Meinicke, Peter

[["dc.bibliographiccitation.artnumber","e3859"],["dc.bibliographiccitation.journal","PeerJ"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Klingenberg, Heiner"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2019-07-09T11:44:30Z"],["dc.date.available","2019-07-09T11:44:30Z"],["dc.date.issued","2017"],["dc.description.abstract","BACKGROUND: Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics. METHODS: We propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data under this model requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows us to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script. RESULTS: When applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data with an organism-independent (global) scaling of counts the resulting differences may be difficult to interpret. The differences may correspond to changing functional profiles of the contributing organisms but may also result from a variation of taxonomic abundances. Taxon-specific scaling eliminates this variation and therefore the resulting differences actually reflect a different behavior of organisms under changing conditions. In simulation studies we show that the divergence between results from global and taxon-specific scaling can be drastic. In particular, the variation of organism abundances can imply a considerable increase of significant differences with global scaling. Also, on real metatranscriptomic data, the predictions from taxon-specific and global scaling can differ widely. Our studies indicate that in real data applications performed with global scaling it might be impossible to distinguish between differential expression in terms of transcriptomic changes and differential composition in terms of changing taxonomic proportions. CONCLUSIONS: As in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore provides a clear interpretation of the observed functional differences."],["dc.description.abstract","BACKGROUND: Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics. METHODS: We propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data under this model requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows us to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script. RESULTS: When applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data with an organism-independent (global) scaling of counts the resulting differences may be difficult to interpret. The differences may correspond to changing functional profiles of the contributing organisms but may also result from a variation of taxonomic abundances. Taxon-specific scaling eliminates this variation and therefore the resulting differences actually reflect a different behavior of organisms under changing conditions. In simulation studies we show that the divergence between results from global and taxon-specific scaling can be drastic. In particular, the variation of organism abundances can imply a considerable increase of significant differences with global scaling. Also, on real metatranscriptomic data, the predictions from taxon-specific and global scaling can differ widely. Our studies indicate that in real data applications performed with global scaling it might be impossible to distinguish between differential expression in terms of transcriptomic changes and differential composition in terms of changing taxonomic proportions. CONCLUSIONS: As in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore provides a clear interpretation of the observed functional differences."],["dc.identifier.doi","10.7717/peerj.3859"],["dc.identifier.pmid","29062598"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14808"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59028"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2167-8359"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","570"],["dc.title","How to normalize metatranscriptomic count data for differential expression analysis."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","14"],["dc.bibliographiccitation.journal","Environmental and Experimental Botany"],["dc.bibliographiccitation.lastpage","22"],["dc.bibliographiccitation.volume","108"],["dc.contributor.author","Hoppenau, Clara E."],["dc.contributor.author","Tran, Van-Tuan"],["dc.contributor.author","Kusch, Harald"],["dc.contributor.author","Aßhauer, Kathrin P."],["dc.contributor.author","Landesfeind, Manuel"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Popova, Blagovesta"],["dc.contributor.author","Braus-Stromeyer, Susanna A."],["dc.contributor.author","Braus, Gerhard H."],["dc.date.accessioned","2018-04-24T15:04:17Z"],["dc.date.available","2018-04-24T15:04:17Z"],["dc.date.issued","2014"],["dc.description.abstract","The vascular plant pathogen Verticillium dahliae colonizes the xylem fluid where only low nutrient concentrations are provided. Biosynthesis of the vitamin thiamine is connected to oxidative stress. The highly conserved VdThi4 protein is localized in fungal mitochondria and is required under vitamin B1 limiting conditions. Deletion of the corresponding VdTHI4 gene by Agrobacterium-mediated transformation resulted in strains which were impaired in growth on thiamine-free medium and could be rescued by additional vitamin supply or by complementation with the original gene after protoplastation. Furthermore, we show that VdThi4 increases fungal stress tolerance such as UV-damage or oxidative stress. The orthologous sti35 gene of Fusarium oxysporum, another vascular