Publications by authors named "Maike Tech"

Genetic screens are powerful tools to identify the genes required for a given biological process. However, for technical reasons, comprehensive screens have been restricted to very few model organisms. Therefore, although deep sequencing is revealing the genes of ever more insect species, the functional studies predominantly focus on candidate genes previously identified in Drosophila, which is biasing research towards conserved gene functions.

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The iBeetle-Base (http://ibeetle-base.uni-goettingen.de) makes available annotations of RNAi phenotypes, which were gathered in a large scale RNAi screen in the red flour beetle Tribolium castaneum (iBeetle screen).

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Six transcription regulatory genes of the Verticillium plant pathogen, which reprogrammed nonadherent budding yeasts for adhesion, were isolated by a genetic screen to identify control elements for early plant infection. Verticillium transcription activator of adhesion Vta2 is highly conserved in filamentous fungi but not present in yeasts. The Magnaporthe grisea ortholog conidiation regulator Con7 controls the formation of appressoria which are absent in Verticillium species.

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Metagenomic sequencing projects yield numerous sequencing reads of a diverse range of uncultivated and mostly yet unknown microorganisms. In many cases, these sequencing reads cannot be assembled into longer contigs. Thus, gene prediction tools that were originally developed for whole-genome analysis are not suitable for processing metagenomes.

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Background: Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads.

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Background: Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species.

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Unlabelled: We provide the tool 'TICO' (Translation Initiation site COrrection) for improving the results of conventional gene finders for prokaryotic genomes with regard to exact localization of the translation initiation site (TIS). At the current state TICO provides an interface for direct post processing of the predictions obtained from the widely used program GLIMMER. Our program is based on a clustering algorithm for completely unsupervised scoring of potential TIS locations.

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Background: Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals.

Results: We propose a kernel-based approach to datamining on biological sequences.

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The performance of gene-predicting tools varies considerably if evaluated with respect to the parameters sensitivity and specificity or their capability to identify the correct start codon. We were interested to validate tools for gene prediction and to implement a metatool named YACOP, which combines existing tools and has a higher performance. YACOP parses and combines the output of the three gene-predicting systems Criticia, Glimmer and ZCURVE.

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