SARS-CoV-2 pipeline live on EIT Pathogena Philip Fowler, 28th January 202528th January 2025 Back in the SARS-CoV-2 pandemic we worked closely with ORACLE Corp to build and deploy a bioinformatics pipeline in ORACLE Cloud that processes raw genetic files and infers what the consensus genome is and hence what lineage (e.g. BA.2) it belongs to. The heart of the pipeline is an amplicon-aware variant caller, viridian, that was written by Zamin Iqbal’s group and there is a nice preprint out showing how it avoids various calling artefacts that can complicate phylogenetic tree construction. An improved version of the pipeline has now been deployed, tested and launched on EIT Pathogena, a cloud-based genetics platform. Anyone can sign up and process at least 1,000 samples for free. Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Related clinical microbiology
antimicrobial resistance Research position advertised 26th January 202126th January 2021 Come and work with me on antimicrobial resistance! Advert here. Broadly the idea is to… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
New publication: fast human read decontamination for SARS-CoV-2 16th May 202216th May 2022 ReadItAndKeep is a new human-read decontamination algorithm that works by mapping the reads in a… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
antimicrobial resistance New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network 29th October 202530th October 2025 In previous work we’ve used “traditional” machine-learning approaches, like XGBoost, to learn and therefore predict… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More