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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P34. FUNCTIONAL SUBGRAPH ENRICHMENTS FOR NODE SETS IN REGULATORY NETWORKS Pieter Meysman 1,2* , Yvan Saeys 3,4 , Ehsan Sabaghian 5,6 , Wout Bittremieux 1,2 , Yves van de Peer 5,6 , Bart Goethals 1 & Kris Laukens 1,2 . Advanced Database Research and Modeling (ADReM), University of Antwerp 1 ; Biomedical informatics research center Antwerpen (biomina) 2 ; VIB Inflammation Research Center 3 ; Department of Respiratory Medicine, Ghent University 4 ; Department of Plant Biotechnology and Bioinformatics, Ghent University 5 ; Department of Plant Systems Biology, VIB/Ghent University 6 . * pieter.meysman@uantwerpen.be We have developed a subgroup discovery algorithm to find subgraphs in a single graph that are associated with a given set of nodes. The association between a subgraph pattern and a set of vertices is defined by its significant enrichment based on a Bonferroni-corrected hypergeometric probability value, and can therefore be considered as a network-focused extension of traditional gene ontology enrichment analysis. We demonstrate the operation of this algorithm by applying it on two transcriptional regulatory networks and show that we can find relevant functional subgraphs enriched for the selected nodes. INTRODUCTION Frequent subgraph mining (FSM) is a common but complex problem within the data mining field that has gained in importance as more graph data has become available. However traditional FSM finds all frequent subgraphs within the graph dataset, while often a more interesting query is to find the subgraphs that are most associated with a specific set of nodes. Nodes of interest might be those that are associated with a specific disease, or those that are differentially expressed in an omics experiment. METHODS To address this issue, we developed a novel subgraph mining algorithm that can efficiently construct, match and test candidate subgraphs against the given graph for enrichment within a specific set of nodes (Meysman et al. 2015). To allow the enrichment testing, each candidate subgraph is built around a ‘source’ node. A subgraph match where the source node corresponds to a node of interest is counted as a ‘hit’. If the source node is not a node of interest, it is counted as a background hit. In this manner the problem of enrichment can be easily tested using a hypergeometric test. Furthermore, we show that this definition of enrichment allows us to drastically prune the search space that the algorithm must traverse to find all enriched subgraphs. An implementation of the algorithm is available at http://adrem.ua.ac.be/sigsubgraph. RESULTS & DISCUSSION The first data set concerned the yeast genes that have remained in duplicate following the most recent whole genome duplication. Within the yeast transcriptional network, we found that these duplicate genes were enriched for self-regulating motifs (e.g. feedback loops, self edges, etc.), which matches the duplicated nature of these genes (Figure 1). FIGURE 1. Enriched subgraphs for yeast duplicated genes The second data set concerned mining the subgraphs associated with the homologs of the PhoR transcription factor across seven different inferred bacterial regulatory networks from Colombos expression data (Meysman et al. 2014). These PhoR homologs were found to be significantly associated with several complex regulatory motifs. REFERENCES Meysman P et al. Discovery of Significantly Enriched Subgraphs Associated with Selected Vertices in a Single Graph. Proceedings of the 14th International Workshop on Data Mining in Bioinformatics (2015). Meysman P et al. COLOMBOS v2. 0: an ever expanding collection of bacterial expression compendia. Nucleic acids research 42 (D1), D649-D653 (2014). 78
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: 000 Category: Poster 10th Benelux Bioinformatics Conference bbc 2015 P35. HUMANS DROVE THE INTRODUCTION & SPREAD OF MYCOBACTERIUM ULCERANS IN AFRICA Koen Vandelannoote 1,2,* , Conor Meehan 1* , Miriam Eddyani 1 , Dissou Affolabi 3 , Delphin Mavinga Phanzu 4 , Sara Eyangoh 5 , Kurt Jordaens 6 , Françoise Portaels 1 , Kirstie Mangas 7 , Torsten Seemann 7 , Herwig Leirs 2 , Tim Stinear 7 & Bouke C. de Jong 1 . Institute of Tropical Medicine, Antwerp, Belgium 1 ; Evolutionary Ecology Group, University of Antwerp, Antwerp, Belgium 2 ; Laboratoire de Référence des Mycobactéries, Cotonou, Benin 3 ; Institut Médical Evangélique, Kimpese, Democratic Republic of Congo 4 ; Centre Pasteur du Cameroun, Yaoundé, Cameroun 5 ; Joint Experimental Molecular Unit, Royal Museum for Central Africa, Tervuren, Belgium 6 ; Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia 7 . *cmeehan@itg.be