With the development of next-generation sequencing (NGS), different NGS technologies have been developed and launched in the last few years, and NGS based applications such as metagenomics and RNAseq have received considerable attention.
In this Ph.D. project, we firstly compared the performances of the Illumina and DNBseq platforms using the most commonly use reference sample, the Genome-In-A-Bottle (GIAB) sample (NA12878). Our findings indicate a comparable single-nucleotide polymorphism (SNP) calling accuracy for DNBseq data compared to Illumina data as well as for copy number variant (CNV) detection. However, for Insertions and deletions (InDels), we found lower accuracy for Illumina than for DNBseq data. In addition, our study also showed that DNBseq can be a more cost-effective platform for WGS compared to the Illumina platform.
We conducted a comparative analysis of metagenomics applications as there are many possible factors that may affect the studies of human microbiome, including the specimen status after preservation, extracted DNA quality, library preparation protocol, and sample DNA input. Through our study, a combined protocol is recommended for performing metagenomics studies, by using Mag-Bind® Universal Metagenomics Kit (OM) method plus KAPA Hyper Prep Kit (KH) protocol as well as suitable DNA quantity on either fresh or freeze-thaw samples. Our findings provide clues for potential variations from various DNA extraction methods, library protocols, and sample DNA inputs, which are critical for consistent and comprehensive profiling of the human gut microbiome.
Finally, to identify suitable methods and provide a benchmark for formalin-fixed paraffin-embedded (FFPE) RNAseq, we investigated three major library construction methods, including Duplex-Specific Nuclease (DSN), Truseq Ribo-Zero (Ribo-Zero), and Truseq RNA Access (RNA Access). Based on our results we recommend that RNA Access should be used for the analysis of mRNA expression, noting that also non-coding RNA can be detected by this method. By contrast, our analyses indicated that the DSN protocol would be the preferred choice for analysis of ncRNA, and furthermore, our results also provided evidence that DSN would be preferable especially for SNV calling using FFPE samples.