Abstract
Although new sequencing technology has enabled biologists to examine genome-wide variation in their taxa of interest, new experimental design, analysis, and cost hurdles impede many researchers from using the benefits of next-generation sequencing. Here we present a workflow to help researchers identify genomic intervals associated with a desired gene function by using gene ontology terms to filter an annotated genome. Resulting intervals comprise a target region that can then be used to design complementary sequences or baits for capturing desired targets in a target enrichment experiment. We show that target regions produced by our workflow and Ensembl Genebuild share intervals but nonetheless differ, and then demonstrate our workflow’s utility for target enrichment experiments involving non-model organisms. Using available turtle genomes and bait sequences designed to capture a workflow-generated target region, in silico analysis predicts between 48 and 86 % of baits will bind to complementary sequences in genomes across the Order Testudines (turtles and tortoises). We then use these bait sequences in an actual target enrichment experiment and show that bait performance falls within the range predicted by in silico analysis. We show that by selecting a reference genome related as closely as possible to taxa of interest and focusing on important and likely conserved gene functions, users can acquire valuable genomic data from non-model organisms.
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Acknowledgments
We thank LSU and the LSU AgCenter for financial and logistical support. The Lucius Gilbert Foundation provided support for sequencing and for J.P.E. We are grateful to Richard Carmouche of Pennington Biomedical Research Center’s Genomic Core Facility for performing next-generation sequencing laboratory work. This project used Genomics core facilities that are supported in part by COBRE (NIH 8P20GM103528) and NORC (NIH 2P30DK072476) center Grants from the National Institutes of Health. We thank High Performance Computing at LSU for resources to analyze next-generation sequencing data.
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J.P.E. designed the study and wrote the workflow scripts. J.P.E. and S.S.T. wrote the paper.
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12686_2015_487_MOESM1_ESM.docx
Supplementary Table 1 Comparison of two target regions made by filtering the Pelodiscus sinensis 1.0 genome assembly using the gene ontology term immune response (GO:0006955) and its descendants. GO2TR’s target region was generated using standard GO2TR settings and mRNA predicted by the NCBI Eukaryotic Genome Annotation Pipeline. Ensembl-GOanna’s target region was generated using gene annotations and GO term associations determined by GOanna. For each comparison, features found in the first target region are compared to features in the second. Shared features overlap at least 99 %, while unique features are present in the first but not the second target region. Shared intervals differ slightly between the two target regions because some were considered flanking, which are those present in the first target region that stretch beyond intervals in the second, resulting in non-overlapping 5′ or 3′ bases. (DOCX 12 kb)
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Elbers, J.P., Taylor, S.S. GO2TR: a gene ontology-based workflow to generate target regions for target enrichment experiments. Conservation Genet Resour 7, 851–857 (2015). https://doi.org/10.1007/s12686-015-0487-6
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DOI: https://doi.org/10.1007/s12686-015-0487-6