P235 - GCB 2014 - German Conference on Bioinformatics 2014
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- KonferenzbeitragTowards accurate transcription start site prediction: a modelling approach(German conference on bioinformatics 2014, 2014) Djordjevic, MarkoPromoter prediction in bacteria is a classical bioinformatics problem, where available methods for regulatory element detection exhibit a very high number of false positives. We here argue that accurate transcription start site (TSS) prediction is a complex problem, where available methods for sequence motif discovery are not in itself well adopted for solving the problem. We here instead propose that the problem requires integration of quantitative understanding of transcription initiation with careful description of promoter sequence specificity. We review evidence for this viewpoint based on our recent work, and discuss a current progress on accurate TSS detection on the example of sigma70 transcription start sites in E. coli.
- KonferenzbeitragFlexible database-assisted graphical representation of metabolic networks for model comparison and the display of experimental data(German conference on bioinformatics 2014, 2014) Tillack, Jana; Bende, Melanie; Rother, Michael; Scheer, Maurice; Ulas, Susanne; Schomburg, DietmarIntracellular processes in living organisms are described by metabolic models. A visualization of metabolic models assists interpretation of data or analyzing results. We introduce the visualization tool DaViMM creating personalized graphical representations of metabolic networks for model comparison or the display of measurements or analyzing results. The tool is coupled to a relational database containing graphical network properties like coordinates, which ensure an intuitive network layout. A combination of DaViMM, the graphical database, and available biochemical databases enables an automated creation of metabolic network maps. The flexibility of this combination is demonstrated with some application examples.
- KonferenzbeitragA general approach for discriminative de novo motif discovery from high-throughput data(German conference on bioinformatics 2014, 2014) Grau, Jan; Posch, Stefan; Grosse, Ivo; Keilwagen, JensHigh-throughput techniques like ChIP-seq, ChIP-exo, and protein binding microarrays (PBMs) demand for novel de novo motif discovery approaches that focus on accuracy and runtime on large data sets. While specialized algorithms have been designed for discovering motifs in in-vivo ChIP-seq/ChIP-exo or in in-vitro PBM data, none of these works equally well for all these high-throughput techniques. Here, we present Dimont, a general approach for fast and accurate de-novo motif discovery from high-throughput data, which achieves a competitive performance on both ChIP-seq and PBM data compared to recent approaches specifically designed for either technique. Hence, Dimont allows for investigating differences between in-vitro and in-vivo binding in an unbiased manner using a unified approach. For most transcription factors, Dimont discovers similar motifs from in-vivo and in-vitro data, but we also find notable exceptions. Scrutinizing the benefit of modeling dependencies between binding site positions, we find that more complex motif models often increase prediction performance and, hence, are a worthwhile field of research. Original paper: doi: 10.1093/nar/gkt831
- KonferenzbeitragLarge-scale bicluster editing(German conference on bioinformatics 2014, 2014) Sun, Peng; Guo, Jiong; Efficient, Jan BaumbachThe explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as simultaneous clustering or co-clustering, has been successfully utilized to discover local patterns in gene expression data and similar biomedical data types. Here, we contribute a new approach: Bi-Force. It is based on the weighted bicluster editing model, to perform biclustering on arbitrary sets of biological entities, given any kind of similarity function. We first evaluated the power of Bi-Force to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package. We then followed a biclustering evaluation protocol from a recent review paper from Eren et al. and compared Bi-Force against eight existing tools: FABIA, QUBIC, Cheng and Church, Plaid, Bimax, Spectral, xMOTIFS and ISA. To this end, a suite of synthetic data sets as well as nine large gene expression data sets from Gene Expression Omnibus were analyzed. All resulting biclusters were subsequently investigated by Gene Ontology enrichment analysis to evaluate their biological relevance. The distinct theoretical foundation of Bi-Force (bicluster editing) is more powerful than strict biclustering. We thus outperformed existing tools with Bi-Force at least when following the evaluation protocols from Eren et al.. Bi-Force is implemented in Java and integrated into the open source software package of BiCluE. The software as well as all used data sets are publicly available at
- KonferenzbeitragProtein family analysis at the domain-level(German conference on bioinformatics 2014, 2014) Terrapon, Nicolas; Moore, Andrew; Bornberg-Bauer, ErichThe analysis of protein domains has gained considerable attention over the last years. Many new insights on protein modular evolution, combined with improved domain detection, have paved the way for an integrated analysis of protein families from a domain-centric perspective. We recently released DoMosaics, a JAVA application that facilitates the interactive analysis of protein domain arrangements. DoMosaics combines guided domain annotation, a highly-customisable visualization of arrangements, and a number of analysis tools. It also integrates domain-centric algorithms such as CODD, which is used for the detection of divergent domain occurences that have escaped Pfam thresholds, as well as RADS/RAMPAGE which provides means to search for proteins with a domain arrangement similar to a given query. RADS provides an alignment of domain strings as opposed to amino-acid sequences, while RAMPAGE produces an amino-acid alignment guided by RADS results. Hence, RADS/RAMPAGE produces fast and yet accurate alignments, and associated ranking, of proteins with similar domain arrangements. Together, these tools greatly simplify the domain-centric analysis of protein function, structure and evolution.
