Biology subjects

Greenfest-Allen, E.

Publications and source records attributed to Greenfest-Allen, E..

4 recordsLinked to original sources

bioRxiv2021

FILER: large-scale, harmonized FunctIonaL gEnomics Repository

MotivationQuerying massive collections of functional genomic and annotation data, linking and summarizing the query results across data sources and data types are important steps in high-throughput genomic and genetic analytical workflows. However, accomplishing these steps is difficult because of the heterogeneity and breadth of data sources, experimental assays, biological conditions (e.g., tissues, cell types), data types, and file formats. ResultsFunctIonaL gEnomics Repository (FILER) is a large-scale, harmonized functional genomics data catalog uniquely providing: 1) streamlined access to >50,000 harmonized, annotated functional genomic and annotation datasets across >20 integrated data sources, >1,100 biological conditions/tissues/cell types, and >20 experimental assays; 2) a scalable, indexing-based genomic querying interface; 3) ability for users to analyze and annotate their own experimental data against reference datasets. This rich resource spans >17 Billion genomic records for both GRCh37/hg19 and GRCh38/hg38 genome builds. FILER scales well with the experimental (query) data size and the number of reference datasets and data sources. When evaluated on large-scale analysis tasks, FILER demonstrated great efficiency as the observed running time for querying 1000x more genomic intervals (106 vs. 103) against all 7x109 hg19 FILER records increased sub-linearly by only a factor of 15x. Together, these features facilitate reproducible research and streamline querying, integrating, and utilizing large-scale functional genomics and annotation data. Availability and implementationFILER can be 1) freely accessed at https://lisanwanglab.org/FILER, 2) deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER), and 3) integrated with other pipelines using provided scripts. Contactlswang@pennmedicine.upenn.edu

bioinformatics↗

bioRxiv2020

NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's Disease genetic and genomic knowledge

INTRODUCTIONThe NIAGADS Alzheimers Genomics Database (GenomicsDB) is a public knowledgebase of Alzheimers disease (AD) genetic datasets and genomic annotations. METHODSIt uses a custom systems architecture to adopt and enforce rigorous standards that facilitate harmonization of AD-relevant GWAS summary statistics datasets with functional annotations, including a database of >230 million annotated variants from the AD Sequencing Projects joint-calling efforts. RESULTSThe knowledgebase generates genome browser tracks and interactive compiled from harmonized datasets and annotations in the underlying database. These facilitate data sharing and discovery, by contextualizing AD-risk associations in a broader functional genomic context or summarizing them in the context of functionally annotated genes and variants. DISCUSSIONCreated to make AD-genetics knowledge more accessible to AD-researchers, the GenomicsDB shares annotated AD-relevant summary statistics datasets via a web interface designed to guide users unfamiliar with genetic data in not only exploring, but also interpreting this ever-growing volume of data.

bioinformatics↗

bioRxiv2020

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

SummaryWe 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

bioinformatics↗

bioRxiv2017

iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks

Weighted-gene correlation network analysis (WGCNA) is frequently used to identify highly co-expressed clusters of genes (modules) within whole-transcriptome datasets. However, transcriptome-scale networks tend to be highly connected, making it challenging for the hierarchical clustering underlying the WGCNA-based classification to discriminate coherently expressed gene sets without significant information loss from either a priori filtering of the expression dataset or a posteriori pruning of the cluster dendrogram.\n\nHere we present iterativeWGCNA, a Python-wrapped extension for the WGCNA R software package that improves the robustness of detected modules and minimizes information loss. The method works by pruning poorly fitting genes from estimated modules and then re-running WGCNA to refine gene clusters. After refining, pruned genes are assembled into a new expression dataset to isolate overlapping modules and the process repeated. In doing so, iterativeWGCNA provides an unsupervised, non-biased filtering to generate a robust, comprehensive network-based classification of whole-transcriptome expression datasets.

bioinformatics↗