wilt fungus, was shown to be involved in stress response, however to be dispensable for pathogenicity on tomato. In contrast, VdTHI4 is required for fungal-induced tomato disease demonstrated by infection assays with a V. dahliae ΔVdTHI4 deletion strain which is still able to invade plants through the roots but is asymptomatic. Our results suggest remarkable differences between two vascular tomato pathogens where VdThi4 is required for pathogenicity of V. dahliae, whereas F. oxysporum still causes disease when the corresponding Sti35 protein is absent."],["dc.description.sponsorship","Federal Ministry of Education and Research (BMBF)"],["dc.description.sponsorship","Cluster of Excellence and DFG Research Center Nanoscale Microscopy and Molecular Physiology of the Brain"],["dc.identifier.doi","10.1016/j.envexpbot.2013.12.015"],["dc.identifier.other","http://www.sciencedirect.com/science/article/pii/S0098847213002268"],["dc.identifier.pii","S0098847213002268"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13764"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.notes.status","zu prüfen"],["dc.relation.issn","0098-8472"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Verticillium dahliae VdTHI4, involved in thiazole biosynthesis, stress response and DNA repair functions, is required for vascular disease induction in tomato"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1935"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Ecology and Evolution"],["dc.bibliographiccitation.lastpage","1948"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Danielsen, Lara"],["dc.contributor.author","Thürmer, A."],["dc.contributor.author","Meinicke, P."],["dc.contributor.author","Buée, Marc"],["dc.contributor.author","Morin, E."],["dc.contributor.author","Martin, Francis"],["dc.contributor.author","Pilate, Gilles"],["dc.contributor.author","Daniel, Rolf"],["dc.contributor.author","Polle, Andrea"],["dc.contributor.author","Reich, M."],["dc.date.accessioned","2017-09-07T11:49:14Z"],["dc.date.available","2017-09-07T11:49:14Z"],["dc.date.issued","2012"],["dc.description.abstract","Fungal communities play a key role in ecosystem functioning. However, only little is known about their composition in plant roots and the soil of biomass plantations. The goal of this study was to analyze fungal biodiversity in their belowground habitats and to gain information on the strategies by which ectomycorrhizal (ECM) fungi form colonies. In a 2‐year‐old plantation, fungal communities in the soil and roots of three different poplar genotypes (Populus × canescens, wildtype and two transgenic lines with suppressed cinnamyl alcohol dehydrogenase activity) were analyzed by 454 pyrosequencing targeting the rDNA internal transcribed spacer 1 (ITS) region. The results were compared with the dynamics of the root‐associated ECM community studied by morphotyping/Sanger sequencing in two subsequent years. Fungal species and family richness in the soil were surprisingly high in this simple plantation ecosystem, with 5944 operational taxonomic units (OTUs) and 186 described fungal families. These findings indicate the importance that fungal species are already available for colonization of plant roots (2399 OTUs and 115 families). The transgenic modification of poplar plants had no influence on fungal root or soil communities. Fungal families and OTUs were more evenly distributed in the soil than in roots, probably as a result of soil plowing before the establishment of the plantation. Saprophytic, pathogenic, and endophytic fungi were the dominating groups in soil, whereas ECMs were dominant in roots (87%). Arbuscular mycorrhizal diversity was higher in soil than in roots. Species richness of the root‐associated ECM community, which was low compared with ECM fungi detected by 454 analyses, increased after 1 year. This increase was mainly caused by ECM fungal species already traced in the preceding year in roots. This result supports the priority concept that ECMs present on roots have a competitive advantage over soil‐localized ECM fungi."],["dc.identifier.doi","10.1002/ece3.305"],["dc.identifier.gro","3147227"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9771"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4859"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","2045-7758"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Fungal soil communities in a young transgenic poplar plantation form a rich reservoir for fungal root communities"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","263910"],["dc.bibliographiccitation.journal","JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY"],["dc.contributor.author","Kaever, Alexander"],["dc.contributor.author","Landesfeind, Manuel"],["dc.contributor.author","Possienke, Mareike"],["dc.contributor.author","Feussner, Kirstin"],["dc.contributor.author","Feussner, Ivo"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T09:15:23Z"],["dc.date.available","2018-11-07T09:15:23Z"],["dc.date.issued","2012"],["dc.description.abstract","Statistical ranking, filtering, adduct detection, isotope correction, and molecular formula calculation are essential tasks in processing mass spectrometry data in metabolomics studies. In order to obtain high-quality data sets, a framework which incorporates all these methods is required. We present the MarVis-Filter software, which provides well-established and specialized methods for processing mass spectrometry data. For the task of ranking and filtering multivariate intensity profiles, MarVis-Filter provides the ANOVA and Kruskal-Wallis tests with adjustment for multiple hypothesis testing. Adduct and isotope correction are based on a novel algorithm which takes the similarity of intensity profiles into account and allows user-defined ionization rules. The molecular formula calculation utilizes the results of the adduct and isotope correction. For a comprehensive analysis, MarVis-Filter provides an interactive interface to combine data sets deriving from positive and negative ionization mode. The software is exemplarily applied in a metabolic case study, where octadecanoids could be identified as markers for wounding in plants."