In recent decades, scientific research has marked an important change in the
conceptualization of studies. The development of new analytical technologies, capable
of generating large amounts of data, led to the transition from the reductionist scientific
model to the holistic one. Among these “high-throughput” technologies, nextgeneration
sequencing (NGS) has exponentially increased the amount of knowledge
about complex living systems. Bioinformatics and biostatistics are two disciplines
developed together with the NGS platforms in order to allow more accurate analysis
and data management. NGS technology can be equally applied to both emerging DNA
and RNA, originally, for the detection of variants and the analysis of gene expression,
respectively. However, in recent years, the possibility of calling variants from the
RNA-seq analysis has become increasingly concrete. Here we discuss the different
analytical conceptualizations that distinguish DNA from the analysis of RNA
sequencing data, highlighting the informative potential of RNA-seq data, not only in
relation to the quantification of gene expression. Therefore, the application of the
variant calling pipeline analysis to transcriptome data is discussed. Furthermore, the
possibility of identifying single nucleotide variants starting from RNA samples, allows
characterizing two important mechanisms of regulation of gene expressions such as
RNA editing and genomic imprinting. The study of these two biological mechanisms is
probably the most stimulating resource obtained from RNA-seq and clearly requires
highly adequate bioinformatics support, which is now being developed.
Keywords: Alignment, ABRA2, CaSpER, Diploid-SQUID, eSNV-Detect, Editome,
Epigenetics, GATK, Imprinting, JACUSA, RNA sequencing, Transcriptome
analysis, SNP discovery, RNA editing, SAMtools, RNAIndel, RVboost, SNPiR,
VaDiR, Variant calling.
