Development of Bioinformatics Platforms for Methylome and Transcriptome Data Analysis

High-throughput massive parallel sequencing technologies, or Next-Generation Sequencing (NGS) technologies, have greatly accelerated biological and medical research. With the ever-growing throughput and complexity of the NGS technologies, bioinformatics methods and tools are urgently needed for analyzing the large amount of data and discovering the meaningful information behind. In this thesis, I mainly worked on developing bioinformatics algorithms for two research fields: DNA methylation data analysis and large intergenic noncoding RNA discovery, where the NGS technologies are in-depth employed and novel bioinformatics algorithms are highly needed.;DNA methylation is one of the important epigenetic modifications to control the transcriptional regulations of the genes. Whole genome bisulfite sequencing (BS-seq) is one of the most precise methodologies for DNA methylation study which allows us to perform whole methylome research at single-base resolution. To analyze the large amount of data generated by BS-seq experiments, I have co-developed and optimized Methy-Pipe, an integrated bioinformatics pipeline which can perform both sequencing read alignment and methylation state decoding. Furthermore, I've developed a novel algorithm for Differentially Methylated Regions (DMR) mining, which can be used for large scale methylation marker discovery. Methy-Pipe has been routinely used in our laboratory for methylomic studies, including non-invasive prenatal diagnosis and early cancer detections in human plasma.;Large intergenic noncoding RNAs, or lincRNAs, is a very important novel family of gene regulators in many biological processes, such as post-transcriptional regulation, splicing and aging. Due to high tissue-specific expression pattern of the lincRNAs, a large proportion is still undiscovered. The development of Whole Transcriptome Shotgun Sequencing, also known as RNA-seq, combined with de novo or ab initio assembly, promises quantity discovery of novel lincRNAs hence building the complete transcriptome catalog. However, to efficiently and accurately identify the novel lincRNAs from the large transcriptome data still remains a bioinformatics challenge. To fill this gap, I have developed two bioinformatics tools: I) iSeeRNA for distinguishing lincRNAs from mRNAs and II) sebnif for comprehensive filtering towards high quality lincRNA screening which has been used in various biological systems and showed satisfactory performance.;In summary, I have developed several bioinformatics algorithms which help the researchers to take advantage of the strength of the NGS technologies (methylome and transcriptome studies) and explore the biological nature behind the large amount of data.