Each of these steps is exposed as a sub-command in the CLI, so advanced users can integrate KrakenParser into custom workflows. By default, running `KrakenParser --complete -i <reports_dir>/kreports` executes all steps sequentially, writing outputs to a structured directory tree (with subfolders for each step). The outputs include one CSV file per rank (e.g. counts_phylum.csv, counts_species.csv) containing absolute read counts, and similarly named files under a `csv_relabund/` directory for percentages if requested. KrakenParser is optimized for speed and memory efficiency given the nature of the task: it processes text files line by line and uses `pandas` data frames for merging and calculations, which easily handle dozens of samples and tens of thousands of taxa on a standard workstation. The reliance on `KrakenTools` for the initial conversion ensures that the parsing logic benefits from the robustness of well-tested scripts, while the unified interface adds convenience. The tool also includes built-in help for each subcommand (`-h`), guiding users on required inputs and options. `KrakenParser`’s design reflects practical needs observed in the metagenomics community—it was tested during the 2025 “Bioinformatics Bootcamp” hackathon organized by ITMO University, where teams analyzing metagenomic datasets were able to obtain meaningful results in a short time thanks to `KrakenParser`’s streamlined processing pipeline. By combining established methods with new automation, `KrakenParser` provides an efficient, reproducible, and user-friendly means to handle the otherwise tedious steps of post-classification data processing.
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