Assistant Professor of Biomedical Informatics
Harvard Medical School
Dr. Farhat is an Assistant Professor of of
Biomedical Informatics atHarvard Medical School and a a practicing physician at
the Massachusetts General Hospital Division of Pulmonary and Critical Care
Medicine.
Dr. Farhat's research focuses on the development and application of methods for associating genotype and phenotype in infectious disease pathogens, with a strong emphasis on translation to better diagnostics and surveillance in resource-poor settings. Farhat's work has focused on bacterial and viral pathoges and spans the spectrum from computational analysis to field studies. She is PI and Co-Investigator on several large projects funded by NIH including the NIAID and the BD2K initiative.
Maha Farhat holds an MD from the McGill University Faculty of Medicine and a MSc in biostatistics from the Harvard Chan School of Public Health.
Assistant Professor of Biomedical Informatics
Harvard Medical School
Dr. Farhat is an Assistant Professor of of
Biomedical Informatics atHarvard Medical School and a a practicing physician at
the Massachusetts General Hospital Division of Pulmonary and Critical Care
Medicine.
Dr. Farhat's research focuses on the development and application of methods for associating genotype and phenotype in infectious disease pathogens, with a strong emphasis on translation to better diagnostics and surveillance in resource-poor settings. Farhat's work has focused on bacterial and viral pathoges and spans the spectrum from computational analysis to field studies. She is PI and Co-Investigator on several large projects funded by NIH including the NIAID and the BD2K initiative.
Maha Farhat holds an MD from the McGill University Faculty of Medicine and a MSc in biostatistics from the Harvard Chan School of Public Health.
Journal article
CONCLUSIONS: Our analysis provides evidence to support best practices for low-frequency variant calling, including tool choice, masking and filtering. We also develop and provide a new error model that excludes false positive low-frequency variant calls from FreeBayes output.
Journal article
Direct sputum whole genome sequencing (dsWGS) can revolutionize Mycobacterium tuberculosis (Mtb) diagnosis by enabling rapid detection clinically relevant resistance mutations and strain diversity without the biohazard of culture. We searched PubMed, Web of Science, and Google Scholar, identifying 8 studies meeting inclusion criteria for testing protocols for dsWGS. Utilising meta-regression, we identified factors positively associated with dsWGS success, including higher Mtb bacillary load,...