Mikhail Karasikov

The paper develops a scalable indexing framework for biological sequences, enabling efficient and cost-effective full-text search across vast genomic datasets.

This paper introduces a new alignment method for annotated De Bruijn graphs that improves taxonomic classification accuracy and coverage for long reads.

This paper introduces Counting de Bruijn graphs for efficiently indexing and querying sequencing data, achieving smaller storage and faster construction compared to existing methods.

The paper introduces RowDiff, a method to compactly store graph labels by exploiting annotation similarities, achieving significant space reductions when combined with compression techniques.

This paper introduces a new compression method for de Bruijn graph labels, showing improved performance over existing methods on real-world datasets.

The paper introduces two compression methods for graph coloring in sequencing data, reducing memory use by up to three orders of magnitude while maintaining dynamic graph topology.

The paper introduces a dynamic data structure called Metannot for compressing and indexing graph metadata in HTS data, achieving better compression ratios than existing methods.