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KrakenParser Usage Guide

INSTALLATION

conda create -n krakenparser pip -y
conda activate krakenparser
pip install krakenparser

HELP

! KrakenParser -h

KrakenParser by Ilia V. Popov
usage: KrakenParser [--complete] [--kreport2mpa] [--combine_mpa]
                    [--deconstruct] [--deconstruct_viruses] [--process]
                    [--txt2csv] [--relabund] [-V]

KrakenParser: Convert Kraken2 Reports to CSV.

options:
  --complete            Run the full pipeline automated
  --kreport2mpa         Convert Kraken2 Reports to MPA Format
  --combine_mpa         Combine MPA Files
  --deconstruct         Extract Taxonomic Levels from combined MPA file
  --deconstruct_viruses
                        Extract Taxonomic Levels from combined MPA file using
                        only VIRUSES domain
  --process             Process Extracted Taxonomic Data
  --txt2csv             Convert TXT to CSV
  --relabund            Calculate relative abundance
  -V, --version         show program's version number and exit

COMPLETE PIPELINE USAGE EXAMPLE

First, download the demo data

Input

! wget https://github.com/PopovIILab/KrakenParser/raw/refs/heads/dev/demo_data.zip && \
    unzip demo_data.zip && rm -rf demo_data.zip

Then run KrakenParser

Input

! KrakenParser --complete -i demo_data/kreports/

By the key -i the path to input file(s) (kraken2 reports file) must be provided

The resulted output files:

data/
├─ kreports/           # Input Kraken2 reports
├─ mpa/                # Converted MPA files
├─ COMBINED.txt        # Merged MPA file
└─ counts/
   ├─ txt/             # Extracted taxonomic levels in TXT
   │  ├─ counts_species.txt
   │  ├─ counts_genus.txt
   │  ├─ counts_family.txt
   │  ├─ counts_order.txt
   │  ├─ counts_class.txt
   │  └─ counts_phylum.txt
   └─ csv/             # Total abundance CSV output
   │  ├─ counts_species.csv
   │  ├─ counts_genus.csv
   │  ├─ counts_family.csv
   │  ├─ counts_order.csv
   │  ├─ counts_class.csv
   │  ├─ counts_phylum.csv
   └─ csv_relabund/    # Relative abundance CSV output
      ├─ counts_species.csv
      ├─ counts_genus.csv
      ├─ counts_family.csv
      ├─ counts_order.csv
      ├─ counts_class.csv
      └─ counts_phylum.csv

Then group low abundant (<4.0%) taxa on species level

! KrakenParser --relabund -i demo_data/counts/csv/counts_species.csv -o demo_data/counts/csv_relabund/counts_species_2.csv -O 4

The resulted demo_data/counts/csv_relabund/counts_species_2.csv file:

Sample_id,taxon,rel_abund_perc
X1,Other (<4.0%),42.686249843331495
X1,Escherichia coli,12.75490546506176
X1,Haemophilus ducreyi,10.075525825933164
X1,Salmonella enterica,9.632211973651838
X1,Staphylococcus aureus,8.720517307808358
X1,Klebsiella pneumoniae,7.072132502100518
X1,Bacteroides fragilis,4.549653472470442
X1,Morganella morganii,4.508803609642424
X2,Other (<4.0%),46.04232622442552
X2,Morganella morganii,16.5117268346954
X2,Escherichia coli,15.349350872456869
X2,Salmonella enterica,10.345628717042564
X2,Klebsiella pneumoniae,7.063587503374482
X2,Haemophilus ducreyi,4.68737984800517
...
X8,Other (<4.0%),32.34802433843469
X8,Bartonella krasnovii,25.39596964453076
X8,Pediococcus pentosaceus,9.254716232772273
X8,Latilactobacillus sakei,8.688709539996534
X8,Staphylococcus aureus,7.8557850790470285
X8,Bacteroides fragilis,5.997643719867084
X8,Escherichia coli,5.454406567029118
X8,Haemophilus ducreyi,5.004744878322517
X9,Other (<4.0%),46.9787738378817
X9,Escherichia coli,18.22909641989718
X9,Salmonella enterica,10.944105961952845
X9,Klebsiella pneumoniae,9.848017408277853
X9,Staphylococcus aureus,7.364958937831675
X9,Haemophilus ducreyi,6.635047434158754

This file will be used as the input in all the visualization APIs documentation later on