**1. Introduction**

A transcriptome represents the entire repertoire of RNA content from an organism, a tis‐ sue or a cell and it is dynamic, changing in response to genetic and environmental factors. Several approaches have been developed for transcriptome analysis: hybridization‐based (DNA microarray [1]) or sequence‐based (ESTs—Expressed Sequence Tags [2], SAGE—Serial Analysis of Gene Expression [3], CAGE—Cap Analysis of Gene Expression [4] and MPSS— Massively Parallel Signature Sequencing [5]). The first sequence‐based methods relied on

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© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

Sanger sequencing [6], but with advances in next‐generation sequencing technology (NGS), transcriptomic studies have evolved considerably and RNA‐seq [7, 8] became the state‐of‐art for transcriptome analysis.

RNA‐seq consists of the direct sequencing of transcripts by NGS. Several NGS platforms [9–11] are commercially available nowadays. In general, an RNA set of interest is converted to a library of complementary DNA (cDNA) fragments and sequenced in a high‐throughput manner. Compared to ESTs, RNA‐seq provides better resolution and representativeness, whereas when compared to microarrays, the independence of reference sequences facilitates the discovery of novel genes and isoforms [8].

RNA‐seq experiments harbors challenges from the experimental design to data analysis. Since a complete comprehension of each step is critical to make right decision, this chapter will encompass essential principles required for a successful RNA‐seq experiment, focusing on best recommended practices based on specialized and recent literature. Basic techniques and well‐known algorithms are presented and discussed, guiding both beginners and experi‐ enced users in the implementation of reliable experiments.
