ISBaC: An automated pipeline for In-Silico Bacterial Classification

Identifying bacterial isolates using traditional phenotyping and genotyping techniques sometimes gives inaccurate results. The current high-throughput sequencing technologies and development of these sequence-based identification methods make rapid and accurate microbial identification feasible. Mycobacterium isolates are very difficult to identify using conventional tests. This thesis developed an analysis pipeline, ISBaC, that can accurately identify mycobacterial species by chaining various processing and identification steps. The ISBaC pipeline script is user-friendly and can be run with just one command. In test validation cases, ISBaC recognized mycobacterium species accurately. ISBac can also be adapted for the identification of other species.