**Author details**

Regarding the eQTL‐Gene position, when they are located close to the genes, they influence they are called local eQTLs. Local eQTLs can affect a gene in two ways: in *cis* (cis‐eQTL) when the variant affects only the gene that is located on the same chromosome and not affecting the copy of the homologous chromosome, thus causing an allelic imbalance; and in *trans* (trans‐eQTL) when the eQTLs do not affect the target expression directly, but instead affect an intermediate factor that will ultimately affect its target expression. Since the inter‐ mediate factor acts equally for both alleles, it does not cause allelic imbalance. On the other hand, eQTLs located further away from their target genes are referred as distant eQTLs, usually act in *trans* and are harder to find [192]. Several eQTL‐mapping studies published in the past few years showed that many variants often affect gene expression levels of nearby and distant genes [193–197] highlighting the importance of integrating transcriptomic and

22 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

Despite the mapping process for eQTL analysis being conceptually simple, since this anal‐ ysis is dealing with allelic specific expression, some caution is required during its counting estimation. For the aligning process, general purpose aligners or variant aware aligners such as GSNAP [164] can be used. After the alignment, some steps are recommended for retrieving allelic‐specific counts, such as removing duplicate reads that may arise from PCR artifacts. However, it is important that the choice for discarding a duplicate read is not done by mapping score as this might bias toward the reference allele [198]. Also, mapping bias should be controlled by filtering sites with likely bias [199]. Some tools like ASEReadCounter from GATK for allelic‐specific expression implement these filters by

The GTEx portal is a valuable resource to study human gene expression and regulation related to genetic variation. It hosts data from several eQTL studies and much information on

In the past few years, recent advances in sequencing technologies allowed the cost‐efficient generation of an unprecedented amount of biological information. Similarly, RNA‐seq tech‐ niques are under continuous improvements allowing wide range applications and develop‐ ment of high level resolution experiments such as those based on the emergent single‐cell RNA sequencing (scRNA‐seq) field. To couple with this ever increasing data, several tools and pipelines have been constantly developed. The bioinformatics field changes in an astonishing pace, in a way that it is almost impossible to keep up with all the new tenden‐ cies, the overwhelming amount of available software and the controversial opinions in the scientific community. For some aspects, it is difficult to find a consensus on the best pipeline to be applied. This chapter goal was to guide RNA‐seq users through its complex steps, providing a brief overview of the complete workflow, highlighting accessible protocols and currently available tools, most of which correlated with supporting benchmark studies.

genomic data.

default [200].

**5. Concluding remarks**

laboratory and analysis methods for eQTL [201].

Michele Araújo Pereira<sup>1</sup> \*, Eddie Luidy Imada2 and Rafael Lucas Muniz Guedes1

\*Address all correspondence to: michele.pereira@hermespardini.com.br

1 Hermes Pardini Group, Vespasiano, Brazil

2 Federal University of Minas Gerais, Belo Horizonte, Brazil
