Foreword

The ability to analyze the genomes and transcriptomes using NGS techniques was a potential breakthrough in research. These sequencing techniques allow the discovery of unexpected transcripts, high speed, scalability and recently have become highly accessible thanks to a drastic drop in costs. This last condition has enabled the use of sequencing as a clinical tool. However, brute force does not automatically lead to an advancement in knowledge, in fact, the biggest challenge related to the sequencing is processing this huge amount of raw data to assess the differential gene expression, RNA editing, genomic imprinting, new splicing variants, and gene fusions. In this regard, much of the research in Bioinformatics and Biostatistics is developing algorithms and publishing software for filtering, analyzing, and visualizing sequencing data. A few researchers have the necessary skills to create software or understand the algorithms implemented by a tool, so most of them are limited only to the use of the software. These researchers may find themselves disoriented in front of a vastness of software that promises to face the same problem but offers different results. In parallel, technological improvements are providing increasingly long and accurate sequencing allowing direct reading of full-length transcripts and single-cell RNA sequencing. The latter is highly applicable for studying tumor heterogeneity, tracking metastases and deciphering the message carried by even a single extracellular vesicle. In the future, more intelligent programs are expected, that is, capable of comparing sequencing information with all data available in databases and directly providing biologically significant information and translating these findings into clinically actionable results. These will be programs that will speak more and more in biological but also in medical terms. For clinical use, response times are very important. In the case of aggressive diseases, the entire pipeline duration should not exceed a couple of weeks and in the case of aggressive infections, we speak about a few days. All this together will allow for a revolution in science for precision medicine based on personal genome. This book shows various methods for analyzing genomics and transcriptomics data, always keeping in mind the objective of providing really useful information to medicine. Among the different omics applications, it shows the analysis of mitochondrial DNA for the diagnosis of mitochondrial diseases and the improvement of genetic counseling, the prioritization of genes, and the discovery of gene variants. The reader is guided through the use and performance analysis of various programs, data visualization tools are shown and the results of multiple programs are compared for an integrated approach. Through the chapters, we are accompanied in a didactic way, thus bringing non-experts closer to a field usually dominated by bioinformaticians. In the end, the reader understands that sequencing, even if carried out some time before, does not age so quickly, because as new algorithms are produced, we can look at those same data from a new perspective, obtaining new results which provide us with satisfaction of unexpected discovery.