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

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Summary</jats:title><jats:p>We 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.</jats:p></jats:sec><jats:sec><jats:title>Availability</jats:title><jats:p>SparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://bitbucket.org/wanglab-upenn/SparkINFERNO">https://bitbucket.org/wanglab-upenn/SparkINFERNO</jats:ext-link> or <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://hub.docker.com/r/wanglab/spark-inferno">https://hub.docker.com/r/wanglab/spark-inferno</jats:ext-link>.</jats:p></jats:sec><jats:sec><jats:title>Contact</jats:title><jats:p><jats:email>lswang@pennmedicine.upenn.edu</jats:email></jats:p></jats:sec>

FILER: large-scale, harmonized FunctIonaL gEnomics Repository

“…FILER provides query/search functions ( Section 3.3 ), allowing to easily integrate and query datasets using custom genetic and genomic analysis workflows such as INFERNO (Amlie-Wolf et al ., 2018; Kuksa et al ., 2020) (see Section 3.4 for an example). The scalable interface of FILER allows analysis and annotation of user-generated experimental data (e.g., for a particular biological condition) with reference FILER datasets across various tissues/cell types and data sources ( Section 3.3 ), facilitating research studies across different human diseases.…”

“…Example of integrating FILER (middle part of the figure) with a custom aggregation and analysis workflow (bottom of the figure), SparkINFERNO (Kuksa et al ., 2020) for high-throughput non-coding variant analysis (inputs at the top).…”

“…To demonstrate the utility of FILER in analyzing and characterizing genomic regions or features of interest, we used FILER together with SparkINFERNO pipeline (Kuksa et al, 2020) for functional characterization of non-coding variants in IBD GWAS (Liu et al, 2015). As part of the SparkINFERNO pipeline, all genome-wide significant GWAS et al, 2015).…”

Alzheimer’s Disease variant portal (ADVP): a catalog of genetic findings for Alzheimer’s Disease

“…Both Ensembl ID (Yates et al, 2020) and HGNC (Braschi et al, 2019) gene symbols are reported for each genetic variant. For each ADVP variant, the co-localized genomic element (e.g., promoter, intron, UTR, repeat, intergenic) is reported based on the genomic partition information (Amlie-Wolf et al, 2018; Kuksa et al, 2020).…”

“…For each genetic variant co-localized with one or more genes, both EnsemblID [27] and HGNC [28] symbols for the gene(s) are reported. For each ADVP variant, the co-localized genomic element is reported based on the genomic partition information [29, 30].…”