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ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » Research » Publications at this Location » Publication #369307

Research Project: Gene Discovery and Designing Soybeans for Food, Feed, and Industrial Applications

Location: Plant Genetics Research

Title: SNPViz v2.0: A web-based tool for enhanced haplotype analysis using large scale resequencing datasets and discovery of phenotypes causative gene using allelic variations

item ZENG, SHUAL - University Of Missouri
item SKRABISOVA, MARIA - Palacky University
item LYU, ZHEN - University Of Missouri
item CHAN, YEN ON - University Of Missouri
item Bilyeu, Kristin
item JOSHI, TRUPTI - University Of Missouri

Submitted to: Bioinformatics
Publication Type: Proceedings
Publication Acceptance Date: 12/19/2020
Publication Date: 1/13/2021
Citation: Zeng, S., Skrabisova, M., Lyu, Z., Chan, Y., Bilyeu, K.D., Joshi, T. 2021. SNPViz v2.0: A web-based tool for enhanced haplotype analysis using large scale resequencing datasets and discovery of phenotypes causative gene using allelic variations. Bioinformatics. 1408-1415.

Interpretive Summary: The ability to determine the DNA sequence of the whole genome of large sets of soybean accessions has overwhelmed researchers' abilities to efficiently analyze and interpret the significance of that data, a symptom of the the "big data" era. The research described here was to develop an improved software interactive tool that enables researchers to effectively and efficiently analyze and interpret such big data (over 800) whole genome sequence sets for soybean. The software tool has enhanced features that allow it to be used in conjunction with a novel strategy for identifying the exact genes responsible for important traits that will improve the value of soybeans and speed up soybean variety development.

Technical Abstract: Single nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) are widely spread across all chromosomes of the genome and act as biological markers, which aid in identification of genes associated with traits or phenotypes. With the advances in next-generation sequencing (NGS) technology, large amounts of SNPs and Indels data have become available, making it difficult to effectively perform analysis across multiple samples and intuitively integrate, compare and/or visualize them simultaneously. Genome-wide association studies (GWAS) is a widely used method to find genetic variations associated with a trait, but it lacks an efficient way to investigate genomic variant functions. To tackle these issues, we have developed SNPViz v2.0, a web-based tool to visualize large-scale haplotype blocks with detailed SNPs and Indels grouped by their chromosomal coordinates, along with their overlapping gene models, phenotype to genotype accuracies, Gene Ontology (GO) annotations, protein families (Pfam) annotations, genomic variant annotations, and their functional effects. Moreover, SNPViz v2.0 integrates several large scale soybean SNPs and Indels datasets including G. Soja, GWAS, NAM41, USB-15x, USB-40x, MSMC and Zhou302, available from multiple studies. SNPViz v2.0 is deployed for all organisms and available in both SoyKB and KBCommons frameworks. For soybean data only, the SNPViz 2.0 is publicly available at For other organisms such as Arabidopsis thaliana, Mus musculus and Zea mays, SNPViz 2.0 is publicly available in their respective knowledge bases at