Today’s guest post was contributed by Caitlin Rossi, a scientific and medical writer. Her work can be found at caitlinrossi.com.

Maize might just be the world’s highest-achieving grain—it’s a triple threat through its contributions as food, fiber, and fuel. In addition to being a crop production powerhouse, maize is also a genomic gem as it is much more molecularly diverse than most plants, cultivated and wild alike.

Geneticists have been trying to capture the genomic sequence diversity of maize for over a decade, but efforts to resequence the crop have proven challenging. The maize data available from major resequencing projects span mapping and genotype-calling pipelines as well as versions of the reference genome, creating continuity issues that prevent a corn-to-corn comparison. Different pipelines can produce conflicting variant calls, and using disparate versions of a reference genome can leave researchers with inconsistent genomic coordinates. Just because a variant is found in one data set doesn’t guarantee it’ll be found in another. On top of everything else, the available data is rarely delivered in a user-friendly platform.

So, how do geneticists find their way out of a maze of nucleotide diversity data? Andorf et al. provide the ball of yarn in a new paper published in G3: Genes|Genomes|Genetics.

The authors’ goal was to offer a standardized resource for performing comparative genomics experiments in maize—one that uses the same variant-calling pipeline on the same reference genome. To accomplish this, the Maize Genetics and Genomics Database (MaizeGDB) joined forces with additional maize researchers from Iowa State University and the University of California-Davis to offer variant data from nearly 1,500 resequenced accessions, all analyzed through a standardized pipeline and mapped to version 5 of the B73 reference genome.

Along with this dataset comes an impressive update to one of the field’s most important tools. SNPversity is an open-source, web-based genotype visualization tool that was launched in 2018 in response to community survey data. In this study, Andorf et al. present SNPversity 2.0, a full rewrite of the original tool packed with upgrades.  

Augmented with additional data from other variant-calling pipelines, the new and improved build allows researchers to filter and download variants based on their own parameters. Users can select which data sets they’d like to query, explore the genomic locations and accessions of their choice, and receive outputs that plainly visualize variants and interpret findings. The authors stress the simplicity of their tool—accessions on SNPversity 2.0 are even color-coded. In addition to its consistent pipeline and effortless downloads, SNPversity 2.0 offers more metadata and interoperability with other genome resources. Users can even build a “tree visualization,” letting them picture variant data within the graphical context of evolutionary trees.

With this new resource publicly available, geneticists attempting to assay the all-important maize can sidestep the problems of inconsistent datasets and software. The dataset will also be updated regularly with the latest maize accessions, maintaining a powerful resource to map and call variants and to help characterize maize’s molecular diversity, one user at a time.

References

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