RNA sequencing is a powerful technology with many applications, and consequently there are many data analysis paths available. Over the past few decades, RNA-seq has progressed significantly, becoming a predominant approach to the field of transcriptomics. It uses the capabilities of existing high-throughput next generation sequencing methods to provide insight into the current tissue transcriptome. This makes it very popular in studies of interaction and influence of different pharmacological substances, environment parameters or physiological states on tissue or cells. There are many different approaches to RNA-seq, as it is still a developing field of research. A typical path for the RNA-seq data analysis steps is filtering data, mapping, transcript construction and expression quantification. Each of those steps represent a massive array of different possibilities and approaches to data analysis. New implementations are being investigated together with innovations in software and wet lab methods, contributing to a better comprehension of RNA biology, giving answers to questions such as when and where transcription actually occurs to intermolecular interactions that govern the function of RNA. My intention in the following chapters is to plainly demonstrate the background, theory, and practical execution of each step involved in RNA-seq data analysis.
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