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  • Computational Methods, Genomics  (56)
  • Oxford University Press  (56)
  • American Chemical Society
  • American Institute of Physics (AIP)
  • Institute of Physics
  • Society of Petroleum Engineers
  • Wiley
  • 2015-2019  (33)
  • 2010-2014  (23)
  • 1980-1984
  • 1945-1949
  • 1935-1939
  • 1930-1934
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  • Oxford University Press  (56)
  • American Chemical Society
  • American Institute of Physics (AIP)
  • Institute of Physics
  • Society of Petroleum Engineers
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  • 2015-2019  (33)
  • 2010-2014  (23)
  • 1980-1984
  • 1945-1949
  • 1935-1939
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  • 1
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    Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum (2015)
    Oxford University Press
    Details
    Publikationsdatum: 2015-05-29
    Beschreibung: Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/ . We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 2
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    A genome-wide approach for detecting novel insertion-deletion variants of mid-range size (2016)
    Oxford University Press
    Details
    Publikationsdatum: 2016-09-03
    Beschreibung: We present SWAN, a statistical framework for robust detection of genomic structural variants in next-generation sequencing data and an analysis of mid-range size insertion and deletions (〈10 Kb) for whole genome analysis and DNA mixtures. To identify these mid-range size events, SWAN collectively uses information from read-pair, read-depth and one end mapped reads through statistical likelihoods based on Poisson field models. SWAN also uses soft-clip/split read remapping to supplement the likelihood analysis and determine variant boundaries. The accuracy of SWAN is demonstrated by in silico spike-ins and by identification of known variants in the NA12878 genome. We used SWAN to identify a series of novel set of mid-range insertion/deletion detection that were confirmed by targeted deep re-sequencing. An R package implementation of SWAN is open source and freely available.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 3
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    Gene integrated set profile analysis: a context-based approach for inferring biological endpoints (2016)
    Oxford University Press
    Details
    Publikationsdatum: 2016-04-21
    Beschreibung: The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an ‘integrate by intersection’ (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 4
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    Finding approximate gene clusters with GECKO 3 (2016)
    Oxford University Press
    Details
    Publikationsdatum: 2016-12-01
    Beschreibung: Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in 〈40 min.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 5
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    Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform (2017)
    Oxford University Press
    Details
    Publikationsdatum: 2017-01-10
    Beschreibung: Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 6
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    TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2016)
    Oxford University Press
    Details
    Publikationsdatum: 2016-05-06
    Beschreibung: The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 7
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    Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism (2015)
    Oxford University Press
    Details
    Publikationsdatum: 2015-05-03
    Beschreibung: Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF–DNA binding specificities (‘motifs’). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF–DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of ~200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 8
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    Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015)
    Oxford University Press
    Details
    Publikationsdatum: 2015-08-29
    Beschreibung: Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 9
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    f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome (2016)
    Oxford University Press
    Details
    Publikationsdatum: 2016-06-03
    Beschreibung: The ability to integrate ‘omics’ (i.e. transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs) across different ‘omics’ data types or multi-dimensional data including time courses. We present fCI (f-divergence Cut-out Index), a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying fCI to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package fCI is available at R Bioconductor and http://software.steenlab.org/fCI/ .
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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  • 10
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    An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data (2015)
    Oxford University Press
    Details
    Publikationsdatum: 2015-04-21
    Beschreibung: Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free ( de novo ) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.
    Schlagwort(e): Computational Methods, Genomics
    Print ISSN: 0305-1048
    Digitale ISSN: 1362-4962
    Thema: Biologie
    Publiziert von Oxford University Press
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