Our group aims to harness the power of genomics and informatics to enable (1) accurate surveillance, (2) rapid diagnosis and (3) personalized treatment of infections. Maha Farhat, the PI, is a practicing pulmonary and critical care physician at Massachusetts General Hospital and an Assistant Professor of Biomedical Informatics at Harvard Medical School. She trained with Dr. Pardis Sabeti at the Broad Institute, and Dr. Megan Murray at the Harvard Department of Global Health and Social Medicine. The Farhat lab is an interdisciplinary group of evolutionary biologists, clinical scientists, bioinformaticians and computational biologists. 

Our work has a current strong focus on M. tuberculosis and its drug resistance phenotype. We also study non-tuberculosis mycobacteria, gram negative rods, COVID, C.diff and other pathogens. The group has focused on the interpretation of microbial whole genome and transcriptome data, and the development of scalable analysis methods. For example, we developed a phylogenetic method for the detection of signatures of natural selection in M. tuberculosis at a whole genome scale to uncover cellular mechanisms associated with drug resistance in tuberculosis. We have studied in host evolution of pathogens during infection, and is using pathogen genetic signatures to assess response to antibiotic therapy. We have measured delays in TB diagnosis at a national level using health record data from millions of Americans, and identifed the importance of molecular diagnostics in reducing time-to-diagnosis. We partner with field researchers in South Africa and India to enroll and collect samples and data from TB patients, and have led consortium projects to scale up analysis and confirm generalizability at an international level. We are also invested in understanding genome-phenome relationships related to antibiotic resistance using predictive models, machine learning with innovative architectures, evolutionary couplings and analysis of evolutionary dependency. For more details visit our publications page or reach out to us!



    Our TB drug resistance predict function is now released.