Fotis E. Psomopoulos

Detection of Genomic Idiosyncrasies Using Fuzzy Phylogenetic Profiles

Phylogenetic profiles express the presence or absence of genes and their homologs across a number of reference genomes. They have emerged as an elegant representation framework for comparative genomics and have been used for the genome-wide inference and discovery of functionally linked genes or metabolic pathways. As the number of reference genomes grows, there is an acute need for faster and more accurate methods for phylogenetic profile analysis with increased performance in speed and quality. We propose a novel, efficient method for the detection of genomic idiosyncrasies, i.e. sets of genes found in a specific genome with peculiar phylogenetic properties, such as intra-genome correlations or inter-genome relationships. Our algorithm is a four-step process where genome profiles are first defined as fuzzy vectors, then discretized to binary vectors, followed by a de-noising step, and finally a comparison step to generate intra- and inter-genome distances for each gene profile. The method is validated with a carefully selected benchmark set of five reference genomes, using a range of approaches regarding similarity metrics and pre-processing stages for noise reduction. We demonstrate that the fuzzy profile method consistently identifies the actual phylogenetic relationship and origin of the genes under consideration for the majority of the cases, while the detected outliers are found to be particular genes with peculiar phylogenetic patterns. The proposed method provides a time-efficient and highly scalable approach for phylogenetic stratification, with the detected groups of genes being either similar to their own genome profile or different from it, thus revealing atypical evolutionary histories.

Towards an integrated robotics architecture for social inclusion – The RAPP paradigm

Abstract Scientific breakthroughs have led to an increase in life expectancy, to the point where senior citizens comprise an ever increasing percentage of the general population. In this direction, the EU funded RAPP project “Robotic Applications for Delivering Smart User Empowering Applications” introduces socially interactive robots that will not only physically assist, but also serve as a companion to senior citizens. The proposed RAPP framework has been designed aiming towards a cloud-based integrated approach that enables robotic devices to seamlessly deploy robotic applications, relieving the actual robots from computational burdens. The Robotic Applications (RApps) developed according to the RAPP paradigm will empower consumer social robots, allowing them to adapt to versatile situations and materialize complex behaviors and scenarios. The RAPP pilot cases involve the development of RApps for the NAO humanoid robot and the ANG-MED rollator targeting senior citizens that (a) are technology illiterate, (b) have been diagnosed with mild cognitive impairment or (c) are in the process of hip fracture rehabilitation. Initial results establish the robustness of RAPP in addressing the needs of end users and developers, as well as its contribution in significantly increasing the quality of life of senior citizens.

The Chlamydiales Pangenome Revisited: Structural Stability and Functional Coherence

The entire publicly available set of 37 genome sequences from the bacterial order Chlamydiales has been subjected to comparative analysis in order to reveal the salient features of this pangenome and its evolutionary history. Over 2,000 protein families are detected across multiple species, with a distribution consistent to other studied pangenomes. Of these, there are 180 protein families with multiple members, 312 families with exactly 37 members corresponding to core genes, 428 families with peripheral genes with varying taxonomic distribution and finally 1,125 smaller families. The fact that, even for smaller genomes of Chlamydiales, core genes represent over a quarter of the average protein complement, signifies a certain degree of structural stability, given the wide range of phylogenetic relationships within the group. In addition, the propagation of a corpus of manually curated annotations within the discovered core families reveals key functional properties, reflecting a coherent repertoire of cellular capabilities for Chlamydiales. We further investigate over 2,000 genes without homologs in the pangenome and discover two new protein sequence domains. Our results, supported by the genome-based phylogeny for this group, are fully consistent with previous analyses and current knowledge, and point to future research directions towards a better understanding of the structural and functional properties of Chlamydiales.

De novo transcriptome assembly of two contrasting pumpkin cultivars

Cucurbita pepo (squash, pumpkin, gourd), a worldwide-cultivated vegetable of American origin, is extremely variable in fruit characteristics. However, the information associated with genes and genetic markers for pumpkin is very limited. In order to identify new genes and to develop genetic markers, we performed a transcriptome analysis (RNA-Seq) of two contrasting pumpkin cultivars. Leaves and female flowers of cultivars, ‘Big Moose’ with large round fruits and ‘Munchkin’ with small round fruits, were harvested for total RNA extraction. We obtained a total of 6 GB (Big Moose; http://www.ncbi.nlm.nih.gov/Traces/sra/?run=SRR3056882) and 5 GB (Munchkin; http://www.ncbi.nlm.nih.gov/Traces/sra/?run=SRR3056883) sequence data (NCBI SRA database SRX1502732 and SRX1502735, respectively), which correspond to 18,055,786 and 14,824,292 150-base reads. After quality assessment, the clean sequences where 17,995,932 and 14,774,486 respectively. The numbers of total transcripts for ‘Big Moose’ and ‘Munchkin’ were 84,727 and 68,051, respectively. TransDecoder identified possible coding regions in assembled transcripts. This study provides transcriptome data for two contrasting pumpkin cultivars, which might be useful for genetic marker development and comparative transcriptome analyses.

