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AN IMPLEMENTATION OF BIOINFORMATICS TOOLS FOR RNA-SEQ DATA ANALYSIS
Abstract
RNA-Seq is a new approach to transcriptome profiling that makes advantage of deep-sequencing technology. The availability of RNA-seq data prompted computational biologists to create algorithms to analyze the data statistically and yield biologically useful conclusions. By clustering viral sequences, we can characterize the content and structure of intrahost and interhost viral populations, which are important in disease progression and epidemic dissemination. We propose and test a new entropy-based technique for grouping aligned viral sequences as categorical data in this study. The approach determines homogenous clustering by minimizing information entropy rather than the distance between sequences within the same cluster. Furthermore, in this paper, we provide a novel pathway analysis method based on the Expectation-Maximization (EM) algorithm.
Using meta-transcriptomic data, researchers investigated enzyme expression and pathway activity. We will also describe our attempts to develop unique gene signatures to better understand the role of sensory nerve interference in the anti-melanoma immune response, as well as our research into the racial disparities in Triple-negative breast cancer. Finally, we show our method for detecting retained introns in RNA-seq data, which will be used to construct a vaccination against cancers with p53 mutations. In conclusion, this study presents fresh ways to analyze RNA-seq data and their application to real-world biological research.
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