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","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","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.artnumber","1003"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Landesfeind, Manuel"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T09:32:33Z"],["dc.date.available","2018-11-07T09:32:33Z"],["dc.date.issued","2014"],["dc.description.abstract","Background: The annotation of biomolecular functions is an essential step in the analysis of newly sequenced organisms. Usually, the functions are inferred from predicted genes on the genome using homology search techniques. A high quality genomic sequence is an important prerequisite which, however, is difficult to achieve for certain organisms, such as hybrids or organisms with a large genome. For functional analysis it is also possible to use a de novo transcriptome assembly but the computational requirements can be demanding. Up to now, it is unclear how much of the functional repertoire of an organism can be reliably predicted from unassembled RNA-seq short reads alone. Results: We have conducted a study to investigate to what degree it is possible to reconstruct the functional profile of an organism from unassembled transcriptome data. We simulated the de novo prediction of biomolecular functions for Arabidopsis thaliana using a comprehensive RNA-seq data set. We evaluated the prediction performance using several homology search methods in combination with different evidence measures. For the decision on the presence or absence of a particular function under noisy conditions we propose a statistical mixture model enabling unsupervised estimation of a detection threshold. Our results indicate that the prediction of the biomolecular functions from the KEGG database is possible with a high sensitivity up to 94 percent. In this setting, the application of the mixture model for automatic threshold calibration allowed the reduction of the falsely predicted functions down to 4 percent. Furthermore, we found that our statistical approach even outperforms the prediction from a de novo transcriptome assembly. Conclusion: The analysis of an organism's transcriptome can provide a solid basis for the prediction of biomolecular functions. Using RNA-seq short reads directly, the functional profile of an organism can be reconstructed in a computationally efficient way to provide a draft annotation in cases where the classical genome-based approaches cannot be applied."],["dc.identifier.doi","10.1186/1471-2164-15-1003"],["dc.identifier.isi","000345796300001"],["dc.identifier.pmid","25409897"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11178"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/31777"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2164"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Predicting the functional repertoire of an organism from unassembled RNA-seq 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.artnumber","481"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Muehlhausen, Stefanie"],["dc.contributor.author","Gabaldon, Toni"],["dc.contributor.author","Notredame, Cedric"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T08:39:06Z"],["dc.date.available","2018-11-07T08:39:06Z"],["dc.date.issued","2010"],["dc.description.abstract","Background: Establishing the relationship between an organism's genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel high-throughput and single cell sequencing techniques. Results: We present a novel approach to predict the phenotype of prokaryotes directly from their protein domain frequencies. Our discriminative machine learning approach provides high prediction accuracy of relevant phenotypes such as motility, oxygen requirement or spore formation. Moreover, the set of discriminative domains provides biological insight into the underlying phenotype-genotype relationship and enables deriving hypotheses on the possible functions of uncharacterized domains. Conclusions: Fast and accurate prediction of microbial phenotypes based on genomic protein domain content is feasible and has the potential to provide novel biological insights. First results of a systematic check for annotation errors indicate that our approach may also be applied to semi-automatic correction and completion of the existing phenotype annotation."],["dc.description.sponsorship","German Academic Exchange Service (DAAD)"],["dc.identifier.doi","10.1186/1471-2105-11-481"],["dc.identifier.isi","000283062400001"],["dc.identifier.pmid","20868492"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6017"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18909"],["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","Predicting phenotypic traits of prokaryotes from protein domain frequencies"],["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","e89297"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Kaever, Alexander"],["dc.contributor.author","Landesfeind, Manuel"],["dc.contributor.author","Feussner, Kirstin"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Feussner, Ivo"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T09:43:35Z"],["dc.date.available","2018-11-07T09:43:35Z"],["dc.date.issued","2014"],["dc.description.abstract","A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e. g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.identifier.doi","10.1371/journal.pone.0089297"],["dc.identifier.isi","000332396200047"],["dc.identifier.pmid","24586671"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9992"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34212"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets"],["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","409"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T11:24:24Z"],["dc.date.available","2018-11-07T11:24:24Z"],["dc.date.issued","2009"],["dc.description.abstract","Background: Functional profiling is a key technique to characterize and compare the functional potential of entire genomes. The estimation of profiles according to an assignment of sequences to functional categories is a computationally expensive task because it requires the comparison of all protein sequences from a genome with a usually large database of annotated sequences or sequence families. Description: Based on machine learning techniques for Pfam domain detection, the UFO web server for ultra-fast functional profiling allows researchers to process large protein sequence collections instantaneously. Besides the frequencies of Pfam and GO categories, the user also obtains the sequence specific assignments to Pfam domain families. In addition, a comparison with existing genomes provides dissimilarity scores with respect to 821 reference proteomes. Considering the underlying UFO domain detection, the results on 206 test genomes indicate a high sensitivity of the approach. In comparison with current state-of-the-art HMMs, the runtime measurements show a considerable speed up in the range of four orders of magnitude. For an average size prokaryotic genome, the computation of a functional profile together with its comparison typically requires about 10 seconds of processing time. Conclusion: For the first time the UFO web server makes it possible to get a quick overview on the functional inventory of newly sequenced organisms. The genome scale comparison with a large number of precomputed profiles allows a first guess about functionally related organisms. The service is freely available and does not require user registration or specification of a valid email address."],["dc.identifier.doi","10.1186/1471-2164-10-409"],["dc.identifier.isi","000270394400002"],["dc.identifier.pmid","19725959"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5851"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/56396"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2164"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","UFO: a web server for ultra-fast functional profiling of whole genome protein sequences"],["dc.type","review"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]