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- KonferenzbeitragExplaining gene responses by linear modeling(German conference on bioinformatics 2014, 2014) Poeschl, Yvonne; Grosse, Ivo; Gogol-Döring, AndreasIncreasing our knowledge about molecular processes in response to a certain treatment or infection in plants, insects, or other organisms requires the identification of the genes involved in this response. In this paper, we propose the Profile Interaction Finder (PIF) to identify such genes from gene expression data which is based on a convex linear model, and we investigate its efficacy for two applications related to stimulus response. First, we seek to identify sets of putative regulatory genes that explain the expression levels of a gene under different stimuli best. Second, we aim at identifying genes that show a specific response to a stimulus or a combination of stimuli. For both applications, we study the expression response of two Arabidopsis species to treatment with the plant hormone auxin and of Apis mellifera to pathogen infection. The proposed approach may be of general utility for analyzing expression data with a focus on genes and gene sets that explain specific stimulus response.
- KonferenzbeitragBlockclust: efficient clustering and classification of non-coding rnas from short Read RNA-seq profiles(German conference on bioinformatics 2014, 2014) Videm, Pavankumar; Rose, Dominic; Costa, Fabrizio; Backofen, RolfSequence and secondary structure analysis can be used to assign putative functions to non-coding RNAs. However sequence information is changed by post-transcriptional modifications and secondary structure is only a proxy for the true 3D conformation of the RNA polymer. In order to tackle these issues we can extract a different type of description using the pattern of processing that can be observed through the traces left in small RNA-seq reads data. To obtain an efficient and scalable procedure, we propose to encode expression profiles in discrete structures, and process them using fast graph-kernel techniques.
- KonferenzbeitragOn the limits of computational functional genomics for bacterial lifestyle prediction(German conference on bioinformatics 2014, 2014) Barbosa, Eudes; Röttger, Richard; Hauschild, Anne-Christin; Azevedo, Vasco; Baumbach, JanWe review the level of genomic specificity regarding actinobacterial pathogenicity. As they occupy various niches in diverse habitats, one may assume the existence of lifestyle-specific genomic features. We include 240 actinobacteria classified into four pathogenicity classes: human pathogens (HP), broad-spectrum pathogens (BP), opportunistic pathogens (OP), and non-pathogenic (NP). We hypothesize: (H1) Pathogens (HPs and BPs) possess specific pathogenicity signature genes. (H2) The same holds for opportunistic pathogens. (H3) Broadspectrum and exclusively human pathogens cannot be distinguished from each other due to an observation bias, i.e. many HPs might be yet unclassified BPs. (H4) There is no intrinsic genomic characteristic of opportunistic pathogens compared to pathogens, as small mutations are likely to play a more dominant role in order to survive the immune system. To study these hypotheses, we implemented a bioinformatics pipeline that combines evolutionary sequence analysis with statistical learning methods (Random Forest with feature selection, model tuning and robustness analysis). Essentially, we present orthologous gene sets that computationally distinguish pathogens from non-pathogens (H1). We further show a clear limit in differentiating opportunistic pathogens from both, non-pathogens (H2) and pathogens (H4). Human pathogens may also not be distinguished from bacteria annotated as broad-spectrum pathogens based on a small set of orthologous genes only (H3), as many human pathogens might as well target a broad range of mammals but have not been annotated accordingly. In conclusion, we illustrate that even in the post-genome era and despite next-generation sequencing technology our ability to efficiError: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in 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We now wonder to what extend we can deduce real-world qualitative information from this treasure of data. The aim of this paper was to investigate the power of computational functional genomics to predict bacterial lifestyles utilizing DNA sequence information only. Specifically, we asked the question if we may utilize sequence similarity to identify dedicated lifestyle-specific protein-coding genes. We restrict our report, first, to a set of 240 well-studied actinobacterial genomes and, second, to four pathogenicity lifestyles, namely: human pathogens (HP), broad-spectrum pathogens (BP), opportunistic pathogens (OP), and non-pathogenic (NP). In [EB14], we illustrate and quantify the boundaries we face when trying to deduce a specific microbial pathogenicity class if given the genomic repertoire only, at least in the case of actinobacteria (see figure 1 below). In summary, we show that we indeed find signature genes that differentiate pathogens from non-pathogens. Only a small set of three genes for each bias, i.e. classification direction, is sufficient to reach an approximately 90\% accuracy (figures 2-4). When trying to classify the different pathogenicity lifestyles though, it appears that too many confounding factors unbalance our data sets such that we cannot differentiate, for instance, a strain-specific from a lifestyle-specific gene. We conclude that even in the post-genome era, and even for supposedly simple questions, our ability to efficiently deduce real-world implications from large-scale de novo next-generation sequencing data remains quite limited. 80 Figure 1: Illustration of our bias introduction strategy. Distribution of homologous gene clusters over two lifestyles (pathogens vs. non-pathogens) and illustration of our strategy to
- KonferenzbeitragCharacterizing metagenomic novelty with unexplained protein domain hits(German conference on bioinformatics 2014, 2014) Lingner, Thomas; Meinicke, PeterIn metagenomics, the discovery of functional novelty has always been pursued in a gene-centered manner. In that way, sequence-based analysis has been restricted to particular features and to a sufficient length of the sequences. We propose a statistical approach that is independent from the identification of single sequences but rather yields an overall characterization of a metagenome. Our method is based on the analysis of significant differences between the functional profile of a metagenome and its reconstruction from a combination of genomic profiles using the Taxy-Pro mixture model. Here, protein families with a large proportion of domain hits that cannot be explained by the model are interesting candidates for the exploration of metagenomic novelty. The results of three case studies indicate that our method is able to characterize metagenomic novelty in terms of the protein families that significantly contribute to unexplained domain counts. We found a good correspondence between our predictions and the discoveries in the original studies as well as specific indicators of functional novelty that have not yet been described.