A community resource for paired genomic and metabolomic data mining.
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ABSTRACT:
SUBMITTER: Schorn MA
PROVIDER: S-EPMC7987574 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:With the advent of high-throughput sequencing technologies, many staphylococcal genomes have been sequenced. Comparative analysis of these strains will provide better understanding of their biology, phylogeny, virulence and taxonomy, which may contribute to better management of diseases caused by staphylococcal pathogens. We developed StaphyloBase with the goal of having a one-stop genomic resource platform for the scientific community to access, retrieve, download, browse, search, visualize and analyse the staphylococcal genomic data and annotations. We anticipate this resource platform will facilitate the analysis of staphylococcal genomic data, particularly in comparative analyses. StaphyloBase currently has a collection of 754 032 protein-coding sequences (CDSs), 19 258 rRNAs and 15 965 tRNAs from 292 genomes of different staphylococcal species. Information about these features is also included, such as putative functions, subcellular localizations and gene/protein sequences. Our web implementation supports diverse query types and the exploration of CDS- and RNA-type information in detail using an AJAX-based real-time search system. JBrowse has also been incorporated to allow rapid and seamless browsing of staphylococcal genomes. The Pairwise Genome Comparison tool is designed for comparative genomic analysis, for example, to reveal the relationships between two user-defined staphylococcal genomes. A newly designed Pathogenomics Profiling Tool (PathoProT) is also included in this platform to facilitate comparative pathogenomics analysis of staphylococcal strains. In conclusion, StaphyloBase offers access to a range of staphylococcal genomic resources as well as analysis tools for comparative analyses. Database URL: http://staphylococcus.um.edu.my/.
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Project description:Mining bacterial genomes for bacteriocins is a challenging task due to the substantial structure and sequence diversity, and generally small sizes, of these antimicrobial peptides. Major progress in the research of antimicrobial peptides and the ever-increasing quantities of genomic data, varying from (un)finished genomes to meta-genomic data, led us to develop the significantly improved genome mining software BAGEL2, as a follow-up of our previous BAGEL software. BAGEL2 identifies putative bacteriocins on the basis of conserved domains, physical properties and the presence of biosynthesis, transport and immunity genes in their genomic context. The software supports parameter-free, class-specific mining and has high-throughput capabilities. Besides building an expert validated bacteriocin database, we describe the development of novel Hidden Markov Models (HMMs) and the interpretation of combinations of HMMs via simple decision rules for prediction of bacteriocin (sub-)classes. Furthermore, the genetic context is automatically annotated based on (combinations of) PFAM domains and databases of known context genes. The scoring system was fine-tuned using expert knowledge on data derived from screening all bacterial genomes currently available at the NCBI. BAGEL2 is freely accessible at http://bagel2.molgenrug.nl.
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Project description:Untargeted metabolomics analysis captures chemical reactions among small molecules. Common mass spectrometry-based metabolomics workflows first identify the small molecules significantly associated with the outcome of interest, then begin exploring their biochemical relationships to understand biological fate or impact. We suggest an alternative by which general chemical relationships including abiotic reactions can be directly retrieved through untargeted high-resolution paired mass distance (PMD) analysis without a priori knowledge of the identities of participating compounds. PMDs calculated from the mass spectrometry data are linked to chemical reactions obtained via data mining of small molecule and reaction databases, i.e. 'PMD-based reactomics'. We demonstrate applications of PMD-based reactomics including PMD network analysis, source appointment of unknown compounds, and biomarker reaction discovery as complements to compound discovery analyses used in traditional untargeted workflows. An R implementation of reactomics analysis and the reaction/PMD databases is available as the pmd package.
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Project description:The Comprehensive Phytopathogen Genomics Resource (CPGR) provides a web-based portal for plant pathologists and diagnosticians to view the genome and trancriptome sequence status of 806 bacterial, fungal, oomycete, nematode, viral and viroid plant pathogens. Tools are available to search and analyze annotated genome sequences of 74 bacterial, fungal and oomycete pathogens. Oomycete and fungal genomes are obtained directly from GenBank, whereas bacterial genome sequences are downloaded from the A Systematic Annotation Package (ASAP) database that provides curation of genomes using comparative approaches. Curated lists of bacterial genes relevant to pathogenicity and avirulence are also provided. The Plant Pathogen Transcript Assemblies Database provides annotated assemblies of the transcribed regions of 82 eukaryotic genomes from publicly available single pass Expressed Sequence Tags. Data-mining tools are provided along with tools to create candidate diagnostic markers, an emerging use for genomic sequence data in plant pathology. The Plant Pathogen Ribosomal DNA (rDNA) database is a resource for pathogens that lack genome or transcriptome data sets and contains 131 755 rDNA sequences from GenBank for 17 613 species identified as plant pathogens and related genera. Database URL: http://cpgr.plantbiology.msu.edu.
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Project description:Homing endonucleases have great potential as tools for targeted gene therapy and gene correction, but identifying variants of these enzymes capable of cleaving specific DNA targets of interest is necessary before the widespread use of such technologies is possible. We identified homologues of the LAGLIDADG homing endonuclease I-AniI and their putative target insertion sites by BLAST searches followed by examination of the sequences of the flanking genomic regions. Amino acid substitutions in these homologues that were located close to the target site DNA, and thus potentially conferring differences in target specificity, were grafted onto the I-AniI scaffold. Many of these grafts exhibited novel and unexpected specificities. These findings show that the information present in genomic data can be exploited for endonuclease specificity redesign.
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Project description:Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.
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Project description:The normal functions of genomes depend on the precise expression of messenger RNAs and noncoding RNAs (ncRNAs) such as transfer RNAs and microRNAs in eukaryotes. These ncRNAs and functional RNA structures (FRSs) act as regulators or response elements for cellular factors and participate in transcription, posttranscriptional processing, and translation. Knowledge discovery of these FRSs in huge DNA/RNA sequence databases is a very important step to reach our goal of going from genomic sequence data to biological knowledge for understanding RNA-based regulation. Analyses of a large number of FRSs have indicated that the FRS can be well characterized by some quantitative measures such as significance and well-ordered scores of the local segment. Various data mining tools have been developed and successfully applied to FRS discovery in genomic sequence databases. Here, we summarize our efforts in the computational discovery of structured features of ncRNAs and FRSs within complex genomes by EDscan and SigED.
| S-EPMC8301259 | biostudies-literature
Project description:BackgroundAdvances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets.ResultsWe provide three implementations of MKL in R. These methods are applied to simulated data to illustrate that MKL can select appropriate models. We also apply MKL to combine clinical information with miRNA gene expression data of ovarian cancer study into a single analysis. Lastly, we show that MKL can identify gene sets that are known to play a role in the prognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as, identify new gene sets for the future research.ConclusionMultiple kernel learning coupled with modern optimization techniques provides a promising learning tool for building predictive models based on multi-source genomic data. MKL also provides an automated scheme for kernel prioritization and parameter tuning. The methods used in the paper are implemented as an R package called RMKL package, which is freely available for download through CRAN at https://CRAN.R-project.org/package=RMKL .
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Project description:Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.
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Project description:Celsius is a data warehousing system to aggregate Affymetrix CEL files and associated metadata. It provides mechanisms for importing, storing, querying, and exporting large volumes of primary and pre-processed microarray data. Celsius contains ten billion assay measurements and affiliated metadata. It is the largest publicly available source of Affymetrix microarray data, and through sheer volume it allows a sophisticated, broad view of transcription that has not previously been possible.
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