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ISBaC: An automated pipeline for In-Silico Bacterial Classification

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posted on 11.05.2022, 23:37 by HIRA JAVAID
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.


Campus location


Principal supervisor

Wee Wei Yee

Additional supervisor 1

Song Beng Kah

Additional supervisor 2

Md Zobaer Hasan

Year of Award


Department, School or Centre

School of Sciences (Monash University Malaysia)


Master of Science (Research)

Degree Type



Faculty of Science