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2012"],["dc.identifier.doi","10.1155/2012/263910"],["dc.identifier.isi","000303726300001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7716"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27674"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Hindawi Publishing Corporation"],["dc.relation.issn","1110-7243"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","MarVis-Filter: Ranking, Filtering, Adduct and Isotope Correction of Mass Spectrometry Data"],["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","W19"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","W22"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Subramanian, Amarendran R."],["dc.contributor.author","Hiran, Suvrat"],["dc.contributor.author","Steinkamp, Rasmus"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Corel, Eduardo"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.date.accessioned","2018-11-07T08:41:57Z"],["dc.date.available","2018-11-07T08:41:57Z"],["dc.date.issued","2010"],["dc.description.abstract","We introduce web interfaces for two recent extensions of the multiple-alignment program DIALIGN. DIALIGN-TX combines the greedy heuristic previously used in DIALIGN with a more traditional 'progressive' approach for improved performance on locally and globally related sequence sets. In addition, we offer a version of DIALIGN that uses predicted protein secondary structures together with primary sequence information to construct multiple protein alignments. Both programs are available through 'Gottingen Bioinformatics Compute Server' (GOBICS)."],["dc.identifier.doi","10.1093/nar/gkq442"],["dc.identifier.isi","000284148900004"],["dc.identifier.pmid","20497995"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7256"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19585"],["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","DIALIGN-TX and multiple protein alignment using secondary structure information at GOBICS"],["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","92"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Kaever, Alexander"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Feussner, Kirstin"],["dc.contributor.author","Goebel, Cornelia"],["dc.contributor.author","Feussner, Ivo"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T08:31:37Z"],["dc.date.available","2018-11-07T08:31:37Z"],["dc.date.issued","2009"],["dc.description.abstract","Background: A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters. Results: We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data. Conclusion: MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well."],["dc.identifier.doi","10.1186/1471-2105-10-92"],["dc.identifier.isi","000265607300001"],["dc.identifier.pmid","19302701"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5796"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/17162"],["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","MarVis: a tool for clustering and visualization of metabolic biomarkers"],["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","1073"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","1076"],["dc.bibliographiccitation.volume","51"],["dc.contributor.author","Kaper, M."],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Grossekathoefer, U."],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Ritter, H."],["dc.date.accessioned","2018-11-07T10:48:28Z"],["dc.date.available","2018-11-07T10:48:28Z"],["dc.date.issued","2004"],["dc.description.abstract","We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing."],["dc.identifier.doi","10.1109/TBME.2004.826698"],["dc.identifier.isi","000221578000029"],["dc.identifier.pmid","15188881"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/48199"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","0018-9294"],["dc.title","BCI competition 2003 - Data set IIb: Support vector machines for the P300 speller paradigm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1063"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Nature Methods"],["dc.bibliographiccitation.lastpage","1071"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Sczyrba, Alexander"],["dc.contributor.author","Hofmann, Peter"],["dc.contributor.author","Belmann, Peter"],["dc.contributor.author","Koslicki, David"],["dc.contributor.author","Janssen, Stefan"],["dc.contributor.author","Dröge, Johannes"],["dc.contributor.author","Gregor, Ivan"],["dc.contributor.author","Majda, Stephan"],["dc.contributor.author","Fiedler, Jessika"],["dc.contributor.author","Dahms, Eik"],["dc.contributor.author","Bremges, Andreas"],["dc.contributor.author","Fritz, Adrian"],["dc.contributor.author","Garrido-Oter, Ruben"],["dc.contributor.author","Jørgensen, Tue