Genetic epidemiology algorithms for tracking drug resistance variants and genomic clustering of plasmodium species

Bioinformatics;Variant calling;Consensus variant calling;Genomic clustering;Alignment free algorithms;k-mer based;Molecular surveillance;Malaria;Anti-malarial drug resistance

Ravishankar, Shashidhar ; Vannberg, Fredrik O. Biology Jordan, I. King Udhayakumar, Venkatachalam Voit, Eberhard McDonald, John ; Vannberg, Fredrik O.

University:Georgia Institute of Technology

Department:Biology

关键词: Bioinformatics;    Variant calling;    Consensus variant calling;    Genomic clustering;    Alignment free algorithms;    k-mer based;    Molecular surveillance;    Malaria;    Anti-malarial drug resistance;   

Others  :  https://smartech.gatech.edu/bitstream/1853/62281/1/RAVISHANKAR-DISSERTATION-2019.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The goal of this thesis is to develop algorithms for the analysis of P. falciparum, P. brasilianum, and P. malariae. Malaria is endemic in many parts of the world, including regions of central Africa, South America, and South East Asia. There are five known species that cause malaria in humans: P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi. P. knowlesi is a zoonotic parasite restricted to mostly South East Asia.According to a World Health Organization (WHO) report from 2018, these five species were responsible for nearly 219 million infections, resulting in an estimated 435,000 deaths related to malaria in 2017. In this work, we highlight algorithms that can identify the similarity between Plasmodium species and detect drug-resistant P. falciparum parasites. The two specific aims in this work, describe two novel algorithms for genomic clustering and molecular surveillance from Next-generation Sequencing (NGS) data. First, we describe a consensus-based variant identification framework molecular surveillance of drug resistance in infectious disease. We highlight its utility by identifying mutations associated with drug resistance in malaria isolates. The scalability of the framework is highlighted by analyzing 8351 M. tuberculosis isolates for the genotypic prediction of drug resistance. In the second aim, we describe a k-mer based alignment-free algorithm for the estimation of similarity between isolates from raw NGS data. Using a weighted Jaccard distance, we describe an exact method for estimation of the distance between isolates from k-mer count data. The memory efficiency, scalability, and accuracy of the algorithm was demonstrated using in-silico datasets generated from genomes of 12 Plasmodium species, as well as real-world isolates from an outbreak of C. auris in Colombia. The improved accuracy and scalability offered by the methods described in this work can facilitate the use of NGS in public health.