24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability. The mitochondrion is an organelle floating in the cytoplasm of almost all eukaryotic cells. Its primary function is to generate energy. It contains an independent DNA (mtDNA), which is inherited maternally in many organisms. This DNA is highly susceptible to mutations since it does not possess the robust DNA repair mechanisms proper of the nuclear DNA. Mutations in the mtDNA were associated to several inherited and acquired mitochondrial diseases, including Alzheimer and Parkinson diseases, and cancer. The assessment of the mutation-disease causal link is an onerous task. It requires important laboratory skills/equipment and, often, an animal facility, which are not always available to any laboratory altogether. More and more often, one falls back on software solutions that rely on structural and functional characteristics of proteins to predict the putative harmfulness of a mutation. Many have been implemented and tested on the nuclear proteins, but only a few were finely tuned to the “neglected genome”. Our work not only presents APOGEE, a machine-learning-based predictor that outperforms all existing predictors in reliability and sensitivity, but it makes freely available the APOGEE’s predictions for all the mitochondrial missense mutations in MitImpact.

Water buffalo is a globally important species for agriculture and local economies. A de novo assembled, well-annotated reference sequence for the water buffalo is an important prerequisite for studying the biology of this species, and is necessary to manage genetic diversity and to use modern breeding and genomic selection techniques. However, no such genome assembly has been previously reported. There are 2 species of domestic water buffalo, the river (2n = 50) and the swamp (2n = 48) buffalo. Here we describe a draft quality reference sequence for the river buffalo created from Illumina GA and Roche 454 short read sequences using the MaSuRCA assembler. The assembled sequence is 2.83 Gb, consisting of 366 983 scaffolds with a scaffold N50 of 1.41 Mb and contig N50 of 21 398 bp. Annotation of the genome was supported by transcriptome data from 30 tissues and identified 21 711 predicted protein coding genes. Searches for complete mammalian BUSCO gene groups found 98.6% of curated single copy orthologs present among predicted genes, which suggests a high level of completeness of the genome. The annotated sequence is available from NCBI at accession GCA_000471725.1.

Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in sorting pathogenic from neutral genomic mutations. Considering their computational requirements, simulations are commonly performed on HPC computing devices, which are generally expensive and hard to administer. However, variables like the software tool used for modeling and simulation or the size of the molecule under investigation might make one hardware type or configuration more advantageous than another or even make the commodity hardware definitely suitable for MD studies. This work aims to shed lights on this aspect.

Background: Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. Results: We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. Conclusions: We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

// <![CDATA[ $('.header-date').hide();$('#titleAuthors').hide();$('#abstractHeader').hide(); // ]]> Gianluigi Mazzoccoli1, Stefano Castellana2, Massimo Carella3, Orazio Palumbo3, Cristiana Tiberio1, Caterina Fusilli2, Daniele Capocefalo2, Tommaso Biagini2, Tommaso Mazza2, * and Lorenzo Lo Muzio4, * 1Department of Medical Sciences, Division of Internal Medicine and Chronobiology Unit, IRCCS Scientific Institute and Regional General Hospital “Casa Sollievo della Sofferenza”, S. Giovanni Rotondo (FG), Italy 2Bioinformatics Unit, IRCCS Scientific Institute and Regional General Hospital “Casa Sollievo della Sofferenza”, S. Giovanni Rotondo (FG), Italy 3Medical Genetics, IRCCS Scientific Institute and Regional General Hospital “Casa Sollievo della Sofferenza”, S. Giovanni Rotondo (FG), Italy 4Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy *These authors have contributed equally to this work Correspondence to: Gianluigi Mazzoccoli, email: g.mazzoccoli@operapadrepio.it Keywords: OSCC, gene signature, metastasis, transcriptome, myofibroblast Received: March 07, 2017 Accepted: August 07, 2017 Published: September 05, 2017 ABSTRACT Oral squamous cell carcinoma (OSCC) is the most common oral and pharyngeal cancer, and is responsible of approximately 3% of cancers in men and 2% in women in the Western World, with increasing incidence rates in developing countries. Early detection by screening is necessary to prevent fatal disease because early, curable lesions are rarely symptomatic. The overall 5-yr survival rate is approximately 50% when surgery, radiation, or both are employed as treatment options, but lymph node involvement greatly influences this estimate, by decreasing the survival rate by about 50%. Here, we aimed at finding genetic signatures associated with lymph node metastasis in OSCC patients. We addressed this issue by whole transcriptome analysis through microarray expression profiling of a set of OSSC specimens of patients without lymph node involvement (10 patients, mean age ± SD 61.2±13.8, male 7, female 3) and with lymph node involvement (11 patients, mean age ± SD 62.1±15.1, male 8, female 3). We evidenced a gene expression signature associated to muscle contraction-related genes in specimens obtained from OSCC patients with lymph node involvement. This gene signature suggests the presence of myofibroblasts in tumor stoma of patients with lymph node involvement and emphasizes the decisive role played by myofibroblasts probably through their secretome in determining OSCC invasiveness.