Genome Assembly & SARS-CoV-2 Quantification
de Bruijn genome assembly and variant quantification for viral surveillance.
Genome-assembly pipelines built on de Bruijn graphs to quantify closely related viral strains, with machine-learning-optimised k-mer selection for accuracy and robustness. The workflow was extended to SARS-CoV-2 and Omicron quantification — integrating variant calling (PySam), mutation annotation (Nextclade, CoV-GLUE), and phylogenetic tree generation (ETE3, Biopython) for large-scale genomic surveillance.
Telling apart closely related viral strains from short reads is a hard combinatorial problem — and it has to scale for real genomic surveillance.
de Bruijn-graph assembly with ML-tuned k-mer selection differentiates strains, then variant calling, mutation annotation, and phylogenetics extend it into a full SARS-CoV-2 / Omicron quantification and surveillance workflow.
- 1Optimising k-mer selection for accuracy and robustness.
- 2Differentiating strains and enhancing mutation sensitivity.
- 3Scaling the workflow for large-scale viral surveillance.
- ML-optimised k-mer selection over de Bruijn assembly.
- SARS-CoV-2 & Omicron variant calling and annotation.
- Phylogenetic trees for large-scale viral surveillance.