Sparholt"],["dc.contributor.author","Shapiro, Nicole"],["dc.contributor.author","Blood, Philip D"],["dc.contributor.author","Gurevich, Alexey"],["dc.contributor.author","Bai, Yang"],["dc.contributor.author","Turaev, Dmitrij"],["dc.contributor.author","DeMaere, Matthew Z"],["dc.contributor.author","Chikhi, Rayan"],["dc.contributor.author","Nagarajan, Niranjan"],["dc.contributor.author","Quince, Christopher"],["dc.contributor.author","Meyer, Fernando"],["dc.contributor.author","Balvočiūtė, Monika"],["dc.contributor.author","Hansen, Lars Hestbjerg"],["dc.contributor.author","Sørensen, Søren J"],["dc.contributor.author","Chia, Burton K H"],["dc.contributor.author","Denis, Bertrand"],["dc.contributor.author","Froula, Jeff L"],["dc.contributor.author","Wang, Zhong"],["dc.contributor.author","Egan, Robert"],["dc.contributor.author","Don Kang, Dongwan"],["dc.contributor.author","Cook, Jeffrey J"],["dc.contributor.author","Deltel, Charles"],["dc.contributor.author","Beckstette, Michael"],["dc.contributor.author","Lemaitre, Claire"],["dc.contributor.author","Peterlongo, Pierre"],["dc.contributor.author","Rizk, Guillaume"],["dc.contributor.author","Lavenier, Dominique"],["dc.contributor.author","Wu, Yu-Wei"],["dc.contributor.author","Singer, Steven W"],["dc.contributor.author","Jain, Chirag"],["dc.contributor.author","Strous, Marc"],["dc.contributor.author","Klingenberg, Heiner"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Barton, Michael D"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Lin, Hsin-Hung"],["dc.contributor.author","Liao, Yu-Chieh"],["dc.contributor.author","Silva, Genivaldo Gueiros Z"],["dc.contributor.author","Cuevas, Daniel A"],["dc.contributor.author","Edwards, Robert A"],["dc.contributor.author","Saha, Surya"],["dc.contributor.author","Piro, Vitor C"],["dc.contributor.author","Renard, Bernhard Y"],["dc.contributor.author","Pop, Mihai"],["dc.contributor.author","Klenk, Hans-Peter"],["dc.contributor.author","Göker, Markus"],["dc.contributor.author","Kyrpides, Nikos C"],["dc.contributor.author","Woyke, Tanja"],["dc.contributor.author","Vorholt, Julia A"],["dc.contributor.author","Schulze-Lefert, Paul"],["dc.contributor.author","Rubin, Edward M"],["dc.contributor.author","Darling, Aaron E"],["dc.contributor.author","Rattei, Thomas"],["dc.contributor.author","McHardy, Alice C"],["dc.date.accessioned","2020-12-10T18:09:31Z"],["dc.date.available","2020-12-10T18:09:31Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1038/nmeth.4458"],["dc.identifier.eissn","1548-7105"],["dc.identifier.issn","1548-7091"],["dc.identifier.pii","BFnmeth4458"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16720"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73676"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110494"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1548-7105"],["dc.relation.issn","1548-7091"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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.firstpage","919"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Microbial Ecology"],["dc.bibliographiccitation.lastpage","930"],["dc.bibliographiccitation.volume","67"],["dc.contributor.author","Nacke, Heiko"],["dc.contributor.author","Fischer, Christiane"],["dc.contributor.author","Thuermer, Andrea"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Daniel, Rolf"],["dc.date.accessioned","2018-11-07T09:41:00Z"],["dc.date.available","2018-11-07T09:41:00Z"],["dc.date.issued","2014"],["dc.description.abstract","Soil microorganisms play an essential role in sustaining biogeochemical processes and cycling of nutrients across different land use types. To gain insights into microbial gene transcription in forest and grassland soil, we isolated mRNA from 32 sampling sites. After sequencing of generated complementary DNA (cDNA), a total of 5,824,229 sequences could be further analyzed. We were able to assign nonribosomal cDNA sequences to all three domains of life. A dominance of bacterial sequences, which were affiliated to 25 different phyla, was found. Bacterial groups capable of aromatic compound degradation such as Phenylobacterium and Burkholderia were detected in significantly higher relative abundance in forest soil than in grassland soil. Accordingly, KEGG pathway categories related to degradation of aromatic ring-containing molecules (e.g., benzoate degradation) were identified in high abundance within forest soil-derived metatranscriptomic datasets. The impact of land use type forest on community composition and activity is evidently to a high degree caused by the presence of wood breakdown products. Correspondingly, bacterial groups known to be involved in lignin degradation and containing ligninolytic genes such as Burkholderia, Bradyrhizobium, and Azospirillum exhibited increased transcriptional activity in forest soil. Higher solar radiation in grassland presumably induced increased transcription of photosynthesis-related genes within this land use type. This is in accordance with high abundance of photosynthetic organisms and plant-infecting viruses in grassland."],["dc.identifier.doi","10.1007/s00248-014-0377-6"],["dc.identifier.isi","000334495000019"],["dc.identifier.pmid","24553913"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33623"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1432-184X"],["dc.relation.issn","0095-3628"],["dc.title","Land Use Type Significantly Affects Microbial Gene Transcription in Soil"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]