Company
Company Overview
Virginia-based bioinformatics software company developing pathogen-specific solutions for virologists
Recipient of multiple Federal Small Business Innovation Research (SBIR) awards from the NIH and the NSF, and Virginia State awards from the Virginia Innovation Partnership Corporation (VIPC)
Team of multicultural and globally recognized, prestigious scientists and IT specialists
Developing proprietary algorithms, analytical tools, and validation testing models
Key partnerships established with:
- Laboratory of Biochemical Pharmacology (LOBP), Emory University
- The Forum for Collaborative HIV Research
- University of Connecticut
Key emerging partnerships with:
- Monogram Biosciences / Labcorp, Inc
- Intrinsic Life Sciences, Inc
GATACA solutions are developed around the unique characteristics of viruses and their life cycles and are designed to be readily adaptable to other pathogens
Our Company
We are proud to be backed by several SBIR awards and the support of our esteemed collaborators.
Our primary focus is revolutionizing the bioinformatics of the Hepatitis B virus (HBV) and Human Immunodeficiency virus (HIV) through deep machine learning (ML). Our proprietary pipeline, GAT/ML™, ushers in a new era of viral NGS de novo assembly, identifying biomarkers of classification, predicting a gene’s function, and creating precise haplotype and quasispecies reconstructions by learning the ‘syntax’ of infecting viral variants.
Benchmarked as superior against leading viral assemblers in both in vitro experiments and in vivo patient sample studies, GAT/ML™ continues to be trained and enhanced using our expanding proprietary database. At GATACA, we are shaping the future of virology with AI.
GATACA is at the forefront of developing cutting-edge software solutions, empowered by Artificial Intelligence (AI), for virologists worldwide. We are proud to be backed by SBIR awards from esteemed organizations like the National Science Foundation (NSF) and the National Institutes of Health (NIH/NIAID).
Our primary focus is revolutionizing the bioinformatics of the Hepatitis B virus (HBV) and Human Immunodeficiency virus (HIV) through deep machine learning (ML). Our proprietary pipeline, GAT/ML™, ushers in a new era of viral NGS de novo assembly, identifying biomarkers of classification, predicting a gene’s function, and creating precise haplotype and quasispecies reconstructions by learning the ‘syntax’ of infecting viral variants.
Benchmarked as superior against leading viral assemblers in both in vitro experiments and in vivo patient sample studies, GAT/ML™ continues to be trained and enhanced using our expanding proprietary database. At GATACA, we are shaping the future of virology with AI.
HBV and HIV both establish quasispecies in their mono- and co-infected hosts. Quasispecies are closely related variant strain distributions, arising through viral replication and evolving in the host
Quasispecies are tenacious drivers of hepatitis and HIV disease pathology
Yet, quasispecies characteristics across the full mutation spectra are inaccessible
High throughput next-generation sequencing (NGS) and specialized, trained bioinformatics algorithms are required to learn patient-specific quasispecies and predict disease and transmission course
Applications for GAT/ML™ are in labs using NGS for virology discovery
- Academic scientists in research and multidisciplinary translational settings, to eliminate duplication, increase time to publication, pinpoint problems, and maximize grant resources.
- Antiviral drug developers screening drug candidates for emerging resistance, enabling early ‘kill decisions’ to avoid high-risk compound advancement.
- Clinical scientists running trials on vetted drugs, to improve patient selection and perform on-trial monitoring.
GAT/ML™ trained on a vast knowledge base of HBV and HIV patient variations processes a high NGS volume
and profiles existing quasispecies harboring potentially dangerous variants with speed, precision
and unprecedented reproducibility.
GAT/ML clusters HBV sequences into its 10 distinct genotypes, with subtypes also grouped within all major clusters:
