P136 - GCB 2008 - German Conference on Bioinformatics 2008
Auflistung P136 - GCB 2008 - German Conference on Bioinformatics 2008 nach Erscheinungsdatum
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- KonferenzbeitragStatistical detection of co-operative transcription factors with similarity adjustment(German Conference on Bioinformatics, 2008) Pape, Utz J.; Klein, Holger; Vingron, MartinStatistical assessment of cis-regulatory modules (CRMs) is a crucial task in computational biology. Usually, one concludes from exceptional co-occurrences of DNA motifs that the corresponding transcription factors are co-operative. However, similar DNA motifs tend to co-occur in random sequences due to high probability of overlapping occurrences. Therefore, it is important to consider similarity of DNA motifs in the statistical assessment. Based on previous work, we propose to adjust the window size for co-occurrence detection. Using the derived approximation, one obtains different window sizes for different sets of DNA motifs depending on their similarities. This ensures that the probability of co-occurrences in random sequences are equal. Applying the approach to selected similar and dissimilar DNA motifs from human transcription factors shows the necessity of adjustment and confirms the accu- racy of the approximation. Our previously published statistics can only deal with non-overlapping windows. Therefore, we extend the approach and derive Chen-Stein error bounds for the approxi- mation. Comparing the error bounds for similar and dissimilar DNA motifs shows that the approximation for similar DNA motifs yields large bounds. Hence, one has to be careful using overlapping windows. Based on the error bounds, one can pre-compute the approximation errors and select an appropriate overlap-scheme before running the analysis. Software and source code are available at http://mosta.molgen.mpg.de.
- KonferenzbeitragA Comparative Study of Robust Feature Detectors for 2D Electrophoresis Gel Image Registration(German Conference on Bioinformatics, 2008) Möller, Birgit; Greß, Oliver; Posch, StefanIn this study we consider the performance of different feature detectors used as the basis for the registration of images from two-dimensional gel electrophoresis. These are three spot detectors also used to identify proteins, and two domain inde- pendent keypoint detectors. We conduct a case study with images from a publically available data set which are synthetically distorted using thin plate splines. The per- formance is assessed by the repeatability score, the probability of an image structure to be detected in original and distorted images with reasonable localization accuracy.
- KonferenzbeitragRegistration to a neuroanatomical reference atlas - identifying glomeruli in optical recordings of the honeybee brain(German Conference on Bioinformatics, 2008) Strauch, Martin; Galizia, C. GiovanniAn odorant stimulus given to a bee elicits a characteristic combinatorial pattern of activity in neuronal units called glomeruli. These patterns can be measured by optical imaging, however detecting and identifying the glomeruli is a laborious task and prone to errors. Here, we present an image analysis pipeline for the automatic detection and identification of glomeruli. It involves Independent Component Analysis (ICA) to detect glomeruli in CCD camera data, a filtering step to exclude non- glomerulus objects and a graph-matching approach to find the best projection of the observed brain region onto a reference atlas. We evaluate our method against a manual glomerulus identification performed by a human expert and show that we achieve reliable results. Employing our method, we are now able to screen multiple recordings with the same accuracy, yielding a homogeneous collection of glomerulus identity mappings. These will subsequently be used to extract activity patterns that can be compared between individuals.
- KonferenzbeitragFRANz: Fast reconstruction of wild pedigrees(German Conference on Bioinformatics, 2008) Riester, Markus; Peter F., Stadler; Klemm, KonstantinWe present a software package for fast pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatellites and SNPs. If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by a simulation of the sampling process. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical data set with known pedigree. The parentage inference is robust even in the presence of genotyping errors.
- KonferenzbeitragConsecutive KEGG pathway models for the interpretation of high-throughput genomics data(German Conference on Bioinformatics, 2008) Antonov, Alexey V.; Diemann, Sabine; Mewes, Han W.A common strategy to deal with the interpretation of gene lists is to look for overrepresentation of Gene Ontology (GO) terms or pathways. In related computational approaches the cell is formalized as genes that are grouped into functional categories. As output, a list of interesting biological processes is provided, which seems to be mostly covered by the supplied gene list. However, it is more natural to model the cell as a network that reflects relations between genes. For many biological processes such information is available, but it is not used to the full extent in interpretational analyses. In this paper, we propose to interpret gene lists in network terms to provide the most probable scenario of gene interactions based on the available information about the topology of metabolic pathways. The proposed approach is an effort to exploit the biological information available in public resources to a greater extent in comparison to the existing techniques. Applying our approach to experimental data, we demonstrate that the currently widely employed strategy produces an incomplete interpretation, whilst our procedure provides deeper insights into possible molecular mechanisms behind the experimental data.
