GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Easy efficient HDR-based targeted knock-in in Saccharomyces cerevisiae genome using CRISPR-Cas9 system
    Informa UK Limited ; 2022
    In:  Bioengineered Vol. 13, No. 6 ( 2022-06-01), p. 14857-14871
    Details
    In: Bioengineered, Informa UK Limited, Vol. 13, No. 6 ( 2022-06-01), p. 14857-14871
    Type of Medium: Online Resource
    ISSN: 2165-5979 , 2165-5987
    URL: Article
    DOI: 10.1080/21655979.2022.2162667
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2737830-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19
    MDPI AG ; 2021
    In:  Computation Vol. 9, No. 1 ( 2021-01-07), p. 4-
    Details
    In: Computation, MDPI AG, Vol. 9, No. 1 ( 2021-01-07), p. 4-
    Abstract: The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the epidemic, one problem is to determine which factors play an important role in a patient’s progression of the disease. Here, we construct an enhanced COVID-19 structured dataset from more than one source, using natural language processing to add local weather conditions and country-specific research sentiment. The enhanced structured dataset contains 301,363 samples and 43 features, and we applied both machine learning algorithms and deep learning algorithms on it so as to forecast patient’s survival probability. In addition, we import alignment sequence data to improve the performance of the model. Application of Extreme Gradient Boosting (XGBoost) on the enhanced structured dataset achieves 97% accuracy in predicting patient’s survival; with climatic factors, and then age, showing the most importance. Similarly, the application of a Multi-Layer Perceptron (MLP) achieves 98% accuracy. This work suggests that enhancing the available data, mostly basic information on patients, so as to include additional, potentially important features, such as weather conditions, is useful. The explored models suggest that textual weather descriptions can improve outcome forecast.
    Type of Medium: Online Resource
    ISSN: 2079-3197
    URL: Article
    DOI: 10.3390/computation9010004
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2723192-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome
    Oxford University Press (OUP) ; 2022
    In:  Bioinformatics Vol. 38, No. 20 ( 2022-10-14), p. 4670-4676
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 20 ( 2022-10-14), p. 4670-4676
    Abstract: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a ‘theater of activity’ (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. Results Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. Availability and implementation Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btac584
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    MeganServer: facilitating interactive access to metagenomic data on a server
    Oxford University Press (OUP) ; 2023
    In:  Bioinformatics Vol. 39, No. 3 ( 2023-03-01)
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 3 ( 2023-03-01)
    Abstract: Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. Availability and implementation MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btad105
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1468345-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Table_8.XLSX
    Frontiers Media SA ; 2021
    In:  Frontiers in Microbiology Vol. 12 ( 2021-6-25)
    Details
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 12 ( 2021-6-25)
    Abstract: Sulfolobaceae family, comprising diverse thermoacidophilic and aerobic sulfur-metabolizing Archaea from various geographical locations, offers an ideal opportunity to infer the evolutionary dynamics across the members of this family. Comparative pan-genomics coupled with evolutionary analyses has revealed asymmetric genome evolution within the Sulfolobaceae family. The trend of genome streamlining followed by periods of differential gene gains resulted in an overall genome expansion in some species of this family, whereas there was reduction in others. Among the core genes, both Sulfolobus islandicus and Saccharolobus solfataricus showed a considerable fraction of positively selected genes and also higher frequencies of gene acquisition. In contrast, Sulfolobus acidocaldarius genomes experienced substantial amount of gene loss and strong purifying selection as manifested by relatively lower genome size and higher genome conservation. Central carbohydrate metabolism and sulfur metabolism coevolved with the genome diversification pattern of this archaeal family. The autotrophic CO 2 fixation with three significant positively selected enzymes from S. islandicus and S. solfataricus was found to be more imperative than heterotrophic CO 2 fixation for Sulfolobaceae . Overall, our analysis provides an insight into the interplay of various genomic adaptation strategies including gene gain–loss, mutation, and selection influencing genome diversification of Sulfolobaceae at various taxonomic levels and geographical locations.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fmicb.2021.639995
    DOI: 10.3389/fmicb.2021.639995.s001
    DOI: 10.3389/fmicb.2021.639995.s002
    DOI: 10.3389/fmicb.2021.639995.s003
    DOI: 10.3389/fmicb.2021.639995.s004
    DOI: 10.3389/fmicb.2021.639995.s005
    DOI: 10.3389/fmicb.2021.639995.s006
    DOI: 10.3389/fmicb.2021.639995.s007
    DOI: 10.3389/fmicb.2021.639995.s008
    DOI: 10.3389/fmicb.2021.639995.s009
    DOI: 10.3389/fmicb.2021.639995.s010
    DOI: 10.3389/fmicb.2021.639995.s011
    DOI: 10.3389/fmicb.2021.639995.s012
    DOI: 10.3389/fmicb.2021.639995.s013
    DOI: 10.3389/fmicb.2021.639995.s014
    DOI: 10.3389/fmicb.2021.639995.s015
    DOI: 10.3389/fmicb.2021.639995.s016
    DOI: 10.3389/fmicb.2021.639995.s017
    DOI: 10.3389/fmicb.2021.639995.s018
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2587354-4
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    MuLan-Methyl—multiple transformer-based language models for accurate DNA methylation prediction
    Oxford University Press (OUP) ; 2022
    In:  GigaScience Vol. 12 ( 2022-12-28)
    Details
    In: GigaScience, Oxford University Press (OUP), Vol. 12 ( 2022-12-28)
    Abstract: Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning–based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the “pretrain and fine-tune” paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.
    Type of Medium: Online Resource
    ISSN: 2047-217X
    URL: Article
    DOI: 10.1093/gigascience/giad054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2708999-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Interactive analysis of biosurfactants in fruit-waste fermentation samples using BioSurfDB and MEGAN
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-05-11)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-05-11)
    Abstract: Agroindustrial waste, such as fruit residues, are a renewable, abundant, low-cost, commonly-used carbon source. Biosurfactants are molecules of increasing interest due to their multifunctional properties, biodegradable nature and low toxicity, in comparison to synthetic surfactants. A better understanding of the associated microbial communities will aid prospecting for biosurfactant-producing microorganisms. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were subjected to autochthonous fermentation, so as to promote the growth of their associated microbiota, followed by short-read metagenomic sequencing. Using the DIAMOND+MEGAN analysis pipeline, taxonomic analysis shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella , Enterobacter , Stenotrophomonas , Acinetobacter and Escherichia . Functional analysis indicates high similarity among samples and a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. To simplify the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. These results indicate that microbiota obtained from autochthonous fermentation have the genetic potential for biosynthesis of biosurfactants, suggesting that fruit wastes may provide a source of biosurfactant-producing microorganisms, with applications in the agricultural, chemical, food and pharmaceutical industries.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-11753-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis
    American Society for Microbiology ; 2022
    In:  mSystems Vol. 7, No. 1 ( 2022-02-22)
    Details
    In: mSystems, American Society for Microbiology, Vol. 7, No. 1 ( 2022-02-22)
    Abstract: In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.
    Type of Medium: Online Resource
    ISSN: 2379-5077
    URL: Article
    DOI: 10.1128/msystems.01408-21
    Language: English
    Publisher: American Society for Microbiology
    Publication Date: 2022
    detail.hit.zdb_id: 2844333-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 9
    Online Resource
    Online Resource
    Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification
    Oxford University Press (OUP) ; 2023
    In:  Briefings in Bioinformatics Vol. 24, No. 6 ( 2023-09-22)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 24, No. 6 ( 2023-09-22)
    Abstract: A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbad325
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2036055-1
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...