Buruli ulcer (BU) is an insidious neglected tropical disease. BU is reported around the world but the rural regions of West and Central Africa are most affected. How BU is transmitted and spreads has remained a mystery, even though the causative agent, Mycobacterium ulcerans, has been known for more than 70 years. Here, using the tools of population genomics, we reconstruct the evolutionary history of M. ulcerans by comparing 167 isolates spanning 48 years and representing 11 endemic countries across Africa. The genetic diversity of African M. ulcerans proved very limited because of its slow substitution rate coupled with its recent origin. We show for the first time how M. ulcerans has existed in Africa for several hundreds of years but was recently re-introduced during the period of Neo-imperialism. We also provide evidence of the role that the so-called “Scramble for Africa” played in the spread of the disease. INTRODUCTION The clonal population structure of M. ulcerans has meant that conventional genetic fingerprinting methods have largely failed to differentiate clinical disease isolates, complicating molecular analyses on the elucidation of the population structure, and the evolutionary history of the pathogen. Whole genome sequencing (WGS) is currently replacing conventional genotyping methods for M. ulcerans. METHODS We analyzed a panel of 165 M. ulcerans disease isolates originating from disease foci in 11 different African countries that had been cultured between 1964 and 2012. Index-tagged paired-end sequencing-ready libraries were prepared from gDNA extracts. Genome sequencing was performed on the Illumina HiSeq 2000 DNA sequencer or the Illumina MiSeq sequencing platform with respectively 2x150bp and 2x250bp paired-end sequencing chemistry. Read mapping and SNP detection were performed using the Snippy v.2.6 pipeline. Bayesian model-based inference of the genetic population structure was performed using BAPS v.6.0. 1 Evidence for recombination between different BAPS-clusters was assessed using BRAT- NextGen 2 . We used BEAST2 v2.2.1 3 to date evolutionary events, determine the substitution rate and produce a timetree of African M. ulcerans. A permutation test was used to assess the validity of the temporal signal in the data. To assess the geospatial distribution of African M. ulcerans through time, an additional BEAST2 analysis was performed with a discrete BSSVS geospatial model 4 . RESULTS & DISCUSSION Resulting sequence reads were mapped to the Ghanaian M. ulcerans Agy99 reference genome and, after excluding mobile repetitive elements and small indels, we detected a total of 9,193 SNPs randomly distributed across the M. ulcerans chromosome with approximately 1 SNP per 613 bp (0.15% nucleotide divergence). We explored the distribution of DNA chromosomal deletions and identified differential genome reduction that strongly supports the existence of two specific M. ulcerans lineages within the African continent, hereafter referred to as Lineage Africa I (Mu_A1) and Lineage Africa II (Mu_A2). Subsequent SNP-based exploration of the genetic population structure agreed with the above deletion analysis and subdivided the African M. ulcerans population into four major clusters. BRAT-NextGen did not detect any recombined segments in any isolate, supporting a strongly clonal population structure for M. ulcerans that is evolving by vertically inherited mutations. Within the phylogenetic tree, isolates formed tight, shallow-rooted phylogenetic clusters which are suggestive of contemporary dispersal. We estimated a very slow mean genome wide substitution rate of 6.32E-8 per site per year. The Bayesian analysis demonstrated that Mu_A1 has existed in Africa for several hundreds of years and that Mu_A2 was recently introduced on the continent. The re-introduction event coincides well with a historical event of particular interest: the period of Neo-imperialism (1881-1914). Since tMCRA(Mu_A2) did not predate colonization it seems very likely that lineage Mu_A2 was introduced after the instigation of colonial rule through an influx of BU infected humans. The time-tree of African M. ulcerans also reveals evidence of the likely role that the so-called “Scramble for Africa” played in the spread of endemic Mu_A1 clones in three hydrological basins (Congo, Oueme & Nyong) that are particularly well covered by our isolate panel. REFERENCES 1. Corander, J., et al. (2008) BMC bioinformatics. 9: p. 539. 2. Marttinen, P., et al. (2012) Nucleic acids research. 40(1): p. e6. 3. Bouckaert, R., et al. (2014) PLoS computational biology. 10(4): p. e1003537. 4. Lemey, P., et al., (2009) PLoS computational biology. 5(9): p. e1000520. 79
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