The Detection of Hamster Connexins: A Comparison of Expression Profiles with Wild-Type Mouse and the Cancer-Prone Min Mouse

The open reading frames of 17 connexins from Syrian hamster (using tissues) and 16 connexins from the Chinese hamster cell line V79, were fully (Cx30, Cx31, Cx37, Cx43 and Cx45) or partially sequenced. We have also detected, and partially sequenced, seven rat connexins that previously were unavailable. The expression of connexin genes was examined in some hamster organs and cultured hamster cells, and compared with wild-type mouse and the cancer-prone Min mouse. Although the expression patterns were similar for most organs and connexins in hamster and mouse, there were also some prominent differences (Cx29 and 30.3 in testis; Cx31.1 and 32 in eye; Cx46 in brain, kidney and testis; Cx47 in kidney). This suggests that some connexins have species-specific expression profiles. In contrast, there were minimal differences in expression profiles between wild type and Min mice. Species-specific expression profiles should be considered in attempts to make animal models of human connexin-associated diseases.     

A Centipede Toxin Family Defines an Ancient Class of CSαβ Defensins

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ClusTrack: Feature Extraction and Similarity Measures for Clustering of Genome-Wide Data Sets

Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.     

Sea trout Salmo trutta in the subarctic: home-bound but large variation in migratory behaviour between and within populations

Anadromous brown trout (sea trout), Salmo trutta, is currently in decline throughout its range, largely due to anthropogenic stressors in freshwater and marine habitats. Acoustic telmetry was utilized to study the marine migration of sea trout post-smolts from three populations in a relatively pristine subarctic fjord system. While at sea, the sea trout spent a substantial part of their time close to their natal river, preferred near shore over pelagic habitats and were strongly surface oriented. Despite a fidelity towards local areas, the sea trout utilized various parts of the fjord system, with maximum dispersion >30 km and total migration distance >300 km. Almost half of the sea trout (44%) migrated between river outlets, indicating that a metapopulation approach may be appropriate when managing neighbouring sea trout populations at high latitudes. Furthermore, the different populations displayed different migratory behaviours in terms of distance migrated, dispersion from origin and the likelihood of leaving their home area. This variation in migratory behaviour is likely influenced by spatiotemporal differences in habitat quality between sites, indicating that local habitat variations may promote population-specific behavioural responses even in relatively confined fjord systems.     

A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher''s exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.