Detecting Bacterial Species from Next Generation Sequencing Data Derived from Ancient Human Skeletal Samples
Disciplines
Bioinformatics | Biological and Physical Anthropology
Abstract (300 words maximum)
This study aims to isolate and identify bacteria and single nucleotide polymorphisms (SNPs) found alongside Mycobacterium tuberculosis complex (MTCB) in silico. MTCB is a causative agent of tuberculosis (TB). Our secondary objective is to examine variations of TB, study its paleoepidemiology, and apply this information to present-day public health issues. This research utilized data from the Sequence Read Archive (SRA), number PRJNA422903. This dataset is comprised of DNA obtained from the remains of 28 individuals belonging to Neolithic-period populations. This DNA is being studied in silico through next generation sequencing (NGS), utilizing the following bioinformatics software tools with customized setting: Trim Galore! and Kraken2. We plan to implement a bioinformatics pipeline to pass further fastQ files through trimming with deprivation of the adapter sequence, and Kraken2 will act as a filter allowing us to find unknown pieces of DNA sequences. Using NGS, we will be able to isolate and identify bacteria found alongside MTCB in silico to study the paleoepidemiology of TB prior to the usage of antibiotics. Due to advancements in technology, we will be able to identify more bacterial species and SNPs alongside MTBC than previous scholars in the field of paleoepidemiology. In conclusion, this approach broadens the potential scope of paleoepidemiology both to older, sub optimally preserved samples and to pathogens with difficult intrageneric taxonomy. These approaches could also be utilized in future disease diagnosis and control.
Academic department under which the project should be listed
RCHSS - Geography & Anthropology
Primary Investigator (PI) Name
Tsai-Tien Tseng
Detecting Bacterial Species from Next Generation Sequencing Data Derived from Ancient Human Skeletal Samples
This study aims to isolate and identify bacteria and single nucleotide polymorphisms (SNPs) found alongside Mycobacterium tuberculosis complex (MTCB) in silico. MTCB is a causative agent of tuberculosis (TB). Our secondary objective is to examine variations of TB, study its paleoepidemiology, and apply this information to present-day public health issues. This research utilized data from the Sequence Read Archive (SRA), number PRJNA422903. This dataset is comprised of DNA obtained from the remains of 28 individuals belonging to Neolithic-period populations. This DNA is being studied in silico through next generation sequencing (NGS), utilizing the following bioinformatics software tools with customized setting: Trim Galore! and Kraken2. We plan to implement a bioinformatics pipeline to pass further fastQ files through trimming with deprivation of the adapter sequence, and Kraken2 will act as a filter allowing us to find unknown pieces of DNA sequences. Using NGS, we will be able to isolate and identify bacteria found alongside MTCB in silico to study the paleoepidemiology of TB prior to the usage of antibiotics. Due to advancements in technology, we will be able to identify more bacterial species and SNPs alongside MTBC than previous scholars in the field of paleoepidemiology. In conclusion, this approach broadens the potential scope of paleoepidemiology both to older, sub optimally preserved samples and to pathogens with difficult intrageneric taxonomy. These approaches could also be utilized in future disease diagnosis and control.