fastq figure3_4_CTL800.fastq figure3_4_CTL801.fastq figure3_4_CTL802.fastq figure3_4_CTL803.fastq figure3_4_CTL804.fastq figure3_4_CTL805.fastq figure3_4_CTL806.fastq figure3_4_CTL807.fastq figure3_4_CTL808.fastq figure3_4_VAN809.fastq figure3_4_VAN810.fastq figure3_4_VAN811.fastq figure3_4_VAN812.fastq figure3_4_VAN813.fastq figure3_4_VAN814.fastq figure3_4_VAN815.fastq figure3_4_VAN816.fastq figure3_4_VAN817.fastq figure3_4_VAN818.fastq figure3_4_untr. 1 1000.fcs figure3_4_untr. 10 1000.fcs figure3_4_untr. 11 10000.fcs figure3_4_untr. 12 10000.fcs figure3_4_untr. 13 10000.fcs figure3_4_untr. 14 10000.fcs figure3_4_untr. 15 10000.fcs figure3_4_untr. 16 10000.fcs figure3_4_untr. 17 10000.fcs figure3_4_untr. 18 10000.fcs figure3_4_untr. 19 10000.fcs figure3_4_untr. 2 1000.fcs figure3_4_untr. 20 10000.fcs figure3_4_untr. 3 1000.fcs figure3_4_untr. 4 1000.fcs figure3_4_untr. 5 1000.fcs figure3_4_untr. 6 1000.fcs figure3_4_untr. 7 1000.fcs figure3_4_untr. 8 1000.fcs figure3_4_untr. 9 1000.fcs figure3_4_vanco 1 1000.fcs figure3_4_vanco 10 1000.fcs figure3_4_vanco 11 10000.fcs figure3_4_vanco 12 10000.fcs figure3_4_vanco 13 10000.fcs figure3_4_vanco 14 10000.fcs figure3_4_vanco 15 10000.fcs figure3_4_vanco 16 10000.fcs figure3_4_vanco 17 10000.fcs figure3_4_vanco 18 10000.fcs figure3_4_vanco 19 10000.fcs figure3_4_vanco 2 1000.fcs figure3_4_vanco 20 10000.fcs figure3_4_vanco 3 1000.fcs figure3_4_vanco 4 1000.fcs figure3_4_vanco 5 1000.fcs figure3_4_vanco 6 1000.fcs figure3_4_vanco 7 1000.fcs figure3_4_vanco 8 1000.fcs figure3_4_vanco 9 1000.fcs figure3_4_Bact_line.csv figure3_4_Chip186_187.tsv figure3_4_Firm_line.csv figure3_4_Pacbio_data.xlsx figure3_4_Prot_line.csv figure3_4_graphs_monitoring.pzfx figure3_4_rooted-tree_Chip186_187.qza figure3_4_table_Chip186_187.qza figure3_4_taxonomy_Chip186_187.qza figure5_Compensation Controls_APC Stained Control_021.fcs figure5_Compensation Controls_APC-Cy7 Stained Control_023.fcs figure5_Compensation Controls_Alexa Fluor 488 Stained Control_019.fcs figure5_Compensation Controls_Alexa Fluor 700 Stained Control_022.fcs figure5_Compensation Controls_AmCyan Stained Control_025.fcs figure5_Compensation Controls_BUV395 Stained Control_033.fcs figure5_Compensation Controls_BV605 Stained Control_026.fcs figure5_Compensation Controls_BV650 Stained Control_027.fcs figure5_Compensation Controls_BV711 Stained Control_028.fcs figure5_Compensation Controls_BV786 Stained Control_029.fcs figure5_Compensation Controls_PE Stained Control_030.fcs figure5_Compensation Controls_PE-CF594 Stained Control_031.fcs figure5_Compensation Controls_PE-Cy7 Stained Control_032.fcs figure5_Compensation Controls_PerCP Stained Control_020.fcs figure5_Compensation Controls_V450 Stained Control_024.fcs figure5_ZN1_untreated1.fcs figure5_ZN1_untreated2.fcs figure5_ZN1_untreated3.fcs figure5_ZN1_vanco1.fcs figure5_ZN1_vanco2.fcs figure5_ZN1_vanco3.fcs figure5_Chip186_187_ZN1subset.tsv figure5_KO_pred_metagenome_unstrat_descrip.tsv figure5_OTUclass.tsv figure5_cell_populations.xlsx figure5_cytokinesPF.xls - All Chip*.fastq.gz files are raw Ion Torrent sequencing files. Together with the corresponding *.tsv (which includes metadata) and the corresponding *.sh files, they were run on the UNIBE Ubelix cluster and resulted in the *.qza files, which are used for all the figures, named accordingly. - The file "BORIS_portal_final_code.R" contains all code to recreate the figures and comments to recreate them is included. - The file "Supplementary_tables.xlsx" contains tables of enriched pathways for Figure 5 c,d,e - The file "rectal_resection_samples_used.xlsx" contains sample names, which are used in Figure 1 and can be found here: https://figshare.com/s/077130437ca8ac314386 For the following files after the general ones: - The file prefix names the figure, the files are used for - All figure1* files are read in and analysed using the R script, which contains detailed information about the subfigures - All figure2* files are read in and analysed using the R script, which contains detailed information about the subfigures - All figure3_4*.fastq files are raw PacBio DNA sequencing reads. These reads result in the Bacteria-Sample table as seen in "Pacbio_data.xlsx", sheet 1 Taxonomy. They need to be analyzed using commercial SB Analyzer software. - All figure3_4*.fcs files are used for absolute bacterial quantification in the according Figure 3g,h,j - "figure3_4_graphs_monitoring.pzfx" contains data for Figure 4b,i - All other figure3_4* files are read in and analysed using the R script, which contains detailed information about the subfigures - All figure5*.fcs files result in the file "cell_populations.xlsx", which is subsequently used in R for Figure 5a - All other figure5* files are read in and analysed using the R script, which contains detailed information about the subfigures 2. Relationship between files: see above 3. Are there multiple versions of the dataset? yes/no no -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: See in the paper under "Methods" 2. Methods for processing the data: See in the paper under "Methods" 3. Instrument- or software-specific information needed to interpret the data: See further down 4. Standards and calibration information: Not applicable 5. Environmental/experimental conditions: Not applicable 6. Describe any quality-assurance procedures performed on the data: Not applicable 7. People involved with sample collection, processing, analysis and/or submission: Spari D., Zwicky S.N., Yilmaz B., Salm L. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: ----------------------------------------- A command-line interface, python 3, anaconda, qiime2 and preferentially a high performance computing cluster is needed to analyze raw Ion Torrent sequencing files. SB Analyzer software is needed to analyze PacBio DNA sequencing files. All *.tsv, *.xlsx, *.csv and *.qza files are analysed using R software. The R script is in the repository and comments about the subfigures and variables are explained in the script. 1. Number of variables: Not applicable 2. Number of cases/rows: Not applicable 3. Variable List: Not applicable 4. Missing data codes: None 5. Specialized formats of other abbreviations used - .qza files are qiime2 artifacts, which are analysed using qiime2 software. This needs in addition python 3 and anaconda to run. These files can also be analysed using R software - .fcs files are flow cytometry files, which need a dedicated software to be analysed (FCS Express, FlowJo) - .pzfx file is a GraphPad Prism file, which needs the corresponding software.