A nextflow workflow created to predict functions involving major biogeochemical cycles (carbon, sulfur, nitrogen) for taxonomic affiliations (that can be created from metabarcoding or metagenomic sequencing). It relies on EsMeCaTa and bigecyhmm.

• Nextflow: to run the workflow.
• esmecata, bigecyhmm and several python packages for visualisation: they can be installed with the following pip command: pip install esmecata bigecyhmm seaborn pandas plotly kaleido.
• esmecata precomputed database: it can be downloaded from this Zenodo archive. This precomputed database size is 4 Gb.

This workflow can be called by nextflow in two ways:

• by downloading this repository and calling the tabigecy.nf file with nextflow run tabigecy.nf ....
• by calling the GitHub repository in the nextflow command with nextflow run ArnaudBelcour/tabigecy ....

You can print the help with the following command:

nextflow run ArnaudBelcour/tabigecy --help

By default, the script will be using files in the directory where the script has been launched. It uses 3 files:

• EsMeCaTa input file, looking like this:

• EsMeCaTa precomputed database, available here.

Optionally, it can take:

• Abundance file containing the abundance in different samples for the different rows of the EsMeCaTa input file, looking like this:

At the end, it will create an output folder containing the output folders of EsMeCaTa, the one of bigecyhmm and the visualisation output folder. To do this on your own file you can specify the input files with the command line:

nextflow run ArnaudBelcour/tabigecy --infile esmecata_input_file.tsv --inAbundfile abundance.tsv --precomputedDB esmecata_database.zip --outputFolder output_folder --coreBigecyhmm 5

An output folder (by default called output_folder) is created. It contains three subfolders:

• output_1_esmecata: the output folder of the esmecata precomputed command. For more information, look at EsMeCaTa readme.
• output_2_bigecyhmm: the output folder of bigecyhmm command. For more information, look at bigecyhmm readme.
• output_3_visualisation: the output folder for the visualisation of the predictions and (if given) the addition of sample abundances.

output_1_esmecata
├── 0_proteomes
  ├── association_taxon_taxID.json
  ├── proteome_tax_id.tsv
  ├── esmecata_metadata_proteomes.json
  ├── stat_number_proteome.tsv
  ├── taxonomy_diff.tsv
├── 1_clustering
  ├── computed_threshold
  │   └── Taxon_name_1.tsv
  │   └── ...
  ├── reference_proteins_consensus_fasta
  │   └── Taxon_name_1.faa
  │   └── ...
  ├── proteome_tax_id.tsv
  ├── esmecata_metadata_clustering.json
  ├── stat_number_clustering.tsv
├── 2_annotation
  ├── annotation_reference
  │   └── Cluster_1.tsv
  │   └── ...
  ├── pathologic
  │   └── Cluster_1
  │       └── Cluster_1.pf
  │   └── ...
  │   └── taxon_id.tsv
  ├── function_table.tsv
  ├── esmecata_metadata_annotation.json
  ├── stat_number_annotation.tsv
├── esmecata_metadata_precomputed.json
├── esmecata_precomputed.log
├── organism_not_found_in_database.tsv
├── stat_number_precomputed.tsv

association_taxon_taxID.json contains for each observation_name the name of the taxon and the corresponding taxon_id found with ete3.

proteome_tax_id.tsv contains the name, the taxon_id and the proteomes associated with each observation_name.

esmecata_metadata_proteomes.json is a log about the Uniprot release used and how the queries ware made (REST or SPARQL). It also gets the metadata associated with the command used with esmecata and the dependencies.

stat_number_proteome.tsv is a tabulated file containing the number of proteomes found for each observation name.

taxonomy_diff.tsv is a tabulated file indicating the taxon selected by EsMeCaTa compared to the lowest taxon in the taxonomic affiliations.

The computed_threshold folder contains the ratio of proteomes represented in a cluster compared to the total number of proteomes associated with a taxon. If the ratio is equal to 1, it means that all the proteomes are represented by a protein in the cluster, 0.5 means that half of the proteoems are represented in the cluster. This score is used when giving the -t argument.

The reference_proteins_consensus_fasta contains the consensus proteins associated with a taxon name for the cluster kept after clustering process.

The proteome_tax_id.tsv file is the same than the one created in esmecata proteomes.

esmecata_metadata_clustering.json is a log about the the metadata associated with the command used with esmecata and the dependencies.

stat_number_clustering.tsv is a tabulated file containing the number of shared proteins found for each observation name.

The annotation_reference contains the prediction of eggnog-mapper for the consensus protein of each observation_name. To create this file, EsMeCaTa finds the taxon name associated with the observation_name and extracts the annotation (EC numbers, GO termes, KEGG reaction).

The pathologic folder contains one sub-folder for each observation_name in which there is one PathoLogic file. There is also a taxon_id.tsv file which corresponds to a modified version of proteome_tax_id.tsv with only the observation_name and the taxon_id. This folder can be used as input to mpwt to reconstruct draft metabolic networks using Pathway Tools PathoLogic.

The file function_table.tsv contains the EC numbers and GO Terms present in each observation name.

The esmecata_metadata_annotation.json serves the same purpose as the one used in esmecata proteomes to retrieve metadata about Uniprot release at the time of the query. It also gets the metadata associated with the command used with esmecata and the dependencies.

stat_number_annotation.tsv is a tabulated file containing the number of GO Terms and EC numbers found for each observation name.

output_2_bigecyhmm
├── diagram_figures
  ├── carbon_cycle.png
  ├── nitrogen_cycle.png
  ├── other_cycle.png
  ├── sulfur_cycle.png
├── diagram_input
  └── Taxon_name_1.txt
  └── ...
├── hmm_results
  └── Taxon_name_1.tsv
  └── ...
├── bigecyhmm.log
├── bigecyhmm_metadata.json
├── function_presence.tsv
├── pathway_presence.tsv
├── pathway_presence_hmms.tsv
├── Total.R_input.txt

Four png files each showing the percentage of taxon having each functions for carbon, sulfur, nitrogen and other cycles.

One txt file for each taxon analysed. It shows the presence/absence of the major functions of the biogeochemical cycles.

One tsv file for each taxon considered. It indicates matches between input protein sequences and HMMs.

It contains several figures and their associated input files.

output_3_visualisation
├── bigecyhmm_visualisation.log
├── boxplot_function_abundance_ratio_sample.png
├── boxplot_function_ratio_sample.png
├── hmm_cycleboxplot_community.tsv
├── hmm_cycleboxplot_sample.tsv
├── hmm_cycleboxplot_sample_abundance.tsv
├── hmm_gene_community.tsv
├── polar_plot_merged.png
├── swarmplot_function_ratio_community.png