Mycobacterium Tuberculosis (MTB) causes a major problem in public health. With the high prevalence of MTB in Thailand, the world health organization (WHO) assigned Thailand to the high tuberculosis (TB) burden group of 14 countries whose burdens encompass TB infection, infection with both TB and HIV (TB/HIV) and infection with multidrug-resistant TB (MDR-TB). More and more cases of drug resistant TB hampers the success of TB control program due to higher treatment failure rate. Quite often that new TB infected patients will be prescribed with multiple drugs because the standard TB drug sensitivity test usually takes up to two months. Such practices could promote the higher incidence of TB drug resistance. Thus, WHO recommends the use of TB whole genome sequencing (WGS) in the standard TB control program comprising diagnosis, prediction of TB drug resistance, and TB spreading management. WGS is faster and cheaper; soon TB WGS is hence becoming a standard practice in public health. However, WGS of TB entails generation of large sequencing data files. Such data require rather complex bioinformatic operations that could confuse TB interpretation personnel. With this challenge, we develop this platform to solicit support for development of a computational platform assisting TB drug resistant prediction with high accuracy using both TB’s single nucleotide polymorphisms (SNPs) and structural variations (SV) from TB WGS data. The resulting computational prediction of drug-resistant TBs should promote the construction of an up-to-date national TB genomic portal that offer a prototype of TB drug program of TB in Thailand.