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ARS Home » Pacific West Area » Wenatchee, Washington » Physiology and Pathology of Tree Fruits Research » Research » Research Project #432570

Research Project: Enhancing Reference Genomes for Cross-Cultivar Functional Genomics

Location: Physiology and Pathology of Tree Fruits Research

Project Number: 2094-43000-007-19-T
Project Type: Trust Fund Cooperative Agreement

Start Date: Jan 1, 2017
End Date: Dec 31, 2019

Enhance discovery of genetic factors associated with fruit quality differences using existing and in-progress RNA-seq data, along with publicly available genomic resources: Step 1) identify genetic differences between reference genomes and genomes of interest (‘Golden Delicious’ vs ‘Granny Smith’ & ‘Bartlett’ vs. ‘D’Anjou’). Step 2) use bioinformatic approaches to update the reference genomes to reflect these differences creating custom, polished references for analysis of gene expression in each of the genomes of interest. Step 3) compare gene expression results from the original and polished versions to calculate changes in read mapping rates focusing on total reads matched and changes in uniquely matched reads (both indicating changes in sensitivity for measuring gene activity) to evaluate the efficacy of the genome polishing strategy.

Using a combination of read mapping, transcriptome assemblies and long read data, and global and local alignment strategies, we will identify differences between the ‘Granny Smith’ and ‘Golden Delicious’ genomes as well as the ‘D’Anjou’ and ‘Bartlett’ genomes. We will focus our efforts on coding sequences and cDNA sequences as these are targets for read mapping during digital gene expression. Using custom scripts and utilizing existing bioinformatics tools we will customize existing ‘Golden Delicious’ & ‘Bartlett’ genome references towards improvement of read mapping rates of ‘Granny Smith’ & ’D’Anjou’ RNA-Seq data. Changes in read mapping rates, specifically uniquely mapping reads (a single genome match) will be calculated. These will then be compared to experiments using the existing genomes and statistically analyzed for changes in sensitivity to detect differentially expressed genes.