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Background Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand. Results RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. Conclusions Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.

Tumorigenesis is a multi-step process in which normal cells transform into malignant tumors following the accumulation of genetic mutations that enable them to evade the growth control checkpoints that would normally suppress their growth or result in apoptosis. It is therefore important to identify those combinations of mutations that collaborate in cancer development and progression. DNA copy number alterations (CNAs) are one of the ways in which cancer genes are deregulated in tumor cells. We hypothesized that synergistic interactions between cancer genes might be identified by looking for regions of co-occurring gain and/or loss. To this end we developed a scoring framework to separate truly co-occurring aberrations from passenger mutations and dominant single signals present in the data. The resulting regions of high co-occurrence can be investigated for between-region functional interactions. Analysis of high-resolution DNA copy number data from a panel of 95 hematological tumor cell lines correctly identified co-occurring recombinations at the T-cell receptor and immunoglobulin loci in T- and B-cell malignancies, respectively, showing that we can recover truly co-occurring genomic alterations. In addition, our analysis revealed networks of co-occurring genomic losses and gains that are enriched for cancer genes. These networks are also highly enriched for functional relationships between genes. We further examine sub-networks of these networks, core networks, which contain many known cancer genes. The core network for co-occurring DNA losses we find seems to be independent of the canonical cancer genes within the network. Our findings suggest that large-scale, low-intensity copy number alterations may be an important feature of cancer development or maintenance by affecting gene dosage of a large interconnected network of functionally related genes.

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.

Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables. StructLMM is a new method to identify genotype–environment interactions (G×E) that involve multiple exposures or environments. When applied to UK Biobank and eQTL data, StructLMM discovers new G×E signals.

The genetic information of people who smoke present distinctive characteristics. In particular, previous research has revealed differences in patterns of DNA methylation, a type of chemical modification that helps cells switch certain genes on or off. However, most of these studies could not establish for sure whether these changes were caused by smoking, predisposed individuals to smoke, or were driven by underlying genetic variation in the DNA sequence itself. To investigate this question, van Dongen et al. examined DNA methylation data from the blood cells of over 700 pairs of identical twins. These individuals share the exact same genetic information, making it possible to better evaluate the impact of lifestyle on DNA modifications. The analyses identified differences in methylation at 13 DNA locations in pairs of twins where one was a current smoker and their sibling had never smoked. Two of the genes code for proteins involved in the response to nicotine, the primary addictive chemical in cigarette smoke. The differences were smaller if one of the twins had stopped smoking, suggesting that quitting can help to reverse some of these changes. These findings confirm that DNA methylation in blood cells is influenced by cigarette smoke, which could help to better understand smoking-associated diseases. They also demonstrate how useful identical twins studies can be to identify methylation changes that are markers of lifestyle.

Tumorigenesis is a multi-step process in which normal cells transform into malignant tumors following the accumulation of genetic mutations that enable them to evade the growth control checkpoints that would normally suppress their growth or result in apoptosis. It is therefore important to identify those combinations of mutations that collaborate in cancer development and progression. DNA copy number alterations (CNAs) are one of the ways in which cancer genes are deregulated in tumor cells. We hypothesized that synergistic interactions between cancer genes might be identified by looking for regions of co-occurring gain and/or loss. To this end we developed a scoring framework to separate truly co-occurring aberrations from passenger mutations and dominant single signals present in the data. The resulting regions of high co-occurrence can be investigated for between-region functional interactions. Analysis of high-resolution DNA copy number data from a panel of 95 hematological tumor cell lines correctly identified co-occurring recombinations at the T-cell receptor and immunoglobulin loci in T- and B-cell malignancies, respectively, showing that we can recover truly co-occurring genomic alterations. In addition, our analysis revealed networks of co-occurring genomic losses and gains that are enriched for cancer genes. These networks are also highly enriched for functional relationships between genes. We further examine sub-networks of these networks, core networks, which contain many known cancer genes. The core network for co-occurring DNA losses we find seems to be independent of the canonical cancer genes within the network. Our findings suggest that large-scale, low-intensity copy number alterations may be an important feature of cancer development or maintenance by affecting gene dosage of a large interconnected network of functionally related genes.

