A comparative study of S/MAR prediction tools

BACKGROUND: S/MARs are regions of the DNA that are attached to the nuclear matrix. These regions are known to affect substantially the expression of genes. The computer prediction of S/MARs is a highly significant task which could contribute to our understanding of chromatin organisation in eukaryotic cells, the number and distribution of boundary elements, and the understanding of gene regulation in eukaryotic cells. However, while a number of S/MAR predictors have been proposed, their accuracy has so far not come under scrutiny. RESULTS: We have selected S/MARs with sufficient experimental evidence and used these to evaluate existing methods of S/MAR prediction. Our main results are: 1.) all existing methods have little predictive power, 2.) a simple rule based on AT-percentage is generally competitive with other methods, 3.) in practice, the different methods will usually identify different sub-sequences as S/MARs, 4.) more research on the H-Rule would be valuable. CONCLUSION: A new insight is needed to design a method which will predict S/MARs well. Our data, including the control data, has been deposited as additional material and this may help later researchers test new predictors

RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.Comment: A preprint of an invited review manuscript that will be published in a chapter of the book `Methods in Molecular Biology'. Note that this version of the manuscript may differ from the published versio

Reliability and validity in comparative studies of software prediction models

Empirical studies on software prediction models do not converge with respect to the question "which prediction model is best?" The reason for this lack of convergence is poorly understood. In this simulation study, we have examined a frequently used research procedure comprising three main ingredients: a single data sample, an accuracy indicator, and cross validation. Typically, these empirical studies compare a machine learning model with a regression model. In our study, we use simulation and compare a machine learning and a regression model. The results suggest that it is the research procedure itself that is unreliable. This lack of reliability may strongly contribute to the lack of convergence. Our findings thus cast some doubt on the conclusions of any study of competing software prediction models that used this research procedure as a basis of model comparison. Thus, we need to develop more reliable research procedures before we can have confidence in the conclusions of comparative studies of software prediction models

An Ensemble Model of QSAR Tools for Regulatory Risk Assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study

Sequence-Based Analysis of Thermal Adaptation and Protein Energy Landscapes in an Invasive Blue Mussel (Mytilus galloprovincialis).

Adaptive responses to thermal stress in poikilotherms plays an important role in determining competitive ability and species distributions. Amino acid substitutions that affect protein stability and modify the thermal optima of orthologous proteins may be particularly important in this context. Here, we examine a set of 2,770 protein-coding genes to determine if proteins in a highly invasive heat tolerant blue mussel (Mytilus galloprovincialis) contain signals of adaptive increases in protein stability relative to orthologs in a more cold tolerant M. trossulus. Such thermal adaptations might help to explain, mechanistically, the success with which the invasive marine mussel M. galloprovincialis has displaced native species in contact zones in the eastern (California) and western (Japan) Pacific. We tested for stabilizing amino acid substitutions in warm tolerant M. galloprovincialis relative to cold tolerant M. trossulus with a generalized linear model that compares in silico estimates of recent changes in protein stability among closely related congeners. Fixed substitutions in M. galloprovincialis were 3,180.0 calories per mol per substitution more stabilizing at genes with both elevated dN/dS ratios and transcriptional responses to heat stress, and 705.8 calories per mol per substitution more stabilizing across all 2,770 loci investigated. Amino acid substitutions concentrated in a small number of genes were more stabilizing in M. galloprovincialis compared with cold tolerant M. trossulus. We also tested for, but did not find, enrichment of a priori GO terms in genes with elevated dN/dS ratios in M. galloprovincialis. This might indicate that selection for thermodynamic stability is generic across all lineages, and suggests that the high change in estimated protein stability that we observed in M. galloprovincialis is driven by selection for extra stabilizing substitutions, rather than by higher incidence of selection in a greater number of genes in this lineage. Nonetheless, our finding of more stabilizing amino acid changes in the warm adapted lineage is important because it suggests that adaption for thermal stability has contributed to M. galloprovincialis' superior tolerance to heat stress, and that pairing tests for positive selection and tests for transcriptional response to heat stress can identify candidates of protein stability adaptation

