Student Level: Honours or Masters
This study will apply next generation sequencing technologies (NGS) for the identification of fungal pathogens through DNA barcodes to assess the feasibility of the technology in a clinical settings. The project will involve extensive laboratory and bioinformatics applications.
Correct and fast identification of the causative agents of mycoses is of great importance to enable early diagnosis and targeted antifungal therapy. DNA barcoding offers an accurate, fast, cost-effective, culture independent approach for species identification through DNA barcodes (short standardized sequences). The current official fungal DNA barcode is the internal transcribed spacer (ITS) region. So far, mainly Sanger technology has been used to sequence the ITS region for species identification. The main drawback of Sanger sequencing that is requires prior culturing of the fungal pathogens significantly extending the turnaround time of the identification in clinical settings. Over the past decade, enormous progress has been made in the field of next generation sequencing (NGS). The main advantage of NGS technologies is that it allows the sequencing of millions of DNA fragments, from thousands of DNA templates simultaneously saving time and money. This new approach can boost up significantly the generation of barcodes in a short time and at a reduced per-base cost due the high throughput. Other advantage of NGS is the ability to conduct in depth sequencing at a deeper level recovering more species from any environmental (complex) sample than any other techniques. NGS is currently revolutionizing the microorganism diversity providing a more realistic assessment of genetic and taxonomic diversity present in microbial communities. However, the bottleneck of the NGS beside several technical artefacts such as primer choice, DNA extraction methods, PCR biases or the length of the generated sequences is the management and interpretation of the vast amount of data generated. In this project we will use NGS technology for the identification of fungal pathogens and estimate its accuracy and feasibility in clinical settings.
Methods: Various culture based methods for species identificatio, Sanger sequencing, Next Generation Sequencing, extensive data analyses using various bioinformatics softwares and algorithms, databasing.
Supervisor: Professor Wieland Meyer - firstname.lastname@example.org