Contaminant Detection and Control for More Accurate Pathogen Analysis in Low Microbial Biomass Samples

1. Objectives:While next-generation sequencing has become a promising means for pathogen detection, contamination in this process can lead to false positives, particularly for low microbial biomass samples. Contamination cannot be completely removed despite stringent lab operation/hygiene regulations. Our objectives are to develop a pipeline for automatic contaminant detection and removal. 2. Hypothesis to be tested:The final sequenced sample is a mix of the true microbes and different contaminant sources. Thus, we can use non-negative matrix factorization to estimate the contribution of each possible contaminant source to the final sequenced sample. 3. Design and subjects:Our study is an inter-disciplinary project with both wet-lab experiments and software development:  collecting and sequencing clinical samples and lab monitoring samples, tool development, and validation of the pathogen after contamination removal. Specifically, about 1000 low microbial biomass samples will be obtained by the Co-PAs for testing contamination detection and removal.4. Study instruments:Our research uses both “dry” and “wet” instruments, containing clinical sample sequencing and quantitative analysis. 5. Interventions:Our software can remove the contaminants from the sequencing data and thus lead to more accurate pathogen detection, enabling precise diagnosis.6. Main outcome measures:The accuracy, recall, and precision of the contaminant detection software. 7. Data analysis:We will develop our algorithm and apply our software to remove contaminants from the sequenced data and then present the results on pathogen detection.  8. Expected results:A guideline and a software that can be applied for contaminant removal.