In patients with acute ischemic stroke due to a proximal intracranial arterial occlusion, intraarterial treatment (with retrievable stents in 82% of patients) within 6 hours improved functional outcome at 90 days. Alteplase was given to 89% of patients before randomization. Intravenous alteplase administered within 4.5 hours after symptom onset is the only reperfusion therapy with proven efficacy in patients with acute ischemic stroke. 1 However, well-recognized limitations of this therapy include the narrow therapeutic time window and contraindications such as recent surgery, coagulation abnormalities, and a history of intracranial hemorrhage. 2 Moreover, intravenous alteplase appears to be much less effective at opening proximal occlusions of the major intracranial arteries, which account for more than one third of cases of acute anterior-circulation stroke. 3 , 4 Early recanalization after intravenous alteplase is seen in only about one third of patients with an occlusion of the . . .

The measurement of health-related quality of life (HRQOL) is increasingly important in patients with skin diseases. Despite the availability of a variety of instruments and new psychometric techniques, there is no consensus as to which HRQOL instruments are to be preferred in dermatology. The objective of this review is to evaluate the generic HRQOL measures (i.e., health profiles) that have been used in dermatology (Short-Form-36 (SF-36) and -12, NHP, SIP, World Health Organization Quality of Life (WHOQOL)-100 and -BREF) and all dermatology-specific HRQOL measures (Dermatology Life Questionnaire Index, Skindex-29, -16, and -17, Dermatology Quality of Life Scales, and Dermatology-Specific Quality of Life). Criteria for evaluation were adapted from existing guidelines and included conceptual and measurement model, reliability, validity, responsiveness, item functioning, meaning of scores, administrative burden, respondent burden, the availability of alternative forms, and of cultural and language adaptations. Furthermore, an overview of skin diseases in which the included HRQOL tools have been used is presented. Although the selection of the appropriate HRQOL instrument remains a trade-off between various psychometric properties and research objectives, for now, we recommend the combination of SF-36 and Skindex-29 as the instruments of choice in dermatology. Promising new instruments for future research are the WHOQOL and the Skindex-17.

Exploratory data analysis workflows often use clustering algorithms to find groups of similar data points. The shape of these clusters can provide meaningful information about the data. For example, a Y-shaped cluster might represent an evolving process with two distinct outcomes. This article presents flare-sensitive clustering (FLASC), an algorithm that detects branches within clusters to identify such shape-based subgroups. FLASC builds upon HDBSCAN*—a state-of-the-art density-based clustering algorithm—and detects branches in a post-processing step using within-cluster connectivity. Two algorithm variants are presented, which trade computational cost for noise robustness. We show that both variants scale similarly to HDBSCAN* regarding computational cost and provide similar outputs across repeated runs. In addition, we demonstrate the benefit of branch detection on two real-world data sets. Our implementation is included in the hdbscan Python package and available as a standalone package at https://github.com/vda-lab/pyflasc .

Cerebellar ataxia (CA) and hereditary spastic paraplegia (HSP) are two of the most prevalent motor disorders with extensive locus and allelic heterogeneity. We implemented clinical exome sequencing, followed by filtering data for a 'movement disorders' gene panel, as a generic test to increase variant detection in 76 patients with these disorders. Segregation analysis or phenotypic re-evaluation was utilized to substantiate findings. Disease-causing variants were identified in 9 of 28 CA patients, and 8 of 48 HSP patients. In addition, possibly disease-causing variants were identified in 1 and 8 of the remaining CA and HSP patients, respectively. In 10 patients with CA, the total disease-causing or possibly disease-causing variants were detected in 8 different genes, whereas 16 HSP patients had such variants in 12 different genes. In the majority of cases, the identified variants were compatible with the patient phenotype. Interestingly, in some patients variants were identified in genes hitherto related to other movement disorders, such as TH variants in two siblings with HSP. In addition, rare disorders were uncovered, for example, a second case of HSP caused by a VCP variant. For some patients, exome sequencing results had implications for treatment, exemplified by the favorable L-DOPA treatment in a patient with HSP due to ATP13A2 variants (Parkinson type 9). Thus, clinical exome sequencing in this cohort of CA and HSP patients suggests broadening of disease spectra, revealed novel gene-disease associations, and uncovered unanticipated rare disorders. In addition, clinical exome sequencing results have shown their value in guiding practical patient management.