Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.
In the last two decades the study of changes in the genome function that are not induced by changes in DNA has consolidated a strong research field called ”epigenetics”. Chromatin state changes play an essential role in the regulation of transcription of many genes, thus controlling cell differentiation. A large part of these changes is due to histone modifications that alter the accessibility of the DNA.
Current state of the art visualization methods for the analysis of epigenetic data sets are not suited to represent the relationship between the combinatorial pattern of histone modifications and their regulatory effects. A recent strategy to generate a global overview of these interactions is the use of scatterplots. One of the biggest weaknesses of scatterplots is the overplotting. This can be solved using a 2D tiled-binned representation strategy, where dividing scatterplot into bins consisting of tiles for each modification pattern is possible. However, this 2D strategy does not allow to represent the interaction of more than two histone modifications.
Here, TiBi-3D, a tool that can visualize the combinatorics of histone modifications with tiled-binned 3D scatterplots, is presented. Two important features of TiBi-3D are that tiles are represented with spheres in the scatterplot, and that their position and color encodes the histone modification pattern they represent. TiBi-3D also includes a transparency value assigned to each of that spheres to depict the amount of data points in each bin. In addition, to reduce the occlusion in the scatterplot each transparency value is initially filtered by an outlier detection, transformed to log scale, and then normalized. TiBi-3D provides features for exploration and interaction with the scatterplot and the data, thus enabling to examine the data set thoroughly. It is also possible to export the results as figures or in bed file format for further processing. By using TiBi-3D, for example, it was possible to observe new relations between the CpG-density and histone modifications in different cell types. In conclusion, TiBi-3D is an excellent tool for the analysis of global patterns in epigenetic data.
One way to analyse word relations is to examine their co-occurrence in the same context. This allows for the identification of potential semantic or lexical relationships between words. As previous studies showed word co-occurrences often reflect human stimuli-response pairs. In this paper significant sentence co-occurrences on word level were used to identify potential responses for word stimuli based on three automatically generated text corpora of the Leipzig Corpora Collection.
Epigenetics studies heritable phenotypic changes which are not due to changes in the DNA sequence. The molecular basis is chromatin forming a “beads on a string”-like structure of histones. Here we present a new tool called ChromatinVis to visualize ChIP-seq data. Before visualization, the histone modification data is segmented typically yielding several millions of data points. In our example, we process data from three cell types and three modifications resulting in eight combinations. The challenging problem is to study the global changes of histone modifications between different cell types. The data are clustered using the k means++ algorithm. For each cluster we allow the user to study the global and local distribution of histone marks using radial windmill charts. To analyze the configuration of the clusters in the data space we use scatterplots in combination with a Principle Component Analyses. A multitude of filtering options and several methods for outlier detection, like calculation of silhouette coefficients, allow the user to improve clustering. From a biological perspective, the tool gives a deeper insight into relationship between histone modifications.
Published as a poster at the Vizbi 2014 conference.
Over the last years, more and more biological data became available. Besides the pure amount of new data, also its dimensionality – the number of different attributes per data point – increased. Recently, especially the amount of data on chromatin and its modifications increased considerably. In the field of epigenetics, appropriate visualization tools designed for highlighting the different aspects of epigenetic data are currently not available. We present a tool called TiBi-Scatter enabling correlation analysis in 2D. This approach allows for analyzing multidimensional data while keeping the use of resources such as memory small. Thus, it is in particular applicable to large data sets.
TiBi-Scatter is a resource-friendly and easy to use tool that allows for the hypothesis-free analysis of large multidimensional biological data sets.
Published at the BioVis 2014