Most living organisms rely on double-stranded DNA (dsDNA) to store their genetic information and perpetuate themselves. This biological information has been considered as the main target of evolution. However, here we show that symmetries and patterns in the dsDNA sequence can emerge from the physical peculiarities of the dsDNA molecule itself and the maximum entropy principle alone, rather than from biological or environmental evolutionary pressure. The randomness justifies the human codon biases and context-dependent mutation patterns in human populations. Thus, the DNA 'exceptional symmetries,' emerged from the randomness, have to be taken into account when looking for the DNA encoded information. Our results suggest that the double helix energy constraints and, more generally, the physical properties of the dsDNA are the hard drivers of the overall DNA sequence architecture, whereas the selective biological processes act as soft drivers, which only under extraordinary circumstances overtake the overall entropy content of the genome.

The study of such a complex phenomenon as cancer, which depends on several but unexplored and unclear factors, needs new ways to visualize, analyze and combine different data both on species characteristics and genes function. To this respect, we propose a novel platform, named VarCopy, supporting visual Exploratory Data Analysis (EDA) in the context of Copy Number Variation (CNV) data. The platform will be publicly available as a web application soon, and is, to our best knowledge, the first tool allowing visual, interactive exploration and analysis of the CNV landscape of multiple species, allowing the identification of new target genes that might be useful for biomedical research.

One of the most effective visualizations for genomics data is the circular one, supported by popular packages and visualization suites. Many tools are available, however most of them share a number of negative points including limited ease of installation/usage, slow performance and memory limitations (making them unfeasible for very large genomes such as the human one) and non interactivity. In this paper we present the ongoing work on InstaCircos, a web application born from the scientific collaboration between Big Data Analytics and Bioinformatics researchers and aiming at overcoming the available tools' limitations. It provides advanced visualization features through an easy to use web interface and offers interactive functionalities and near real-time performances thanks to an integrated big data management back-end based on MongoDB.

The integration of large-scale genomic and pharmacological data from cancer cell lines promises to be effective in the discovery of new genetic markers of drug sensitivity and of clinically relevant anticancer compounds. The Mutations and Drugs Portal (MDP, http://mdp.unimore.it) is a web accessible database that combines the cell-based NCI60 pharmacological screening with genomic data extracted from the Cancer Cell Line Encyclopedia and the NCI60 DTP projects. MDP currently contains drug sensitivity data for more than 50,800 compounds, describing response to drugs across 115 cancer cell lines. To identify genomic features associated to drug response, cell line drug sensitivity data are integrated with large genomic datasets, including information on somatic mutations and transcriptional data. MDP can be queried for drugs active in cancer cell lines carrying mutations or transcriptional alterations in specific cancer genes and signaling pathways or for genetic and transcriptional profiles associated to sensitivity or resistance to a given compound. Results are presented through graphical representations with links to related data and are fully downloadable. MDP provides a user-friendly web resource to perform in-silico high-throughput screenings of thousand of compounds and facilitate the discovery of associations between genomic portraits and drug responses.

MOTIVATION: Aptamers are synthetic nucleic acid molecules that can bind biological targets in virtue of both their sequence and three-dimensional structure. Aptamers are selected using SELEX, Systematic Evolution of Ligands by EXponential enrichment, a technique that exploits aptamer-target binding affinity. The SELEX procedure, coupled with high-throughput sequencing (HT-SELEX), creates billions of random sequences capable of binding different epitopes on specific targets. Since this technique produces enormous amounts of data, computational analysis represents a critical step to screen and select the most biologically relevant sequences. RESULTS: Here, we present APTANI, a computational tool to identify target-specific aptamers from HT-SELEX data and secondary structure information. APTANI builds on AptaMotif algorithm, originally implemented to analyze SELEX data; extends the applicability of AptaMotif to HT-SELEX data and introduces new functionalities, as the possibility to identify binding motifs, to cluster aptamer families or to compare output results from different HT-SELEX cycles. Tabular and graphical representations facilitate the downstream biological interpretation of results. AVAILABILITY AND IMPLEMENTATION: APTANI is available at http://aptani.unimore.it. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.