Cancer evolution: CATE powered cancer simulator
This, like Apollo, is a forward-in-time simulator. It is designed to study the progression of Cancer cells within the host organism. It includes cancer cell initiation, development, and the onset of metastasis, as well as evolutionary analysis (via Tajima's D) of cancer, complete with tissue-specific selection.
This will be the next addition to our established CATE (CUDA Accelerated Testing of Evolution) infrastructure.
Apollo: CATE powered viral simulator
A forward-in-time within-host viral simulator that encompasses the processes of viral transmission in a population, replication dynamics, evolutionary selection, and virus behaviors at the population, host, tissue, and cell levels.
This is the newest addition to our established CATE (CUDA Accelerated Testing of Evolution) infrastructure.
Video presentation at ISMB conference
Apollo is published in Nature Communications, GitHub, GitHub Wiki, Video tutorial
CATE (CUDA Accelerated Testing of Evolution)
A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data. CATE is magnitudes faster than standard tools with benchmarks estimating it being on average over 180 times faster. For instance, CATE processes all 54,849 human genes for all 22 autosomal chromosomes across the five super populations present in the 1000 Genomes Project in less than 30 minutes while counterpart software took 3.62 days.
Video presentation at BIRS conference
CATE is published in Methods in Ecology and Evolution, GitHub, GitHub Wiki
Viral transmission inference
Using the TransPhylo R package we have developed two viral disease inference pipelines. They are for HIV AIDS and SARS-CoV-2. These pipelines are able to produce complete transmission networks complete with the inference of unsampled sources of infection using only dated sequence information. This work has been published in Microorganisms and PLOS One respectively.
Novel protein's structure and function inference pipeline
A benchmarked, modular, pipeline designed to predict, validate, and assess the function of a protein via protein modeling, structure, functional validation, and docking techniques.
This work has been published in Biology.
The trifecta protein analysis pipeline is available in Biology