Adaptive management, international co-operation and planning for marine conservation hotspots in a changing climate

Acknowledgements This work received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (Grant reference HR09011) and contributing institutions.Peer reviewedPublisher PD

Predictive habitat modelling as a tool to assess the change in distribution and extent of an OSPAR priority habitat under an increased ocean temperature scenario:consequences for marine protected area networks and management

The aims of this study were to determine the extent and distribution of an OSPAR priority habitat under current baseline ocean temperatures; to illustrate the prospect for habitat loss under a changing ocean temperature scenario; and to demonstrate the potential application of predictive habitat mapping in "future-proofing" conservation and biodiversity management. Maxent modelling and GIS environmental envelope analysis of the biogenic bed forming species, Modiolus modiolus was carried out. The Maxent model was tested and validated using 75%/25% training/test occurrence records and validated against two sampling biases (the whole study area and a 20km buffer). The model was compared to the envelope analysis and the area under the receiver operating characteristic curve (Area Under the curve; AUC) was evaluated. The performance of the Maxent model was rated as 'good' to 'excellent' on all replicated runs and low variation in the runs was recorded from the AUC values. The extent of "most suitable", "less suitable" and "unsuitable" habitat was calculated for the baseline year (2009) and the projected increased ocean temperature scenarios (2030, 2050, 2080 and 2100). A loss of 100% of "most suitable" habitat was reported by 2080. Maintaining a suitable level of protection of marine habitats/species of conservation importance may require management of the decline and migration rather than maintenance of present extent. Methods applied in this study provide the initial application of a plausible "conservation management tool"

miRNA expression profiles and molecular networks in resting and LPS-activated BV-2 microglia-Effect of cannabinoids.

Mammalian microRNAs (miRNAs) play a critical role in modulating the response of immune cells to stimuli. Cannabinoids are known to exert beneficial actions such as neuroprotection and immunosuppressive activities. However, the underlying mechanisms which contribute to these effects are not fully understood. We previously reported that the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC) and the non-psychoactive cannabidiol (CBD) differ in their anti-inflammatory signaling pathways. Using lipopolysaccharide (LPS) to stimulate BV-2 microglial cells, we examined the role of cannabinoids on the expression of miRNAs. Expression was analyzed by performing deep sequencing, followed by Ingenuity Pathway Analysis to describe networks and intracellular pathways. miRNA sequencing analysis revealed that 31 miRNAs were differentially modulated by LPS and by cannabinoids treatments. In addition, we found that at the concentration tested, CBD has a greater effect than THC on the expression of most of the studied miRNAs. The results clearly link the effects of both LPS and cannabinoids to inflammatory signaling pathways. LPS upregulated the expression of pro-inflammatory miRNAs associated to Toll-like receptor (TLR) and NF-κB signaling, including miR-21, miR-146a and miR-155, whereas CBD inhibited LPS-stimulated expression of miR-146a and miR-155. In addition, CBD upregulated miR-34a, known to be involved in several pathways including Rb/E2f cell cycle and Notch-Dll1 signaling. Our results show that both CBD and THC reduced the LPS-upregulated Notch ligand Dll1 expression. MiR-155 and miR-34a are considered to be redox sensitive miRNAs, which regulate Nrf2-driven gene expression. Accordingly, we found that Nrf2-mediated expression of redox-dependent genes defines a Mox-like phenotype in CBD treated BV-2 cells. In summary, we have identified a specific repertoire of miRNAs that are regulated by cannabinoids, in resting (surveillant) and in LPS-activated microglia. The modulated miRNAs and their target genes are controlled by TLR, Nrf2 and Notch cross-talk signaling and are involved in immune response, cell cycle regulation as well as cellular stress and redox homeostasis