Paweł Widera

Transcriptional Dynamics of Two Seed Compartments with Opposing Roles in Arabidopsis Seed Germination

Gene expression profiling in two seed compartments uncovers two transcriptional phases during seed germination that are separated by testa rupture. Seed germination is a critical stage in the plant life cycle and the first step toward successful plant establishment. Therefore, understanding germination is of important ecological and agronomical relevance. Previous research revealed that different seed compartments (testa, endosperm, and embryo) control germination, but little is known about the underlying spatial and temporal transcriptome changes that lead to seed germination. We analyzed genome-wide expression in germinating Arabidopsis (Arabidopsis thaliana) seeds with both temporal and spatial detail and provide Web-accessible visualizations of the data reported (vseed.nottingham.ac.uk). We show the potential of this high-resolution data set for the construction of meaningful coexpression networks, which provide insight into the genetic control of germination. The data set reveals two transcriptional phases during germination that are separated by testa rupture. The first phase is marked by large transcriptome changes as the seed switches from a dry, quiescent state to a hydrated and active state. At the end of this first transcriptional phase, the number of differentially expressed genes between consecutive time points drops. This increases again at testa rupture, the start of the second transcriptional phase. Transcriptome data indicate a role for mechano-induced signaling at this stage and subsequently highlight the fates of the endosperm and radicle: senescence and growth, respectively. Finally, using a phylotranscriptomic approach, we show that expression levels of evolutionarily young genes drop during the first transcriptional phase and increase during the second phase. Evolutionarily old genes show an opposite pattern, suggesting a more conserved transcriptome prior to the completion of germination.

Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features

MOTIVATION The prediction of a proteins contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation. RESULTS The main part of the evaluation is the comparison against the sequence-based contact prediction methods from CASP9, where our method presented the best rank in five out of the six evaluated metrics. We also assess the impact of the size of the ensemble used in our predictor to show the trade-off between performance and training time of our method. Finally, we also study the rule sets generated by our machine learning system. From this analysis, we are able to estimate the contribution of the attributes in our representation and how these interact to derive contact predictions. AVAILABILITY http://icos.cs.nott.ac.uk/servers/psp.html. CONTACT natalio.krasnogor@nottingham.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

JEPETTO: a Cytoscape plugin for gene set enrichment and topological analysis based on interaction networks

Summary: JEPETTO (Java Enrichment of Pathways Extended To TOpology) is a Cytoscape 3.x plugin performing integrative human gene set analysis. It identifies functional associations between genes and known cellular pathways, and processes using protein interaction networks and topological analysis. The plugin integrates information from three separate web servers we published previously, specializing in enrichment analysis, pathways expansion and topological matching. This integration substantially simplifies the analysis of user gene sets and the interpretation of the results. We demonstrate the utility of the JEPETTO plugin on a set of misregulated genes associated with Alzheimer’s disease. Availability: Source code and binaries are freely available for download at http://apps.cytoscape.org/apps/jepetto, implemented in Java and multi-platform. Installable directly via Cytoscape plugin manager. Released under the GNU General Public Licence. Contact: jepetto.plugin@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

GP challenge: evolving energy function for protein structure prediction

One of the key elements in protein structure prediction is the ability to distinguish between good and bad candidate structures. This distinction is made by estimation of the structure energy. The energy function used in the best state-of-the-art automatic predictors competing in the most recent CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiment is defined as a weighted sum of a set of energy terms designed by experts. We hypothesised that combining these terms more freely will improve the prediction quality. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. We compared the predictive power of the best evolved function and a linear combination of energy terms featuring weights optimised by the Nelder–Mead algorithm. The GP based optimisation outperformed the optimised linear function. We have made the data used in our experiments publicly available in order to encourage others to further investigate this challenging problem by using GP and other methods, and to attempt to improve on the results presented here.

Meta-stochastic simulation of biochemical models for systems and synthetic biology.

Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochemical systems at low species concentrations. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to determine which algorithm is best suited to simulate a particular model and that this can be predicted a priori to algorithm execution. We present a Web based tool ssapredict that allows scientists to upload a biochemical model and obtain a prediction of the best performing SSA. Furthermore, ssapredict gives the user the option to download our high performance simulator ngss preconfigured to perform the simulation of the queried biochemical model with the predicted fastest algorithm as the simulation engine. The ssapredict Web application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.

Functional networks inference from rule-based machine learning models

BackgroundFunctional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods.ResultsWe propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process.AvailabilityThe implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html

Evolutionary design of the energy function for protein structure prediction

Automatic protein structure predictors use the notion of energy to guide the search towards good candidate structures. The energy functions used by the state-of-the-art predictors are defined as a linear combination of several energy terms designed by human experts. We hypothesised that the energy based guidance could be more accurate if the terms were combined more freely. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. Using several different fitness functions we examined the potential of the evolutionary approach on a set of candidate structures generated during the protein structure prediction process. Although our algorithms were able to improve over the random walk, the fitness of the best individuals was far from the optimum. We discuss the shortcomings of our initial algorithm design and the possible directions for further research.

Parallel DNA sequence assembly

A heuristic algorithm for the DNA sequence assembly problem is presented. Its sequential implementation is described as well as its parallelization method. A computational experiment shows how the parallel algorithm speed depends on a number of processes. Tests on real data from experiments with the SARS coronavirus are also discussed, and the outcome of our algorithm appears to be biologically correct.