This is a method and software that adds a dynamic aspect to adaptive sampling on ONT’s nanopore sequencing platform
MAPLE
This is a maximum likelihood phylogenetic software tailored to large genomic datasets of closely related sequences such as SARS-CoV-2 genomes
ωAI
This is a novel approach to detecting natural selection using Convolutional Neural Networks (CNNs), which outperforms traditional statistical methods prone to false positives due to alignment errors and high indel rates in some cases. By training the CNN on simulated sequences labeled for positive selection, we achieve higher accuracy, and utilise saliency maps for site-wise detection. Future work will focus on exploring different model architectures to further improve prediction accuracy and generalisability.
ProID
In this project, we are developing computational methods to identify proteins using plasmonic nanopore and ultrafast Raman spectroscopy.
PEAR
This tool is intended for comparison of large collections of phylogenetic trees. It calculates distances, embeds them in a lower dimensional space and plots the embeddings with a user-friendly dynamic interface. Pear is both a python software and library.