WGSA: an annotation pipeline for human genome sequencing studies
Year: 2015
Type: article
Abstract: DNA sequencing technologies continue to make progress in increased throughput and quality, and decreased cost. As we transition from whole exome capture sequencing to whole genome sequencing (WGS), our ability to convert machine-generated variant calls, including single nucleotide variant (SNV) and insertion-deletion variants (indels), into human-interpretable knowledge has lagged far behind the ability to obtain enormous amounts of variants. To help narrow this gap, here we present WGSA (WGS annotator), a functional annotation pipeline for human genome sequencing studies, which is runnable out of the box on the Amazon Compute Cloud and is freely downloadable at (https://sites.google.com/site/jpopgen/wgsa/).
Functional annotation is a key step in WGS analysis. In one way, annotation helps the analyst filter to a subset of elements of particular interest (eg, cell type specific enhancers), in another way annotation helps the investigators to increase the power of identifying phenotype-associated loci (eg, association test using functional prediction score as a weight) and interpret potentially interesting findings. Currently, there are several popular gene model based annotation tools, including ANNOVAR,1 SnpEff2 and the Ensembl Variant Effect Predictor (VEP).3 These can annotate a variety of protein coding and non-coding gene models from a range of species. It is well known among practitioners that different databases (eg, RefSeq4 and Ensembl5) use different models for …
Source: Journal of Medical Genetics
Institutions: The University of Texas Health Science Center at Houston, Baylor College of Medicine, The University of Texas MD Anderson Cancer Center, National Heart, Lung, and Blood Institute, Framingham Heart Study, National Heart, Lung, and Blood Institute Division of Intramural Research, University of Washington, DNAnexus (United States), Icahn School of Medicine at Mount Sinai
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2286816102
