Krešimir Križanović

StrainRefine improves strain-level metagenomic classification by refining read mappings using profile similarity, reducing false positives while maintaining accuracy.

MADRe improves strain-level metagenomic classification by combining assembly-driven database reduction with probabilistic read mapping, achieving higher precision and fewer false positives than existing tools.

The paper presents a high-quality, gapless diploid reference genome for an Indian population, revealing extensive structural variation and improved performance for genomic analysis in South Asian samples.

The paper benchmarks metagenomic classification tools for long-read sequencing data, finding that general-purpose mappers outperform classification tools in accuracy but are slower, while kmer-based tools are faster but less accurate.

Graphmap2 improves transcript detection and exon boundary recognition using long-read RNA-seq data, outperforming existing tools in accuracy and recall.

This study evaluates RNA-seq splice-aware alignment tools for long-read sequencing technologies, showing that some tools perform well with error correction.

The paper introduces a modified RNA mapper based on GraphMap for third-generation sequencing data, showing improved mapping quality over existing tools.