Development of Computational Models, Biomarkers, and Tools for Postharvest Traits in Malus Domestica Fruit

Understanding Malus domestica (apple) postharvest biology under typical storageconditions is important for ensuring that a high-quality product reaches consumers and for food waste reduction. Despite this, minimal research has been performed investigating the molecular mechanisms at work during storage. The following dissertation uses transcriptomic data and modeling techniques to investigate this biology. Chapter one is a brief literature review on modeling techniques and apple postharvest biology. Chapter two investigates how the core apple hypoxia response differs from other plants and how different postharvest treatments impact apple biology over long-term storage. Chapter three investigates how we can use machine learning models to develop transcriptomic biomarkers for predicting phenotypic traits in apples. Chapter four investigates currently open questions about data quantity and normalization techniques for modeling transcriptomic traits. Finally, chapter 5 reflects on the lessons learned from this research and on my experiences as a Ph.D. student. This research uncovers potential neo-functionalizations of genes, transcriptomic biomarkers, and a better understanding of modeling using transcriptomic data

SystemsGenetics/KINC.R v1.2

Added a new function for detecting phased edges. Also fixed a bug where ranks for edges were returned as strings instead of numeric values

SystemsGenetics/GEMmaker: Release v2.1.0

Major changes in this release: Revision of GEMmaker to use Nextflow\u27s DSL2 syntax extension. Support for nf-core compatibility with DSL2. The addition of the STAR aligner to the list of supported aligners Updates to the documentatio

SystemsGenetics/GEMmaker v1.0

A workflow for construction of Gene Expression count Matricies (GEMs). Useful for Differential Gene Expression (DGE) analysis and Gene Co-Expression Network (GCN) constructio

SystemsGenetics/KINC: Version 3.4.2

Fixed many bugs. Updated documentation. Added additional arguments for power analysis. Added additional checks to categorial test. Improved 3D viewer UI

Overall average hardness after a 7d simulated supply chain (in air at 20°C).

Overall average hardness after a 7d simulated supply chain (in air at 20°C).</p

Fig 6 -

Model performance of a single random forest reduced model (RF-rm) and elastic net reduced model (EN-rm) training A and testing B. training C and testing D. Data for replicated 100 runs of these model is presented in Table 1. Reported r2 and m_rmse values in this figure represent a single run of a representative model, whereas data reported in Table 1 represents the average of 100 replicates.</p

Sampling time points and treatments for 2018 RNA-seq data.

Each time point includes the following samples: 3 biological replicates (6 apples per replicate) used for RNA-Seq, a texture analysis of 9 apple fruit at pullout, and a texture analysis of 9 apple fruit after a 7d ripening period (stored in air at 20°C).</p

[<i>1-column fit image]</i> Random Forest model for the top 15 literature genes selected from the literature gene full model (85 genes).

A is training data and B is testing data. The literature models had reduced performance compared to models using all genes. Data for replicated 100 runs of this model is presented in Table 1. Reported r2 and m_rmse values in this figure represent a single run of a representative model, whereas data reported in Table 1 represents the average of 100 replicates.</p

Supplemental tables S1-S8.

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the ‘Gala’ variety of apples across several postharvest storage regiments. Fruit texture in ‘Gala’ apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments.</div