Solutions, experiments and notes from the Algorithms for Genomic Data Analysis course at the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw.
The repository focuses on algorithmic methods used in genomic data processing, especially problems related to high-throughput sequencing data. It includes implementations and experiments around read mapping, indexing, approximate matching, metagenomic classification, k-mer analysis, motif finding and genome assembly.
Implementation of a read mapping pipeline based on reference indexing, candidate generation and edit-distance verification.
The solution explores:
- suffix-array based indexing,
- exact and approximate shingle matching,
- candidate occurrence aggregation,
- edit-distance based verification,
- testing on sample and larger datasets,
- additional scripts for inspecting and visualizing matching behavior.
Implementation of a metagenomic classification assignment focused on identifying organisms from sequencing data.
The repository includes the main classifier implementation together with helper scripts and experiments used during solution development.
The lab folders contain smaller implementations and exercises covering selected topics from the course, including:
- text indexing,
- Burrows-Wheeler Transform,
- approximate pattern matching,
- high-error read mapping,
- k-mer spectra,
- randomized motif finding,
- genome assembly.
- Python
- Jupyter Notebook
- Shell scripts
This is a coursework repository, not a production bioinformatics library. The goal was to implement, test and reason about algorithmic approaches to genomic data analysis, with emphasis on correctness, complexity and practical experimentation.