Future opportunities and trends for e-infrastructures and life sciences: going beyond the grid to enable life science data analysis

With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community.

Multi Level Clustering of Phylogenetic Profiles

The prediction of gene function from genome sequences is one of the main issues in Bioinformatics. Most computational approaches are based on the similarity between sequences to infer gene function. However, the availability of several fully sequenced genomes has enabled alternative approaches, such as phylogenetic profiles. Phylogenetic profiles are vectors which indicate the presence or absence of a gene in other genomes. The main concept of phylogenetic profiles is that proteins participating in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion. In this paper, a multi level clustering algorithm of phylogenetic profiles is presented, which aims to detect inter- and intra-genome gene clusters.

BioPAXViz: a cytoscape application for the visual exploration of metabolic pathway evolution

BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. AVAILABILITY AND IMPLEMENTATION BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU-CERTH/BioPAX.Viz The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository. CONTACT ouzounis@certh.gr.Summary: BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre‐computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. Availability and Implementation: BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU‐CERTH/BioPAX.Viz. The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository. Contact: ouzounis@certh.gr.

Comparative Genomics of Botrytis cinerea Strains with Differential Multi-Drug Resistance

Botrytis cinerea is a ubiquitous fungus difficult to control because it possess a variety of attack modes, diverse hosts as inoculum sources, and it can survive as mycelia and/or conidia or for extended periods as sclerotia in crop debris. For these reasons the use of any single control measure is unlikely to succeed and a combination of cultural practices with the application of site-specific synthetic compounds provide the best protection for the crops (Williamson et al., 2007). However, the chemical control has been adversely affected by the development of fungicide resistance. The selection of resistant individuals in a fungal population subjected to selective pressure due to fungicides is an evolutionary mechanism that promotes advantageous genotypes (Walker et al., 2013). High levels of resistance to site-specific fungicides are commonly associated with point mutations. For example the mutations G143A, H272R, and F412S leading to changes in the target proteins CytB, SdhB, and Erg27 are conferring resistance of the pathogen to the chemical classes of QoIs, SDHIs, and hydroxyanilides, respectively (Leroux, 2007). Multidrug resistance is another mechanism associated with resistance in B. cinerea which involves mutations leading to overexpression of individual transporters such as ABC and MFS (Kretschmer et al., 2009). This mechanism is associated with low levels of resistance to multiple fungicides including the anilinopyrimidines and phenylpyrroles. However, a subdivision of gray mold populations was found to be more tolerant to these two classes of fungicides (Leroch et al., 2013). Previous reports have clearly demonstrated that the resistance to anilinopyrimidines has a qualitative, disruptive pattern, and is monogenically controlled (Chapeland et al., 1999). In order to elucidate the mechanism of the resistance, the whole genome of three different samples (gene pools) was sequenced, each containing DNA of 10 selected strains of the same genotype regarding resistance to seven different classes of fungicides including anilinopyrimidines. This report presents the publicly available genomic data.

A Grid-Enabled Modular Framework for Efficient Sequence Analysis Workflows

In the era of Big Data in Life Sciences, efficient processing and analysis of vast amounts of sequence data is becoming an ever daunting challenge. Among such analyses, sequence alignment is one of the most commonly used procedures, as it provides useful insights on the functionality and relationship of the involved entities. Sequence alignment is one of the most common computational bottlenecks in several bioinformatics workflows. We have designed and implemented a time-efficient distributed modular application for sequence alignment, phylogenetic profiling and clustering of protein sequences, by utilizing the European Grid Infrastructure. The optimal utilization of the Grid with regards to the respective modules, allowed us to achieve significant speedups to the order of 1400%.

Multi-genome Core Pathway Identification through Gene Clustering

In the wake of gene-oriented data analysis in large-scale bioinformatics studies, focus in research is currently shifting towards the analysis of the functional association of genes, namely the metabolic pathways in which genes participate. The goal of this paper is to attempt to identify the core genes in a specific pathway, based on a user-defined selection of genomes. To this end, a novel methodology has been developed that uses data from the KEGG database, and through the application of the MCL clustering algorithm, identifies clusters that correspond to different “layers” of genes, either on a phylogenetic or a functional level. The algorithm’s complexity, evaluated experimentally, is presented and the results on a characteristic case study are discussed.