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Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes.

Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. KI696 in vitro We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http//github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker.

We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

The catheter-related bladder discomfort (CRBD) of male patients is a common clinical problem, albeit lacking effective solutions. The present study aimed to investigate whether intravesical dexmedetomidine instillation alleviates the postoperative urinary discomfort in male patients with catheter under general anesthesia.

This single-blinded, prospective, randomized study included a total of 167 male patients American Society of Anesthesiologists (ASA) physical status I-II scheduled for surgery under general anesthesia were allocated to two groups 84 in the dexmedetomidine group and 83 in the control group. Dexmedetomidine group patients received intravesical instillation of the drug 0.5 μg/kg and normal saline 20 mL, while the control group received intravesical instillation of 20 mL normal saline. The catheter was clamped for 30 min after intravesical instillation for all patients. CRBD scores and urethra pain numerical rating scale (NRS) scores were measured at admittance to post-anesthesia care unit (1800016429 ), date of registration 1st June 2018.

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.

We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.

NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

N-acetyltransferase 2 plays a crucial role in the metabolism of a wide range of xenobiotics, including many drugs, carcinogens, and other chemicals in the human environment. The article presents for the first time data on the frequency of two important "slow" variants of NAT2 gene (NAT2*5, rs1801280 and NAT2*7, rs1799931), which significantly affect the rate of xenobiotics acetylation, among representatives of indigenous populations of Forest and Tundra Nenets in Northern Siberia. The aim of this study was to identify the frequencies of these variants and compare them with frequencies in other ethnic populations.

NAT2*5 (T341C) genotyping revealed frequencies of 28,0% and 38,6% for Tundra and Forest Nenets, respectively. The frequencies of NAT2*7 (G857A) variant were 9,8% and 8,2% for Tundra and Forest Nenets, respectively. Polymorphic variants frequencies for Nenets are intermediate between those in populations of Europeans and Asians. These results can probably be explained by the presence of both European and Asian components in Nenets gene pools.

The results of this study expand the knowledge of NAT2 polymorphism in world populations. These data may also help assess the genetic predisposition of Nenets to multifactorial diseases associated with polymorphism in the NAT2 gene and, in general, contribute to the development of personalized medicine in reference to native people of Siberia.

The results of this study expand the knowledge of NAT2 polymorphism in world populations. These data may also help assess the genetic predisposition of Nenets to multifactorial diseases associated with polymorphism in the NAT2 gene and, in general, contribute to the development of personalized medicine in reference to native people of Siberia.