The Source of the Data Flood: Sequencing Technologies

Where does this huge amount of data come from? What are the costs of producing it? The answers to these questions lie in the impressive development of sequencing technologies, which have opened up many research opportunities and challenges, some of which are described in this issue. DNA sequencing is the process of “reading” a DNA fragment (referred to as a “read”) and determining the exact order of DNA bases (the four possible nucleotides, that are Adenine, Guanine, Cytosine, and Thymine) that compose a given DNA strand. Research in biology and medicine has been revolutionised and accelerated by the advances of DNA and even RNA sequencing biotechnologies

Editorial: Repetitive Structures in Biological Sequences: Algorithms and Applications

Repetitive structures in biological sequences are emerging as an active focus of research and the unifying concept of ?repeatome? (the ensemble of knowledge associated with repeating structures in genomic/proteomic data) has been recently proposed in order to highlight several converging trends

Detecting common copy number variants in high-throughput sequencing data by using JointSLM algorithm

The discovery of genomic structural variants (SVs), such as copy number variants (CNVs), is essential to understand genetic variation of human populations and complex diseases. Over recent years, the advent of new high-throughput sequencing (HTS) platforms has opened many opportunities for SVs discovery, and a very promising approach consists in measuring the depth of coverage (DOC) of reads aligned to the human reference genome. At present, few computational methods have been developed for the analysis of DOC data and all of these methods allow to analyse only one sample at time. For these reasons, we developed a novel algorithm (JointSLM) that allows to detect common CNVs among individuals by analysing DOC data from multiple samples simultaneously. We test JointSLM performance on synthetic and real data and we show its unprecedented resolution that enables the detection of recurrent CNV regions as small as 500 bp in size. When we apply JointSLM to analyse chromosome one of eight genomes with different ancestry, we identify 3000 regions with recurrent CNVs of different frequency and size: hierarchical clustering on these regions segregates the eight individuals in two groups that reflect their ancestry, demonstrating the potential utility of JointSLM for population genetics studies

Galectin-3. One molecule for an alphabet of diseases, from A to Z

Galectin-3 (Gal-3) regulates basic cellular functions such as cell–cell and cell–matrix interactions, growth, proliferation, differentiation, and inflammation. It is not surprising, therefore, that this protein is involved in the pathogenesis of many relevant human diseases, including cancer, fibrosis, chronic inflammation and scarring affecting many different tissues. The papers published in the literature have progressively increased in number during the last decades, testifying the great interest given to this protein by numerous researchers involved in many different clinical contexts. Considering the crucial role exerted by Gal-3 in many different clinical conditions, Gal-3 is emerging as a new diagnostic, prognostic biomarker and as a new promising therapeutic target. The current review aims to extensively examine the studies published so far on the role of Gal-3 in all the clinical conditions and diseases, listed in alphabetical order, where it was analyzed

Enhanced copy number variants detection from whole-exome sequencing data using EXCAVATOR2

Copy Number Variants (CNVs) are structural rear- rangements contributing to phenotypic variation that have been proved to be associated with many dis- ease states. Over the last years, the identification of CNVs from whole-exome sequencing (WES) data has become a common practice for research and clinical purpose and, consequently, the demand for more and more efficient and accurate methods has increased. In this paper, we demonstrate that more than 30% of WES data map outside the targeted re- gions and that these reads, usually discarded, can be exploited to enhance the identification of CNVs from WES experiments. Here, we present EXCAVATOR2, the first read count based tool that exploits all the reads produced by WES experiments to detect CNVs with a genome-wide resolution. To evaluate the per- formance of our novel tool we use it for analysing two WES data sets, a population data set sequenced by the 1000 Genomes Project and a tumor data set made of bladder cancer samples. The results obtained from these analyses demonstrate that EXCAVATOR2 out- performs other four state-of-the-art methods and that our combined approach enlarge the spectrum of detectable CNVs from WES data with an unprece- dented resolution