Jouni Sirén

The paper introduces GBZ-base and GAF-base, indexed pangenome file formats for efficient subgraph and alignment extraction.

The paper introduces a lossless indexing framework for pangenome graphs using tag arrays, enabling efficient haplotype-aware querying and coordinate translation across haplotypes.

SAFARI improves ancient DNA alignment by using RYmer indices to handle chemical damage patterns, resulting in more accurate alignments and better damage rate estimation.

The paper updates Giraffe to map both long and short reads to large pangenome graphs, showing improved speed and accuracy compared to existing tools.

This paper introduces pangenome-aware DeepVariant, a variant caller that improves accuracy by using a pangenome reference, outperforming linear-reference-based DeepVariant by up to 25.5%.

RecAlign improves recombination-aware sequence-to-graph mapping by using an A* algorithm with haplotype information and a heuristic function, achieving faster and more efficient performance.

The paper introduces a personalized pangenome approach to improve short-read mapping and variant calling by reducing reference bias and errors.

The paper presents a draft human pangenome reference that improves variant discovery and structural variant detection compared to existing references.

This paper introduces a toolchain for haplotype-aware transcript analysis using spliced pangenome graphs, improving RNA-seq mapping accuracy and enabling haplotype-specific expression quantification without prior haplotype characterization.

The paper introduces the GBZ file format for efficiently storing pangenome graphs, providing compression tools and demonstrating efficient system usage.

The paper introduces Giraffe, a pangenome mapper that improves genotyping of SNVs, indels, and SVs in 5,202 human genomes using the vg toolkit.

The paper develops efficient algorithms for distance calculation and seed clustering on sequence graphs to improve mapping algorithms.

The paper demonstrates that variation graphs using the vg toolkit improve structural variant genotyping compared to traditional methods and VCF-based catalogs.

The paper introduces haplotype-aware graph indexes to improve the representation of genetic variation in variation graphs.

The paper introduces vg, a toolkit for creating and using variation graphs as genome references, improving read mapping and variant calling accuracy over linear references.