MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes

BACKGROUND: Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity. RESULTS: We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method. CONCLUSION: The search engine, available at , allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation

Прикладна механіка і основи конструювання: навчально-методичний посібник

Розроблено відповідно до навчальної програми і призначено для виконання розрахунково-графічної роботи з дисципліни «Прикладна механіка і основи конструювання» студентам напряму підготовки 6.050202 «Автоматизація та компютерно-ігрегровані технології» денної та заочної форм навчання. Посібник рекомендовано також для самостійної роботи студентів, оскільки він вміщує короткі теоретичні викладки основного матеріалу дисципліни «Прикладна механіка і основи конструювання», умови завдань, приклади їх розв’язування, необхідні довідкові дані

Genome-Wide Analyses for Osteosarcoma in Leonberger Dogs Reveal the CDKN2A/B Gene Locus as a Major Risk Locus

Dogs represent a unique spontaneous cancer model. Osteosarcoma (OSA) is the most common primary bone tumor in dogs (OMIA 001441-9615), and strongly resembles human forms of OSA. Several large- to giant-sized dog breeds, including the Leonberger, have a greatly increased risk of developing OSA. We performed genome-wide association analysis with high-density imputed SNP genotype data from 273 Leonberger cases with a median age of 8.1 [3.1–13.5] years and 365 controls older than eight years. This analysis revealed significant associations at the CDKN2A/B gene locus on canine chromosome 11, mirroring previous findings in other dog breeds, such as the greyhound, that also show an elevated risk for OSA. Heritability (h2SNP) was determined to be 20.6% (SE = 0.08; p-value = 5.7 × 10−4) based on a breed prevalence of 20%. The 2563 SNPs across the genome accounted for nearly all the h2SNP of OSA, with 2183 SNPs of small effect, 316 SNPs of moderate effect, and 64 SNPs of large effect. As with many other cancers it is likely that regulatory, non-coding variants underlie the increased risk for cancer development. Our findings confirm a complex genetic basis of OSA, moderate heritability, and the crucial role of the CDKN2A/B locus leading to strong cancer predisposition in dogs. It will ultimately be interesting to study and compare the known genetic loci associated with canine OSA in human OSA

A comparative genomics multitool for scientific discovery and conservation

A whole-genome alignment of 240 phylogenetically diverse species of eutherian mammal-including 131 previously uncharacterized species-from the Zoonomia Project provides data that support biological discovery, medical research and conservation. The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity.Peer reviewe

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets

Impairment of the Cardiovascular System during SARS-CoV-2 Infection

Although the infection with the severe acute respiratory syndrome (SARS-CoV-2) virus affects primarily the respiratory system, it became evident from the very beginning that the coronavirus disease 2019 (COVID-19) is frequently associated with a large spectrum of cardiovascular involvements such as myocarditis/pericarditis, acute coronary syndrome, arrhythmias, or thromboembolic events, explained by a multitude of pathophysiological mechanisms. Individuals already suffering of significant cardiovascular diseases were more likely to be infected with the virus, had a worse evolution during COVID-19, with further deterioration of their basal condition and increased morbidity and mortality, but significant cardiac dysfunctions were diagnosed even in individuals without a history of heart diseases or being at low risk to develop such a pathology. Cardiovascular complications may occur anytime during the course of COVID-19, persisting even during recovery and, potentially, explaining many of the persisting symptoms included now in terms as subacute or long-COVID-19. It is now well accepted that in COVID-19, the occurrence of cardiovascular impairment represents a significant negative prognostic factor, immensely rising the burden of cardiovascular pathologies

Spontaneous Pneumomediastinum, Pneumothorax, Pneumopericardium and Subcutaneous Emphysema—Not So Uncommon Complications in Patients with COVID-19 Pulmonary Infection—A Series of Cases

(1) Background: Spontaneous pneumomediastinum (PM), pneumothorax (PT), and pneumopericardium (PP) were recently reported as rare complications in patients with severe COVID-19 pneumonia, and our study aims to follow the evolution of these involvements in 11 cases. The presumed pathophysiological mechanism is air leak due to extensive diffuse alveolar damage followed by alveolar rupture. (2) Methods: We followed the occurrence of PM, PN, PP, and subcutaneous emphysema (SE) in 1648 patients hospitalized during the second outbreak of COVID-19 (October 2020–January 2021) in the main hospital of infectious diseases of our county and recorded their demographic data, laboratory investigations and clinical evolution. (3) Results: Eleven patients (0.66%) developed PM, with eight of them having associated PT, one PP, and seven SE, in the absence of mechanical ventilation. Eight patients (72.72%) died and only three (27.27%) survived. All subjects were nonsmokers, without known pulmonary pathology or risk factors for such complications. (4) Conclusions: pneumomediastinum, pneumothorax, and pneumopericardium are not so uncommon complications of SARS-CoV2 pneumonia, being observed mostly in male patients with severe forms and associated with prolonged hospitalization and poor prognosis. In some cases, with mild forms and reduced pulmonary injury, the outcome is favorable, not requiring surgical procedures, mechanical ventilation, or intensive care stay