- KonferenzbeitragA Propagation-based Algorithm for Inferring Gene-Disease Associations(German Conference on Bioinformatics, 2008) Vanunu, Oron; Sharan, RodedA fundamental challenge in human health is the identification of disease- causing genes. Recently, several studies have tackled this challenge via a two-step approach: first, a linkage interval is inferred from population studies; second, a computational approach is used to prioritize genes within this interval. State-of-the-art methods for the latter task are based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process. Here we provide a global, network-based method for prioritizing disease genes. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. A propagation-based method is used to compute a function satisfying the constraints. We test our method on gene-disease association data in a cross-validation setting, and compare it to extant prioritization approaches. We show that our method provides the best overall performance, ranking the true causal gene first for 29% of the 1,369 diseases with a known gene in the OMIM knowledgebase.
- KonferenzbeitragStructure Local Multiple Alignment of RNA(German Conference on Bioinformatics, 2008) Otto, Wolfgang; Will, Sebastian; Backofen, RolfToday, RNA is well known to perform important regulatory and catalytic function due to its distinguished structure. Consequently, state-of-the-art RNA multiple alignment algorithms consider structure as well as sequence information. However, existing tools neglect the important aspect of locality. Notably, locality in RNA occurs as similarity of subsequences as well as similarity of only substructures. We present a novel approach for multiple alignment of RNAs that deals with both kinds of locality. The approach extends LocARNA by structural locality for computing all- against-all pairwise, structural local alignments. The final construction of the multiple alignments from the pairwise ones is delegated to T-Coffee. The paper systematically investigates structural locality in known RNA families. Benchmarking multiple align- ment tools on structural local families shows the need for algorithmic support of this locality. The improvement in accuracy in special cases is achieved while staying competitive with state-of-the-art alignment tools across the whole Bralibase. LocARNA and its T-Coffee extended variant LocARNATE are freely available at http://www.bioinf.uni-freiburg.de/Software/LocARNA/.
- KonferenzbeitragResqMi - a versatile algorithm and software for Resequencing Microarrays(German Conference on Bioinformatics, 2008) Symons, Stephan; Weber, Kirstin; Bonin, Michael; Nieselt, KayResequencing microarrays are a common tool for fast monitoring of individual genetic variations. Applications include diagnosis of genetic and infectious diseases and SNP prediction. Base calling is the crucial step in the analysis of resequencing data. All current base calling algorithms produce ambiguous calls on parts of the sequence. Therefore, proper data handling, editing and visualization as well as revised calling algorithms are generally necessary for successful data interpretation. We present a base calling algorithm that uses a model-based approach using intensity comparisons and region-wise conformance assessment, as well as an algorithm to revise uncalled positions. The calling algorithm is shown to have call rates comparable to ABACUS, the currently most commonly used method. Both algorithms combined however can considerably increase the calling rate. We also present a new open source software called ResqMi, short for Resequencing using Microarrays, which focuses on the efficient and user-friendly analysis, visual inspection and easy manual editing of resequencing microarray data. Both algorithms are implemented as plugins for ResqMi. ResqMi is available at http://www-ps.informatik.uni-tuebingen.de/resqmi
- KonferenzbeitragExploring the Enzyme Neighbourhood to interpret gene expression data(German Conference on Bioinformatics, 2008) Goffard, Nicolas; Frickey, Tancred; Imin, Nijat; Weiller, GeorgPost-genomic data analysis represents a new challenge to link and interpret the vast amount of raw data obtained with transcriptomic or proteomic techniques in the context of metabolic pathways. We propose a new strategy with the help of a metabolic network graph to extend PathExpress, a web-based tool to interpret gene expression data, without being restricted to predefined pathways. We defined the Enzyme Neighbourhood as groups of linked enzymes, corresponding to a sub-network, to explore the metabolic network in order to identify the most relevant sub-networks affected in gene expression experiments.
- KonferenzbeitragEvolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules(German Conference on Bioinformatics, 2008) Fober, Thomas; Hüllermeier, Eyke; Mernberger, MarcoThe concept of multiple graph alignment has recently been introduced as a novel method for the structural analysis of biomolecules. Using inexact, approximate graph-matching techniques, this method enables the robust identification of approximately conserved patterns in biologically related structures. In particular, multiple graph alignments enable the characterization of functional protein families independent of sequence or fold homology. This paper first recalls the concept of multiple graph alignment and then addresses the problem of computing optimal alignments from an algorithmic point of view. In this regard, a method from the field of evolutionary algorithms is proposed and empirically compared to a hitherto existing greedy strategy. Empirically, it is shown that the former yields significantly better results than the latter, albeit at the cost of an increased runtime.