On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Dental pulp exposure, periapical inflammation and suppurative osteomyelitis of the jaws in juvenile Baltic grey seals (<i>Halichoerus grypus grypus</i>) from the late 19th century

<div><p>The systematic analysis of museum collections can provide important insights into the dental and skeletal pathology of wild mammals. Here we present a previously unreported type of dental defect and related skull pathology in five juvenile Baltic grey seals that had been collected in the course of a seal culling program along the Danish coast in 1889 and 1890. All five skulls exhibited openings into the pulp cavities at the crown tips of all (four animals) or two (one animal) canines as well as several incisors and (in one animal) also some anterior premolars. The affected teeth showed wide pulp cavities and thin dentin. Pulp exposure had caused infection, inflammation, and finally necrosis of the pulp. As was evidenced by the extensive radiolucency around the roots of the affected teeth, the inflammation had extended from the pulp into the periapical space, leading to apical periodontitis with extensive bone resorption. Further spreading of the inflammation into the surrounding bone regions had then caused suppurative osteomyelitis of the jaws. The postcanine teeth of the pathological individuals typically had dentin of normal thickness and, except for one specimen, did not exhibit pulp exposure. The condition may have been caused by a late onset of secondary and tertiary dentin formation that led to pulp exposure in anterior teeth exposed to intense wear. Future investigations could address a possible genetic causation of the condition in the studied grey seals.</p></div

Relevance of different prior knowledge sources for inferring gene interaction networks

When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F-scores. Furthermore, their integration with genomic data leads to a significant increase in F-scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference

Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Results: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. Conclusions: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.Peer ReviewedPostprint (published version

An investigation on the presence of Chlamydiaceae in Swedish dogs

<p>Abstract</p> <p>Background</p> <p>Bacteria belonging to the family <it>Chlamydiaceae </it>cause a broad spectrum of diseases in a wide range of hosts, including man, other mammals, and birds. Upper respiratory and genital diseases are common clinical problems caused by <it>Chlamydiaceae</it>. Very little is known about chlamydial infections in dogs. Few clinical reports on natural disease in dogs describe mainly conjunctival and upper respiratory signs, and the role of <it>Chlamydiaceae </it>in genital disease is unclear. The present study aimed at studying the prevalence of <it>Chlamydiaceae </it>in healthy dogs and in dogs with genital or upper respiratory disease, including conjunctivitis.</p> <p>Methods</p> <p>A real-time polymerase chain reaction (PCR) for <it>Chlamydiaceae </it>was used to detect any chlamydial species within this family. Swab samples from the conjunctiva and the mucosal membranes of the oropharynx, rectum and genital tract were taken from 79 dogs: 27 clinically healthy dogs, 25 dogs with clinical signs from the genital tract and 28 dogs with conjunctivitis. There were 52 female and 27 male dogs. From 7 of the male dogs, additional semen samples were analysed.</p> <p>Results</p> <p>No <it>Chlamydiaceae </it>were detected from any dog.</p> <p>Conclusions</p> <p>Although the number of dogs that was included is limited, the results suggest that cases of <it>Chlamydiaceae </it>in dogs probably are related to infection from other species, and that dogs in general do not harbour <it>Chlamydiaceae</it>. Bacteria belonging to the family <it>Chlamydiaceae </it>do not seem to be of major importance for genital or ocular disease in Swedish dogs.</p

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ‘known’ interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe