Ben Callahan is a bioinformatician who examines the impact of microbiomes and complex microbial communities on their human and animal hosts.
Originally trained as a physicist at Iowa State University, Callahan used his experience in mathematical theory to migrate into quantitative biology and pursue his interests in evolution. During his PhD at the University of California, Santa Barbara, where he studied physics, he began to apply mathematical theory to population genetics. He continued this work at Stanford University as a postdoc by investigating adaptation in large populations through modeling, experimental evolution and comparative genomics.
At NC State’s College of Veterinary Medicine, where he is currently an assistant professor in the Department of Population Health and Pathobiology, Callahan focuses on two research areas. First, he solves problems such as bias in data collection (which could be caused by variation in detection efficiency) and errors in long-read sequencing. To do this, he develops new computational methods that account for or correct these differences.
Second, he applies high-resolution statistical bioinformatics to uncover the relationship between microbial communities and human or animal health. At present, Callahan is collaborating with researchers from Stanford University to find the link between microbial communities in different body regions with the risk of pre-term birth. Using software packages such as DADA2, which he created, he converts next-generation sequencing data into usable, census-like descriptions of a microbial community. Then, by statistical analysis, he connects biological features of the microbes with health problems or diseases to identify risk profiles or possible causation.
As a member of the Global Health program, Callahan’s expertise in computational methodologies, bioinformatics, software development and host-microbiome relationships is invaluable for global health research, particularly in disciplines such as infectious diseases, antimicrobial resistance and gastrointestinal health.
Main Areas of Expertise
- Computation methodology
- High-throughput data
Global Health Research Interests
- Developing new software and methodologies to understand the impact of microbiomes and microbial communities on animal and human hosts
- Examining the relationship between the maternal microbiome and preterm birth
- Department of Microbiology and Immunology, Stanford School of Medicine | United States
- Department of Statistics, Stanford School of Humanities & Sciences | United States
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018 Dec; 6(1): 226 (Pubmed)
Ferguson KM, Jacob ME, Theriot CM, Callahan BJ, Prange T, Papich MG, Foster DM. Dosing regimen of enrofloxacin impacts intestinal pharmacokinetics and the fecal microbiota in steers. Front Microbiol 2018 Sep; 9: 2190 (Pubmed)
Callahan BJ, Wong J, Heiner C, Oh S, Theriot CM, Gulati AS, McGill SK, Dougherty MK. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. bioRxiv 2018 Aug; DOI: https://doi.org/10.1101/392332 (Article)
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016 Jul;13(7):581 (Pubmed)
Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 2017 Dec;11(12):2639 (Pubmed)
Davis NM, Proctor D, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. bioRxiv 2017 Jan 1:221499 (Article)View more on Google Scholar
Global Health Memberships