Tech, Maike

[["dc.bibliographiccitation.firstpage","W101"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","W105"],["dc.bibliographiccitation.volume","37"],["dc.contributor.author","Hoff, Katharina J."],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Tech, Maike"],["dc.date.accessioned","2018-11-07T08:28:27Z"],["dc.date.available","2018-11-07T08:28:27Z"],["dc.date.issued","2009"],["dc.description.abstract","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. Orphelia is a program for predicting genes in short DNA sequences that is available through a web server application (http://orphelia.gobics.de). Orphelia utilizes prediction models that were created with machine learning techniques on the basis of a wide range of annotated genomes. In contrast to other methods for metagenomic gene prediction, Orphelia has fragment length-specific prediction models for the two most popular sequencing techniques in metagenomics, chain termination sequencing and pyrosequencing. These models ensure highly specific gene predictions."],["dc.identifier.doi","10.1093/nar/gkp327"],["dc.identifier.isi","000267889100019"],["dc.identifier.pmid","19429689"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5949"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/16421"],["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","Orphelia: predicting genes in metagenomic sequencing reads"],["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","565"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","New Phytologist"],["dc.bibliographiccitation.lastpage","581"],["dc.bibliographiccitation.volume","202"],["dc.contributor.author","Van-Tuan Tran, Van-Tuan Tran"],["dc.contributor.author","Braus-Stromeyer, Susanna A."],["dc.contributor.author","Kusch, Harald"],["dc.contributor.author","Reusche, Michael"],["dc.contributor.author","Kaever, Alexander"],["dc.contributor.author","Kuehn, Anika"],["dc.contributor.author","Valerius, Oliver"],["dc.contributor.author","Landesfeind, Manuel"],["dc.contributor.author","Asshauer, Kathrin"],["dc.contributor.author","Tech, Maike"],["dc.contributor.author","Hoff, Katharina J."],["dc.contributor.author","Pena-Centeno, Tonatiuh"],["dc.contributor.author","Stanke, Mario"],["dc.contributor.author","Lipka, Volker"],["dc.contributor.author","Braus, Gerhard H."],["dc.date.accessioned","2018-11-07T09:41:36Z"],["dc.date.available","2018-11-07T09:41:36Z"],["dc.date.issued","2014"],["dc.description.abstract"," 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. Vta2 was analyzed by using genetics, cell biology, transcriptomics, secretome proteomics and plant pathogenicity assays. Nuclear Vta2 activates the expression of the adhesin-encoding yeast flocculin genes FLO1 and FLO11. Vta2 is required for fungal growth of Verticillium where it is a positive regulator of conidiation. Vta2 is mandatory for accurate timing and suppression of microsclerotia as resting structures. Vta2 controls expression of 270 transcripts, including 10 putative genes for adhesins and 57 for secreted proteins. Vta2 controls the level of 125 secreted proteins, including putative adhesins or effector molecules and a secreted catalase-peroxidase. Vta2 is a major regulator of fungal pathogenesis, and controls host-plant root infection and H2O2 detoxification.Verticillium impaired in Vta2 is unable to colonize plants and induce disease symptoms. Vta2 represents an interesting target for controlling the growth and development of these vascular pathogens."],["dc.identifier.doi","10.1111/nph.12671"],["dc.identifier.isi","000333060500027"],["dc.identifier.pmid","24433459"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33771"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1469-8137"],["dc.relation.issn","0028-646X"],["dc.title","Verticillium transcription activator of adhesion Vta2 suppresses microsclerotia formation and is required for systemic infection of plant roots"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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"]]