Cancer is one of the leading causes of morbidity and mortality in the globe. Microbiological infections account for up to 20% of the total global cancer burden. The human microbiota within each organ system is distinct, and their compositional variation and interactions with the human host have been known to attribute detrimental and beneficial effects on tumor progression. With the advent of next generation sequencing (NGS) technologies, data generated from NGS is being used for pathogen detection in cancer. Numerous bioinformatics computational frameworks have been developed to study viral information from host-sequencing data and can be adapted to bacterial studies. This review highlights existing popular computational frameworks that utilize NGS data as input to decipher microbial composition, which output can predict functional compositional differences with clinically relevant applicability in the development of treatment and prevention strategies.
Keywords: Cancer microbiome; Computational frameworks; NGS.