UBC Sunflower Genome Dataset
This dataset is a catalogue of functionally important variation that can be used to create better-adapted sunflower varieties (e.g. developing breeding lines with improved drought tolerance) and to study the genetic mechanisms driving evolution and adaptation.
Sunflowers are challenging plants to work with, in part because their genome is large (3.6 billion base pairs), exceeding that of humans by 300 million base pairs, but also because the genomes of different sunflower lines differ in gene content and genome structure. This dataset is the result of hundreds of thousands of vcpu hours.
To the best of our knowledge, as of this writing, this is the largest genomic dataset for sunflower in the world. It is composed of whole-genome-shotgun sequence data from 2,392 diverse lines of cultivated, wild, and landrace sunflowers.
What can be found in this set?
This dataset allows the genetic study of the germplasm (i.e. seed) of 2392 Sunflowers (a mix of wild, landrace, and cultivated varieties), from multiple species, collected from diverse ecosystems throughout North America. Plants were grown in a common garden experiment (in greenhouse), and then whole-genome-sequenced over a total of 3523 Illumina HiSeq runs. All the plants involved are associated with curated metadata in NCBI’s Short Read Archive (SRA), where the raw source data is currently available. Along with our analyzed dataset, we also have phenotypic data and environmental metrics which will accompany this dataset.
The genetic data is available in common data formats used in bioinformatics (fastq, bam, .g.vcf, vcf). Scripts and programs used to produce the datase will be made available in a public git repository.
The current dataset consists in sunflower sequences aligned to the best genome reference currently available for sunflower (Ha412HO v2). The dataset will be grown to include variant calls and filtered datasets (preserving only high-quality variants).
How was this dataset produced?
The plant collection, and creation of the raw dataset is the fruit of an intense several years of collaborative effort by members of the Rieseberg Lab (See the Contact page).
The raw data was processed with a novel open-source framework for reproducibility developed at UBC, called “Bunnies”. Bunnies allows developers to express their data workflows in a reproducible way.
The data files produced by the framework however, are presented in standard formats, and can be utilized by existing applications. You may freely browse the files from the dataset and extract the portions that are of interest to your study.
The framework is similar in many ways to existing frameworks and languages (Airflow, Snakemake, nextflow.io, luigi, WDL, etc.), with the difference that different researchers can reuse each other’s partial results. Bunnies captures the provenance of each data item, and will reuse previous computed files if they are available, while ensuring that the files are equivalent (exactly the same parameters).Data-flow-graphs (e.g. makefiles) written with bunnies can use this dataset as a cache of “partial results” for any downstream analysis.
One of the goals with the This workflow will greatly reduce the amount of time spent on bioinformatic analyses for members working downstream, as they will be able to efficiently share and find previously produced results.