GigaScienceBioinformaticsPeer-reviewed journal article
SnpHub: an easy-to-set-up web server framework for exploring large-scale genomic variation data in the post-genomic era with applications in wheat
Wenxi WangiD, Zihao WangiD, Xintong LiiD, Zhongfu NiiD, Zhaorong HuiD, Mingming XiniD, Huiru PengiD, Yingyin YaoiD, Qixin SuniD, Weilong GuoiD
Abstract
BackgroundThe cost of high-throughput sequencing is rapidly decreasing, allowing researchers to investigate genomic variations across hundreds or even thousands of samples in the post-genomic era. The management and exploration of these large-scale genomic variation data require programming skills. The public genotype querying databases of many species are usually centralized and implemented independently, making them difficult to update with new data over time. Currently, there is a lack of a widely used framework for setting up user-friendly web servers for exploring new genomic variation data in diverse species. ResultsHere, we present SnpHub, a Shiny/R-based server framework for retrieving, analysing and visualizing the large-scale genomic variation data that be easily set up on any Linux server. After a pre-building process based on the provided VCF files and genome annotation files, the local server allows users to interactively access SNPs/INDELs and annotation information by locus or gene and for user-defined sample sets through a webpage. Users can freely analyse and visualize genomic variations in heatmaps, phylogenetic trees, haplotype networks, or geographical maps. Sample-specific sequences can be accessed as replaced by SNPs/INDELs. ConclusionsSnpHub can be applied to any species, and we build up a SnpHub portal website for wheat and its progenitors based on published data in present studies. SnpHub and its tutorial are available as http://guoweilong.github.io/SnpHub/.
- assay: whole-genome sequencing
- research focus: database / resource
- research focus: variant calling
- research focus: variant effect prediction
- research focus: benchmarking
- approach: statistical model
- Citations
- 96
- FWCI
- 3.32
- Field percentile
- top 6%
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