<tt>VariantAnnotation</tt>
            : a 
            <tt>Bioconductor</tt>
             package for exploration and annotation of genetic variants

Obenchain, Valerie; Lawrence, Michael; Carey, Vincent J.; Gogarten, Stephanie M.; Shannon, Paul; Morgan, Martin

doi:10.1093/bioinformatics/btu168

Cited by 307 publications

(251 citation statements)

References 10 publications

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“…We use the R package Variant Annotation 59 to functionally annotate the SNPs and indels. We considered only the relationship between the marker and its target gene in a cis -eQTL signal and not the functional property of the SNP in relation to other genes.…”

Section: Methodsmentioning

confidence: 99%

Genetic variability in the regulation of gene expression in ten regions of the human brain

et al. 2014

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Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found that cis-eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis-eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/. Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.

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Section: Methodsmentioning

confidence: 99%

Genetic variability in the regulation of gene expression in ten regions of the human brain

et al. 2014

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“…No cutoff was used for the Sanger data. Variant locations with respect to Zv9 annotated genes were determined using the R Bioconductor package VariantAnnotation (Obenchain et al, 2014). When variant locations differed between transcripts of a gene, the location most likely to affect the protein sequence was used, with splice sites considered more consequential than exonic sites.…”

Section: Molecular Analysismentioning

confidence: 99%

Maximizing mutagenesis with solubilized CRISPR-Cas9 ribonucleoprotein complexes.

et al. 2016

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CRISPR-Cas9 enables efficient sequence-specific mutagenesis for creating somatic or germline mutants of model organisms. Key constraints in vivo remain the expression and delivery of active Cas9-sgRNA ribonucleoprotein complexes (RNPs) with minimal toxicity, variable mutagenesis efficiencies depending on targeting sequence, and high mutation mosaicism. Here, we apply in vitro assembled, fluorescent Cas9-sgRNA RNPs in solubilizing salt solution to achieve maximal mutagenesis efficiency in zebrafish embryos. MiSeq-based sequence analysis of targeted loci in individual embryos using CrispRVariants, a customized software tool for mutagenesis quantification and visualization, reveals efficient biallelic mutagenesis that reaches saturation at several tested gene loci. Such virtually complete mutagenesis exposes loss-of-function phenotypes for candidate genes in somatic mutant embryos for subsequent generation of stable germline mutants. We further show that targeting of non-coding elements in gene regulatory regions using saturating mutagenesis uncovers functional control elements in transgenic reporters and endogenous genes in injected embryos. Our results establish that optimally solubilized, in vitro assembled fluorescent Cas9-sgRNA RNPs provide a reproducible reagent for direct and scalable loss-of-function studies and applications beyond zebrafish experiments that require maximal DNA cutting efficiency in vivo.

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“…Our developments are motivated by an interest in bridging effective single-assay Application Program Interface (API) elements, including endomorphic feature and sample subset operations, to multi-omic contexts of arbitrary complexity and volume (Supplemental Table 1). A main concern in our work is to allow data analysts and developers to simplify the management of both traditional in-memory assay stores for smaller datasets, and out-of-memory assay stores for very large data, in such formats as HDF5 (6), tabix-indexed VCF (7), or Google BigTable (8). …”

Section: Introductionmentioning

confidence: 99%

Software for the Integration of Multiomics Experiments in Bioconductor

Ramos

Schiffer

et al. 2017

Cancer Research

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Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

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`VariantAnnotation` : a `Bioconductor` package for exploration and annotation of genetic variants

Cited by 307 publications

References 10 publications

Genetic variability in the regulation of gene expression in ten regions of the human brain

Genetic variability in the regulation of gene expression in ten regions of the human brain

Maximizing mutagenesis with solubilized CRISPR-Cas9 ribonucleoprotein complexes.

Software for the Integration of Multiomics Experiments in Bioconductor

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VariantAnnotation : a Bioconductor package for exploration and annotation of genetic variants

Cited by 307 publications

References 10 publications

Genetic variability in the regulation of gene expression in ten regions of the human brain

Genetic variability in the regulation of gene expression in ten regions of the human brain

Maximizing mutagenesis with solubilized CRISPR-Cas9 ribonucleoprotein complexes.

Software for the Integration of Multiomics Experiments in Bioconductor

Contact Info

Product

Resources

About

`VariantAnnotation` : a `Bioconductor` package for exploration and annotation of genetic variants