Tumor microbiome is a new trend in human microbiome research, and that has started targeting various cancers such as pancreatic, breast, and colorectal cancer, which suggests that the composition of the microbiome differs depending on the cancer type. However, previous tumor microbiome studies showed the bacterial composition by mapping the metagenomic reads from the cancer tissue to the bacterial genome database, so bacteria that do not exist in the reference could not be detected. The latest shotgun metagenomic technology can construct a bacterial genome called Metagenome Assembled Genome (MAG) by de novo assembly and binning analysis, so this strategy has the potential to apply to the tumor microbiome. Dr. Yuya Kiguchi from the University of Tokyo found that several bacterial species in lung tumor tissue are significantly correlated with cancer progression. However, the number of bacterial-derived reads is very small in the total sequences of the tumor tissue, so they were unable to obtain a sufficient sequence depth to construct a MAG. In addition, the cancer-stage-associated bacteria they discovered are expected as low GC-content bacteria, and they expected that general sequencing methods would result in low sequence depth for these types of bacteria. Learn how Dr. Kiguchi and his team used Twist 96-Plex Library Prep Kit that can be optimized for biased GC content to analyze bacterial genomes in lung tumor tissues.