Infectious disease research
Discover better vaccines, treatments and outcomes with full characterization of pathogens, their mobile elements and communities.
Accelerate your research with in-depth genomic profiling
Comprehensive genetic information provided by Single Molecule, Real-Time (SMRT) sequencing has radically increased our understanding of microbial genomics, pathogenicity, and antimicrobial resistance. The PacBio systems empower scientists to:
- Generate closed microbial genomes and plasmids to uncover the drivers of pathogenicity, virulence, and drug resistance
- Resolve viral populations to unravel host pathogen dynamics and advance the design and evaluation of drugs and vaccines
- Characterize microbial populations to understand how the microbiome impacts human health
Learn how other scientist have used SMRT sequencing to advance infectious disease research:
- Unraveling Malaria Mysteries with Long-Read Sequencing
- Egyptian Rousette Bat Genome Provides Clues to Antiviral Mystery
- Data Release: Zika-Susceptible Aedes aegypti De Novo Genome Assembly
- PacBio-Powered Analysis Indicates Methylation Makes TB Pathogen So Wily
- Sequencing 101: Why Are Long Reads Important for Studying Viral Genomes?
- Full-Length HIV Sequences Reveal Distinction Between Viruses in Brain, Other Tissue
Spotlight
Decipher genome complexity in pathogens using SMRT sequencing
Scientists were able to successfully work through the well-known AT-bias challenge of the Plasmodium falciparum genome. De novo assembly revealed novel insights for genome architecture and complexity, providing a reference to better understand virulence, drug resistance and disease transmission. Explore this research further:
Vembar, S.S. et al., 2016. Complete telomere-to-telomere de novo assembly of the Plasmodium falciparum genome through long-read (>11 kb), single molecule, real-time sequencing. DNA Research, 23(4), pp.339–351.
Spotlight
Single-molecule sequencing to track HIV-1 mutations
The development of novel circular consensus algorithms enabled the tracking of viral evolution of the K103N drug resistant associated mutation in clinical samples. Explore this research further:
Laird Smith, M. et al., 2016. An improved circular consensus algorithm with an application to detect HIV-1 Drug Resistance Associated Mutations (DRAMs). In 2016 ASM Microbe. New Orleans, LA.