Modelling the conditional regulatory activity of methylated and bivalent promoters

Extrinsic Noise and Heavy-Tailed Laws in Gene Expression

Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our ansatz allows us to incorporate the effects of extrinsic noise-encompassing factors external to the transcription of the individual gene-and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

Age-related epigenetic changes in hippocampal subregions of four animal models of Alzheimer's disease

Both aging and Alzheimer's disease (AD) are associated with widespread epigenetic changes, with most evidence suggesting global hypomethylation in AD. It is, however, unclear how these age-related epigenetic changes are linked to molecular aberrations as expressed in animal models of AD. Here, we investigated age-related changes of epigenetic markers of DNA methylation and hydroxymethylation in a range of animal models of AD, and their correlations with amyloid plaque load. Three transgenic mouse models, including the J20, APP/PS1dE9 and 3xTg-AD models, as well as Caribbean vervets (a non-transgenic non-human primate model of AD) were investigated. In the J20 mouse model, an age-related decrease in DNA methylation was found in the dentate gyrus (DG) and a decrease in the ratio between DNA methylation and hydroxymethylation was found in the DG and cornu ammonis (CA) 3. In the 3xTg-AD mice, an age-related increase in DNA methylation was found in the DG and CA1-2. No significant age-related alterations were found in the APP/PS1dE9 mice and non-human primate model. In the J20 model, hippocampal plaque load showed a significant negative correlation with DNA methylation in the DG, and with the ratio a negative correlation in the DG and CA3. For the APP/PS1dE9 model a negative correlation between the ratio and plaque load was observed in the CA3, as well as a negative correlation between DNA methyltransferase 3A (DNMT3A) levels and plaque load in the DG and CA3. Thus, only the J20 model showed an age-related reduction in global DNA methylation, while DNA hypermethylation was observed in the 3xTg-AD model. Given these differences between animal models, future studies are needed to further elucidate the contribution of different AD-related genetic variation to age-related epigenetic changes.

Distributed gene expression modelling for exploring variability in epigenetic function

BACKGROUND

Predictive gene expression modelling is an important tool in computational biology due to the volume of high-throughput sequencing data generated by recent consortia. However, the scope of previous studies has been restricted to a small set of cell-lines or experimental conditions due an inability to leverage distributed processing architectures for large, sharded data-sets.

RESULTS

We present a distributed implementation of gene expression modelling using the MapReduce paradigm and prove that performance improves as a linear function of available processor cores. We then leverage the computational efficiency of this framework to explore the variability of epigenetic function across fifty histone modification data-sets from variety of cancerous and non-cancerous cell-lines.

CONCLUSIONS

We demonstrate that the genome-wide relationships between histone modifications and mRNA transcription are lineage, tissue and karyotype-invariant, and that models trained on matched -omics data from non-cancerous cell-lines are able to predict cancerous expression with equivalent genome-wide fidelity.

Cautionary Tales of Inapproximability

Modeling biology as classical problems in computer science allows researchers to leverage the wealth of theoretical advancements in this field. Despite countless studies presenting heuristics that report improvement on specific benchmarking data, there has been comparatively little focus on exploring the theoretical bounds on the performance of practical (polynomial-time) algorithms. Conversely, theoretical studies tend to overstate the generalizability of their conclusions to physical biological processes. In this article we provide a fresh perspective on the concepts of NP-hardness and inapproximability in the computational biology domain, using popular sequence assembly and alignment (mapping) algorithms as illustrative examples. These algorithms exemplify how computer science theory can both (a) lead to substantial improvement in practical performance and (b) highlight areas ripe for future innovation. Importantly, we discuss caveats that seemingly allow the performance of heuristics to exceed their provable bounds.

Information theoretic approaches for inference of biological networks from continuous-valued data

Background

Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory.

Results

We introduce new model-free and non-linear information theoretic measures for the inference of GRNs and other biological networks from continuous-valued data. Although previous tools have implemented mutual information as a means of inferring pairwise associations, they either introduce statistical bias through discretisation or are limited to modelling undirected relationships. Our approach overcomes both of these limitations, as demonstrated by a substantial improvement in empirical performance for a set of 160 GRNs of varying size and topology.

Conclusions

The information theoretic measures described in this study yield substantial improvements over previous approaches (e.g. ARACNE) and have been implemented in the latest release of NAIL (Network Analysis and Inference Library). However, despite the theoretical and empirical advantages of these new measures, they do not circumvent the fundamental limitation of indeterminacy exhibited across this class of biological networks. These methods have presently found value in computational neurobiology, and will likely gain traction for GRN analysis as the volume and quality of temporal transcriptomics data continues to improve.

FlexDM: Simple, parallel and fault-tolerant data mining using WEKA

Background

With the continued exponential growth in data volume, large-scale data mining and machine learning experiments have become a necessity for many researchers without programming or statistics backgrounds. WEKA (Waikato Environment for Knowledge Analysis) is a gold standard framework that facilitates and simplifies this task by allowing specification of algorithms, hyper-parameters and test strategies from a streamlined Experimenter GUI. Despite its popularity, the WEKA Experimenter exhibits several limitations that we address in our new FlexDM software.

Results

FlexDM addresses four fundamental limitations with the WEKA Experimenter: reliance on a verbose and difficult-to-modify XML schema; inability to meta-optimise experiments over a large number of algorithm hyper-parameters; inability to recover from software or hardware failure during a large experiment; and failing to leverage modern multicore processor architectures. Direct comparisons between the FlexDM and default WEKA XML schemas demonstrate a 10-fold improvement in brevity for a specification that allows finer control of experimental procedures. The stability of FlexDM has been tested on a large biological dataset (approximately 450 k attributes by 150 samples), and automatic parallelisation of tasks yields a quasi-linear reduction in execution time when distributed across multiple processor cores.

Conclusion

FlexDM is a powerful and easy-to-use extension to the WEKA package, which better handles the increased volume and complexity of data that has emerged during the 20 years since WEKA’s original development. FlexDM has been tested on Windows, OSX and Linux operating systems and is provided as a pre-configured virtual reference environment for trivial usage and extensibility. This software can substantially improve the productivity of any research group conducting large-scale data mining or machine learning tasks, in addition to providing non-programmers with improved control over specific aspects of their data analysis pipeline via a succinct and simplified XML schema.

Electronic supplementary material

The online version of this article (doi:10.1186/s13029-015-0045-3) contains supplementary material, which is available to authorized users.