The main theme of this dissertation is to develop a distributed computational pipeline for processing next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate hundreds of millions of short reads for each DNA/RNA sample. There are many existing bioinformatics tools developed for the analysis and visualization of this data, but very large studies present computational and organizational challenges that are difficult to overcome manually. We designed a comprehensive pipeline for the analysis of RNA sequencing which leverages many existing tools and parallel computing technology to facilitate the analysis of extremely large studies. RNA CoMPASS provides a web-based graphical user interface and distributed computational pipeline including endogenous transcriptome quantification and additionally the investigation of exogenous sequences.

The main theme of this thesis research is concerned with developing a computational pipeline for processing Next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate tens of millions of short reads for each DNA/RNA sample. The alignment of a large volume of short reads to a reference genome is a key step in NGS data analysis. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing useful information. In order to assist biomedical researchers to conveniently access essential information from NGS data files in SAM/BAM format, we have developed a Graphical User Interface (GUI) software tool named SAMMate to pipeline human transcriptome quantification. SAMMate allows researchers to easily process NGS data files in SAM/BAM format and is compatible with both single-end and paired-end sequencing technologies. It also allows researchers to accurately calculate gene expression abundance scores.

Abiotic stress is a major constraint for crop productivity worldwide. To better understand the common biological mechanisms of abiotic stress responses in plants, we performed meta-analysis of 652 samples of RNA sequencing (RNA-Seq) data from 43 published abiotic stress experiments in Arabidopsis thaliana. These samples were categorized into eight different abiotic stresses including drought, heat, cold, salt, light and wounding. We developed a multi-step computational pipeline, which performs data downloading, preprocessing, read mapping, read counting and differential expression analyses for RNA-Seq data. We found that 5729 and 5062 genes are induced or repressed by only one type of abiotic stresses. There are only 18 and 12 genes that are induced or repressed by all stresses. The commonly induced genes are related to gene expression regulation by stress hormone abscisic acid. The commonly repressed genes are related to reduced growth and chloroplast activities. We compared stress responsive genes between any two types of stresses and found that heat and cold regulate similar set of genes. We also found that high light affects different set of genes than blue light and red light. Interestingly, ABA regulated genes are different from those regulated by other stresses. Finally, we found that membrane related genes are repressed by ABA, heat, cold and wounding but are up regulated by blue light and red light. The results from this work will be used to further characterize the gene regulatory networks underlying stress responsive genes in plants. / Master of Science / Abiotic stress is a major constraint for crop productivity worldwide. To better understand the common biological mechanisms of abiotic stress responses in plants, we performed analysis of 652 samples of RNA sequencing data from 43 published abiotic stress experiments in Arabidopsis thaliana. These samples were collected from eight different abiotic stresses including drought, heat, cold, salt, light and wounding. We identified genes that were induced or repressed by each of these stresses. We found that 5729 and 5062 genes are induced or repressed by only one type of abiotic stresses. There are only 18 and 12 genes that are induced or repressed by all stresses. The commonly induced genes are related to gene expression regulation by stress hormone. The commonly repressed genes are related to reduced growth. We compared stress responsive genes between any two types of stresses and found that heat and cold regulate similar set of genes. We also found that high light affects different set of genes than blue light and red light. Finally, we found that membrane related genes are repressed by stress hormone, heat, cold and wounding but are up regulated by blue light and red light. The results from this work will be used to further characterize the gene regulations underlying stress responsive genes in plants.