TASUKE+: a web-based platform for exploring genome-wide association studies results and large-scale resequencing data

• Published in: DNA research 2019-12, Vol.26 (6), p.445-452
• Main Authors: Kumagai, Masahiko, Nishikawa, Daiki, Kawahara, Yoshihiro, Wakimoto, Hironobu, Itoh, Ryutaro, Tabei, Norio, Tanaka, Tsuyoshi, Itoh, Takeshi
• Format: Article
• Language: English
• Subjects: Computational Biology - methods; Genetic Association Studies - methods; Genome - genetics; Genome-Wide Association Study - methods; Genomics - methods; High-Throughput Nucleotide Sequencing - methods; Internet; Phylogeny; Reproducibility of Results
• ISSN: 1756-1663
• DOI: 10.1093/dnares/dsz022

Recent revolutionary advancements in sequencing technologies have made it possible to obtain mass quantities of genome-scale sequence data in a cost-effective manner and have drastically altered molecular biological studies. To utilize these sequence data, genome-wide association studies (GWASs) have become increasingly important. Hence, there is an urgent need to develop a visualization tool that enables efficient data retrieval, integration of GWAS results with diverse information and rapid public release of such large-scale genotypic and phenotypic data. We developed a web-based genome browser TASUKE+ (https://tasuke.dna.affrc.go.jp/), which is equipped with the following functions: (i) interactive GWAS results visualization with genome resequencing data and annotation information, (ii) PCR primer design, (iii) phylogenetic tree reconstruction and (iv) data sharing via the web. GWAS results can be displayed in parallel with polymorphism data, read depths and annotation information in an interactive and scalable manner. Users can design PCR primers for polymorphic sites of interest. In addition, a molecular phylogenetic tree of any region can be reconstructed so that the overall relationship among the examined genomes can be understood intuitively at a glance. All functions are implemented through user-friendly web-based interfaces so that researchers can easily share data with collaborators in remote places without extensive bioinformatics knowledge.