http://dl.gi.de/handle/20.500.12116/21227 2026-07-30T00:47:47Z 2026-07-30T00:47:47Z Lingner, Thomas Meinicke, Peter http://dl.gi.de/handle/20.500.12116/3054 2019-04-03T12:29:13Z 2014-01-01T00:00:00Z

Characterizing metagenomic novelty with unexplained protein domain hits Lingner, Thomas; Meinicke, Peter Giegerich, Robert; Hofestädt, Ralf; Nattkemper, Tim W. In 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.

2014-01-01T00:00:00Z Rathke, Magnus Kölling, Jan Nattkemper, Tim W. http://dl.gi.de/handle/20.500.12116/3051 2019-04-03T12:29:13Z 2014-01-01T00:00:00Z

Interactive and dynamic web-based visual exploration of high dimensional bioimages with real time clustering Rathke, Magnus; Kölling, Jan; Nattkemper, Tim W. Giegerich, Robert; Hofestädt, Ralf; Nattkemper, Tim W. Web browsers and web applications have become common tools in bioinformatics over the past decades. Many existing web applications revolve around server-client interaction, where heavy computational tasks are often outsourced to the server and the presentation is handled on the the client-side. However more recent additions to the web browser technology embrace the capability of handling more complex operations on the client-side itself, cutting out most of the server-client interaction except for data loading. This paper contributes to the exploration of the potential of approaches to implement and speed up computational expensive tasks, like image cluster analysis, within a client-side web browser environment. The experimental results, incorporating the well known k-means algorithm which serves as a platform for various parallelization approaches, indicate the possibility to achieve real time image clustering. Especially for the available MALDI-MSI data set the results look promising. Despite good results of multithreading approaches, algorithmic approaches appear to be relevant too. Therefore advancements in accelerating the k-means algorithm itself are considered.

2014-01-01T00:00:00Z Sun, Peng Guo, Jiong Efficient, Jan Baumbach http://dl.gi.de/handle/20.500.12116/3052 2019-04-03T12:29:13Z 2014-01-01T00:00:00Z

Large-scale bicluster editing Sun, Peng; Guo, Jiong; Efficient, Jan Baumbach Giegerich, Robert; Hofestädt, Ralf; Nattkemper, Tim W. The 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

2014-01-01T00:00:00Z Zickmann, Franziska Lindner, Martin S. Renard, Bernhard Y. http://dl.gi.de/handle/20.500.12116/3049 2019-04-03T12:29:13Z 2014-01-01T00:00:00Z

RNA-seq driven gene identification Zickmann, Franziska; Lindner, Martin S.; Renard, Bernhard Y. Giegerich, Robert; Hofestädt, Ralf; Nattkemper, Tim W. The reliable identification of genes is a challenging and crucial part of genome research. Various methods aiming at accurate predictions have evolved that predict genes ab initio on reference sequences or evidence based with help of additional information. With high-throughput RNA-Seq data reflecting currently expressed genes, a particularly meaningful source of information has become commonly available. However, a particular challenge in including RNA-Seq data is the difficult handling of ambiguously mapped reads. Therefore we developed GIIRA, a novel gene finder that is exclusively based on RNA-Seq data and inherently includes ambiguously mapped reads. Evaluation on simulated and real data and comparison with existing methods incorporating RNA-Seq information highlight the accuracy of GIIRA in identifying the expressed genes. Further, we developed a framework to integrate GIIRA and other gene finders to obtain a verified and accurate set of gene predictions.

2014-01-01T00:00:00Z