Di Genoma Lab software

Software development is part of the lab’s core research activity. We build methods, pipelines, and utilities for large-scale genomics with three priorities:

• biological relevance, so tools address real questions in cancer and genome research
• reproducibility, using workflow systems and containerized execution
• performance, so methods remain practical on institutional and HPC-scale datasets

Most of our software is designed to run efficiently on clusters such as Kütral and is integrated with Nextflow and container-based deployment.

Genome assembly and genome reconstruction

1. Wengan: hybrid genome assembler for short- and long-read data with strong speed and accuracy.
2. Fast-SG: alignment-free scaffolding graph construction from sequencing reads.
3. FastKM: ultra-fast k-mer matching with rolling and perfect hashing.
4. hic-scaffolding-nf: Nextflow pipeline for improving assemblies with Hi-C data.
5. k-count: k-mer counting workflow for genome size estimation from WGS data.

Cancer genomics

1. purple-nf: Nextflow workflow for somatic copy-number analysis with PURPLE.
2. sv_somatic_cns: consensus pipeline for somatic structural variant discovery from paired WGS data.
3. ampliconarchitect-nf: workflow for identifying extrachromosomal DNA in cancer genomes.
4. nf-gene-fusions: pipeline for calling somatic mRNA fusions.

General utilities

1. alnsl: short-read alignment workflow for WGS analysis.
2. longreadstats: best-practice long-read QC pipeline based on NanoPlot.

Engineering approach

Our tools are designed to support real research programs, not isolated demos. That means we care about:

• containerized and versioned execution
• workflow portability across servers and clusters
• reproducible inputs, outputs, and parameterization
• software that scales from pilot experiments to large institutional datasets

If you are interested in using one of these tools, adapting a workflow for your own infrastructure, or collaborating on a new method, please get in touch.