Projects

akhal is a command-line toolkit for processing and analyzing r/GFA (reference Graphical Fragment Assembly) files, the graph format used by modern pangenome pipelines. It validates graph structure, computes summary statistics over the graph, and converts GAF (Graphical Alignment Format) alignments into the widely supported SAM (Sequence Alignment Map) format, bringing graph-based alignments into the standard samtools ecosystem.

LCPan is a novel method for constructing variation (pangenome) graphs that leverages Locally Consistent Parsing (LCP) to incorporate genomic variations efficiently. Where existing tools suffer from high, super-linear memory growth as the number of variants increases, LCPan guarantees linear memory scalability by partitioning sequences into consistent cores, so graphs can be built quickly, in parallel, and with a small memory footprint at whole-genome scale.

GenCore estimates genomic distances for comparing sequences without relying on the hashing and sketching techniques used by most alignment-free tools. Instead it applies Locally Consistent Parsing (LCP), processing strings iteratively and partitioning genomic data into small, reproducible modules called cores. Shared cores between genomes yield highly accurate genetic-distance estimates, which GenCore uses to reconstruct reliable phylogenetic trees with low computational overhead.

lcptools is the first practical, high-performance implementation of Locally Consistent Parsing (LCP), written in C. LCP partitions and labels a string into substrings of nearly uniform length called cores; unlike sketching, it guarantees a uniform positional distribution of cores, that identical cores receive identical labels, and that every character belongs to at least one core. lcptools computes these cores efficiently on very large datasets and provides the foundation for both GenCore and LCPan.

SVTrek is a tool for Structural Variation (SV) discovery performed directly on sequence-to-graph alignments. It aims to detect large genomic rearrangements in the pangenome-graph setting, rather than against a single linear reference, so that variation is captured with the richer context a graph representation provides.