This is the repository containing the code used to obtain the results shown in Bellato M, Cappellato M, Longhin F, Del Vecchio C, Brancaccio G, Cattelan AM, Brun P, Salaris C, Castagliuolo I, Di Camillo B. "Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+" (2023).

We characterized through 16S rDNA-seq the microbiota in the upper airways of 192 subjects with a positive nasopharyngeal swab for SARS-CoV-2 to identify a microbial signature predictive of disease prog (...)

This is the repository containing the code used to obtain the results shown in Bellato M, Cappellato M, Longhin F, Del Vecchio C, Brancaccio G, Cattelan AM, Brun P, Salaris C, Castagliuolo I, Di Camillo B. "Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+" (2023).

We characterized through 16S rDNA-seq the microbiota in the upper airways of 192 subjects with a positive nasopharyngeal swab for SARS-CoV-2 to identify a microbial signature predictive of disease progression. Patients were divided in groups based on the presence of symptoms, the level of pneumonia, and whether or not they needed oxygen therapy or intubation. 

In the GitLab repository here there are all the scripts used to perform the preprocessing step and the downstream analysis. The GitLab repository (version 1.0) contains also the Docker image microbiomecovid:1.0.0 that can be used to reproduce the results shown in the paper (see the instruction here).

Here in Zenodo you can find the microbiomecovid_data.zip file containing two folders that need to be unzipped and put in the microbiomecovid local repository, downloaded from GitLab.

In particular, the original_data folder contains:

• Raw_data: a folder with two FASTQ files for each sample, i.e., forward (R1) and reverse (R2) reads.
• Metadata.xlsx: the table containing information on the - anonymized - subjects involved in the study.
• QC Report.pdf: The report provided by the sequencing center.

Additionally, in the output folder, the following items can be found:

• Preprocessing: a folder containing all the output file from step1 to step7, namely:
  • create input data for QIIME2;
  • import data in QIIME2;
  • remove primers;
  • denoising and imputation;
  • taxonomy classification;
  • phylogenetic tree reconstruction;
  • collapse at specific taxonomic level and normalize data.
• Analysis:  a folder containing all the output files form step8 to step10 namely:
  • alpha and beta diversity results;
  • DA_output: a folder containing the differential abundance analysis performed for each taxonomic level and for each covariate;
  • Network_output: a folder containing the sparCC and Cytoscape networks and results.

For more info about all the bioinformatic pipeline see the GitLab repository.

Remember to set the path as ABSOLUTE_PATH_MICROBIOMECOVID.