TY - JOUR T1 - SVCurator: A Crowdsourcing app to visualize evidence of structural variants for the human genome JF - bioRxiv DO - 10.1101/581264 SP - 581264 AU - Lesley M Chapman AU - Noah Spies AU - Patrick Pai AU - Chun Shen Lim AU - Andrew Carroll AU - Giuseppe Narzisi AU - Christopher M. Watson AU - Christos Proukakis AU - Wayne E. Clarke AU - Naoki Nariai AU - Eric Dawson AU - Garan Jones AU - Daniel Blankenberg AU - Christian Brueffer AU - Chunlin Xiao AU - Sree Rohit Raj Kolora AU - Noah Alexander AU - Paul Wolujewicz AU - Azza Ahmed AU - Graeme Smith AU - Saadlee Shehreen AU - Aaron M. Wenger AU - Marc Salit AU - Justin M. Zook Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/05/30/581264.abstract N2 - A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is yet to be defined. In this study, we manually curated 1235 SVs which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app - SVCurator - to help curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy.SVCurator is a Python Flask-based web platform that displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. The crowdsourced results were highly concordant with 37 out of the 61 curators having at least 78% concordance with a set of ‘expert’ curators, where there was 93% concordance amongst ‘expert’ curators. This produced high confidence labels for 935 events. When compared to the heuristic-based draft benchmark SV callset from GIAB, the SVCurator crowdsourced labels were 94.5% concordant with the benchmark set. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies. ER -