Tech, Maike

[["dc.bibliographiccitation.firstpage","3568"],["dc.bibliographiccitation.issue","17"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","3569"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Tech, Maike"],["dc.contributor.author","Pfeifer, N."],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T10:55:56Z"],["dc.date.available","2018-11-07T10:55:56Z"],["dc.date.issued","2005"],["dc.description.abstract","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."],["dc.identifier.doi","10.1093/bioinformatics/bti563"],["dc.identifier.isi","000231472500017"],["dc.identifier.pmid","15994191"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/49897"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1367-4803"],["dc.title","TICO: a tool for improving predictions of prokaryotic translation initiation sites"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","7822"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Schmitt-Engel, Christian"],["dc.contributor.author","Schultheis, Dorothea"],["dc.contributor.author","Schwirz, Jonas"],["dc.contributor.author","Stroehlein, Nadi"],["dc.contributor.author","Troelenberg, Nicole"],["dc.contributor.author","Majumdar, Upalparna"],["dc.contributor.author","Dao, Van Anh"],["dc.contributor.author","Grossmann, Daniela"],["dc.contributor.author","Richter, Tobias"],["dc.contributor.author","Tech, Maike"],["dc.contributor.author","Doenitz, Juergen"],["dc.contributor.author","Gerischer, Lizzy"],["dc.contributor.author","Theis, Mirko"],["dc.contributor.author","Schild, Inga"],["dc.contributor.author","Trauner, Jochen"],["dc.contributor.author","Koniszewski, Nikolaus Dieter Bernhard"],["dc.contributor.author","Kuester, Elke"],["dc.contributor.author","Kittelmann, Sebastian"],["dc.contributor.author","Hu, Yonggang"],["dc.contributor.author","Lehmann, Sabrina"],["dc.contributor.author","Siemanowski, Janna L."],["dc.contributor.author","Ulrich, Julia"],["dc.contributor.author","Panfilio, Kristen A."],["dc.contributor.author","Schroeder, Reinhard"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Stanke, Mario"],["dc.contributor.author","Buchhholz, Frank"],["dc.contributor.author","Frasch, Manfred"],["dc.contributor.author","Roth, Siegfried"],["dc.contributor.author","Wimmer, Ernst A."],["dc.contributor.author","Schoppmeier, Michael"],["dc.contributor.author","Klingler, Martin"],["dc.contributor.author","Bucher, Gregor"],["dc.date.accessioned","2018-11-07T09:55:00Z"],["dc.date.available","2018-11-07T09:55:00Z"],["dc.date.issued","2015"],["dc.description.abstract","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. RNAi screens in other organisms promise to reduce this bias. Here we present the results of the iBeetle screen, a large-scale, unbiased RNAi screen in the red flour beetle, Tribolium castaneum, which identifies gene functions in embryonic and postembryonic development, physiology and cell biology. The utility of Tribolium as a screening platform is demonstrated by the identification of genes involved in insect epithelial adhesion. This work transcends the restrictions of the candidate gene approach and opens fields of research not accessible in Drosophila."],["dc.description.sponsorship","Open-Access Publikationsfonds 2015"],["dc.identifier.doi","10.1038/ncomms8822"],["dc.identifier.isi","000358860900002"],["dc.identifier.pmid","26215380"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12460"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36659"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","2041-1723"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","The iBeetle large-scale RNAi screen reveals gene functions for insect development and physiology"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","169"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Tech, Maike"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Merkl, R."],["dc.date.accessioned","2018-11-07T10:44:36Z"],["dc.date.available","2018-11-07T10:44:36Z"],["dc.date.issued","2004"],["dc.description.abstract","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. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon. Conclusions: We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems."],["dc.identifier.doi","10.1186/1471-2105-5-169"],["dc.identifier.isi","000226616700003"],["dc.identifier.pmid","15511290"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/4439"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/47306"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2105"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","217"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Hoff, Katharina J."],["dc.contributor.author","Tech, Maike"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Daniel, Rolf"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T11:15:57Z"],["dc.date.available","2018-11-07T11:15:57Z"],["dc.date.issued","2008"],["dc.description.abstract","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. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results: We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion: Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided ( see Availability and requirements section)."],["dc.identifier.doi","10.1186/1471-2105-9-217"],["dc.identifier.isi","000256421900002"],["dc.identifier.pmid","18442389"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8429"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/54482"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2105"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Gene prediction in metagenomic fragments: A large scale machine learning approach"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","W588"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","W590"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Tech, Maike"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T09:39:01Z"],["dc.date.available","2018-11-07T09:39:01Z"],["dc.date.issued","2006"],["dc.description.abstract","Exact localization of the translation initiation sites (TIS) in prokaryotic genomes is difficult to achieve using conventional gene finders. We recently introduced the program TICO for postprocessing TIS predictions based on a completely unsupervised learning algorithm. The program can be utilized through our web interface at http://tico.gobics.de/ and it is also freely available as a commandline version for Linux and Windows. The latest version of our program provides a tool for visualization of the resulting TIS model. Although the underlying method is not based on any specific assumptions about characteristic sequence features of prokaryotic TIS the prediction rates of our tool are competitive on experimentally verified test data."],["dc.identifier.doi","10.1093/nar/gkl313"],["dc.identifier.isi","000245650200117"],["dc.identifier.pmid","16845076"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4132"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33191"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","0305-1048"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","TICO: a tool for postprocessing the predictions of prokaryotic translation initiation sites"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]