Meinicke, Peter

[["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.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.firstpage","1556"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","The Plant Cell"],["dc.bibliographiccitation.lastpage","1572"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Kataya, Amr R."],["dc.contributor.author","Antonicelli, Gerardo E."],["dc.contributor.author","Benichou, Aline"],["dc.contributor.author","Nilssen, Kjersti"],["dc.contributor.author","Chen, Xiong-Yan"],["dc.contributor.author","Siemsen, Tanja"],["dc.contributor.author","Morgenstern, Burkhard"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Reumann, Sigrun"],["dc.date.accessioned","2018-11-07T08:57:10Z"],["dc.date.available","2018-11-07T08:57:10Z"],["dc.date.issued","2011"],["dc.description.abstract","In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants."],["dc.identifier.doi","10.1105/tpc.111.084095"],["dc.identifier.isi","000291000500030"],["dc.identifier.pmid","21487095"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23328"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Soc Plant Biologists"],["dc.relation.issn","1040-4651"],["dc.title","Identification of Novel Plant Peroxisomal Targeting Signals by a Combination of Machine Learning Methods and in Vivo Subcellular Targeting Analyses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","973"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","980"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Klingenberg, Heiner"],["dc.contributor.author","Assauer, Kathrin Petra"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T09:25:59Z"],["dc.date.available","2018-11-07T09:25:59Z"],["dc.date.issued","2013"],["dc.description.abstract","Motivation: Metagenome analysis requires tools that can estimate the taxonomic abundances in anonymous sequence data over the whole range of biological entities. Because there is usually no prior knowledge about the data composition, not only all domains of life but also viruses have to be included in taxonomic profiling. Such a full-range approach, however, is difficult to realize owing to the limited coverage of available reference data. In particular, archaea and viruses are generally not well represented by current genome databases. Results: We introduce a novel approach to taxonomic profiling of metagenomes that is based on mixture model analysis of protein signatures. Our results on simulated and real data reveal the difficulties of the existing methods when measuring achaeal or viral abundances and show the overall good profiling performance of the protein-based mixture model. As an application example, we provide a large-scale analysis of data from the Human Microbiome Project. This demonstrates the utility of our method as a first instance profiling tool for a fast estimate of the community structure."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [ME 3138, LI 2050]"],["dc.identifier.doi","10.1093/bioinformatics/btt077"],["dc.identifier.isi","000318109300002"],["dc.identifier.pmid","23418187"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30193"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1367-4803"],["dc.title","Protein signature-based estimation of metagenomic abundances including all domains of life and viruses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["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","1618"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","1624"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Asshauer, Kathrin Petra"],["dc.contributor.author","Lingner, Thomas"],["dc.date.accessioned","2018-11-07T08:55:05Z"],["dc.date.available","2018-11-07T08:55:05Z"],["dc.date.issued","2011"],["dc.description.abstract","Motivation: Inferring the taxonomic profile of a microbial community from a large collection of anonymous DNA sequencing reads is a challenging task in metagenomics. Because existing methods for taxonomic profiling of metagenomes are all based on the assignment of fragmentary sequences to phylogenetic categories, the accuracy of results largely depends on fragment length. This dependence complicates comparative analysis of data originating from different sequencing platforms or resulting from different preprocessing pipelines. Results: We here introduce a new method for taxonomic profiling based on mixture modeling of the overall oligonucleotide distribution of a sample. Our results indicate that the mixture-based profiles compare well with taxonomic profiles obtained with other methods. However, in contrast to the existing methods, our approach shows a nearly constant profiling accuracy across all kinds of read lengths and it operates at an unrivaled speed."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [ME 3138, LI 2050]"],["dc.identifier.doi","10.1093/bioinformatics/btr266"],["dc.identifier.isi","000291261300004"],["dc.identifier.pmid","21546400"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/22821"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1367-4803"],["dc.title","Mixture models for analysis of the taxonomic composition of metagenomes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","259"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T11:14:05Z"],["dc.date.available","2018-11-07T11:14:05Z"],["dc.date.issued","2008"],["dc.description.abstract","Background: Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive. Results: In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection. Conclusion: Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biologically meaningful features. In particular, analysis of discriminative words allows the identification of characteristic regions in biological sequences. Because of its high computational efficiency, our method can be applied to ranking of potential homologs in large databases."],["dc.identifier.doi","10.1186/1471-2105-9-259"],["dc.identifier.isi","257159600001"],["dc.identifier.pmid","18522726"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8428"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/54043"],["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","Word correlation matrices for protein sequence analysis and remote homology detection"],["dc.title.original","8428"],["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","12364"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.lastpage","12378"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Asshauer, Kathrin Petra"],["dc.contributor.author","Klingenberg, Heiner"],["dc.contributor.author","Lingner, Thomas"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T09:38:01Z"],["dc.date.available","2018-11-07T09:38:01Z"],["dc.date.issued","2014"],["dc.description.abstract","The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.description.sponsorship","DFG [ME 3138]"],["dc.identifier.doi","10.3390/ijms150712364"],["dc.identifier.isi","000340038500072"],["dc.identifier.pmid","25026170"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10457"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32971"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Mdpi Ag"],["dc.relation.issn","1422-0067"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Exploring Neighborhoods in the Metagenome Universe"],["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"]]