GIGGLE: a search engine for large-scale integrated genome analysis

Layer, Ryan M.; Pedersen, Brent S.; DiSera, Tonya; Marth, Gábor; Gertz, Jason; Quinlan, Aaron R.

doi:10.1038/nmeth.4556

Cited by 167 publications

(149 citation statements)

References 22 publications

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“…Given these conditions, we can either pre-merge the index of all files or use a data structure to dynamically update index as we parse files. This would be similar to the interval data structure used by GIGGLE (Layer et al 2018), which uses a B+ tree to create an index from thousands of genomic data and annotation files. We would like to extend the File Server to use giggle or giggle like structures for querying data, or an index structure that is updated dynamically as more queries are processed across files.…”

Section: Discussionmentioning

confidence: 99%

Epiviz File Server: Query, Transform and Interactively Explore Data from Indexed Genomic Files

Kancherla

Yang

Chae

et al. 2019

Preprint

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Genomic data repositories like The Cancer Genome Atlas (TCGA), Encyclopedia of DNA Elements (ENCODE), Bioconductor's AnnotationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of files data from these repositories to perform their data analysis. As these data repositories become larger, researchers often face bottlenecks in their exploratory data analysis. Based on the concepts of a NoDB paradigm, we developed epivizFileServer, a Python library that implements an in-situ data query system for local or remotely hosted indexed genomic files, not only for visualization but also data manipulation. The File Server library decouples data from analysis workflows and provides an abstract interface to define computations independent of the location, format or structure of the file.

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Section: Discussionmentioning

confidence: 99%

Epiviz File Server: Query, Transform and Interactively Explore Data from Indexed Genomic Files

Kancherla

Yang

Chae

et al. 2019

Preprint

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“…Analysis modules perform various types of genomic data integration to produce functional evidence including tissue-specific regulatory elements (enhancers), transcription factor (TF) activity, chromatin states, and genetic regulation (eQTL) information. SparkINFERNO implements scalable genomic querying ( Supplementary Figures S2, S3) using Spark parallel transformations and Giggle-based genomic indexing (Layer et al, 2018). SparkINFERNO can be extended with additional annotation data and/or customized evaluation modules.…”

Section: Methodsmentioning

confidence: 99%

SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

Kuksa

Lee

Amlie‐Wolf

et al. 2020

Preprint

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AbstractSummaryWe report SparkINFERNO (Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants), a scalable bioinformatics pipeline characterizing noncoding GWAS association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts, and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci, and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWAS studies and show that SparkINFERNO is more than 60-times efficient and scales with data size and amount of computational resources.AvailabilitySparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno.Contactlswang@pennmedicine.upenn.edu

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“…Genes are defined as overlapping with the genomic intervals based on coordinates of canonical APPRIS (Rodriguez et al, 2013) transcript isoforms if available or alternatively the longest transcript for a given gene; the overlap step is performed with GIGGLE (Layer et al, 2018). Users can extend the gene region by up to 20 kbp in upstream or downstream direction since most of the strong eQTLs (expression quantitative trait loci) regulating gene expression are found in that region (Veyrieras et al, 2008).…”

Section: Identification Of Overlapping Mendelian Genesmentioning

confidence: 99%

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

Sobczyk

Gaunt

Paternoster

2020

Preprint

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Gene prioritisation at GWAS loci necessities careful assembly and examination of different types of molecular evidence to arrive at a set of plausible candidates. In many human traits, common small-effect mutations may subtly dysregulate the function of the very same genes which are impacted by rare, large-effect mutations causing Mendelian disease of similar phenotype. However, information on gene-Mendelian disease associations, rare pathogenic mutations driving the disease, and the disease phenotype ontology is dispersed across many data sources and does not integrate easily with enrichment analysis.MendelVar is a new webserver facilitating transfer of knowledge from Mendelian disease research into interpretation of genetic associations from GWAS of complex traits.MendelVar allows querying of pre-defined or LD-determined genomic intervals against a comprehensive integrated database to find overlap with genes linked to Mendelian disease. Next, MendelVar looks for enrichment of any Human Phenotype Ontology, DiseaseOntology and other ontology/pathway terms associated with identified Mendelian genes. In addition, MendelVar provides a list of all overlapping pathogenic and likely pathogenic variants for Mendelian disease sourced from ClinVar. Inclusion of information obtained from MendelVar in post-GWAS gene annotation pipelinescan strengthen the case for causal importance of some genes. Moreover, as genes with Mendelian disease evidence may make for more successful drug targets, this may be particularly useful in drug discovery pipelines. Taking GWAS summary statistics for malepattern baldness, intelligence and atopic dermatitis, we demonstrate the use of MendelVar in prioritizing candidate genes at these loci which are linked to relevant enriched ontology terms. MendelVar is freely available at https

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GIGGLE: a search engine for large-scale integrated genome analysis

Cited by 167 publications

References 22 publications

Epiviz File Server: Query, Transform and Interactively Explore Data from Indexed Genomic Files

Epiviz File Server: Query, Transform and Interactively Explore Data from Indexed Genomic Files

SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

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