## 3 Results
### Biological Use Case 1: Comparative Genomics to Study Tomato Fruit Shape and Potato Tuber Shape
Metabolomic research studies in the past, have been able to predict functional characteristics of metabolites in the life-cycle of a plant. For example, volatiles are known to play an important role in the defense mechanism of plants against pathogens, where they serve as airborne signaling molecules to induce a defense response in other plant parts or neighboring plants [61]. Similarly, other metabolites like soluble solids (glucose, fructose, and sucrose) contribute to the sweetness of a fruit [62]. Lycopene is a carotenoid compound found in tomatoes which contributes to the nutritional value of a tomato and the red pigment in tomatoes responsible for fruit-color [63]. Similarly, terpenids play a role in attracting pollinators [64]. Finding candidate genes within QTL regions for the trait of interest using computational approaches is a major challenge in plant bioinformatics. Several tools have been developed in the past that tried to prioritize candidate genes based on existing knowledge. QTLSearch is a software that searches for candidate causal genes in QTL studies by combining Gene Ontology annotations across many species and leveraging hierarchical orthologous groups [65]. QTG-Finder is a recently published tool that uses a machine learning model to prioritize candidate genes using function annotation, co-function network, and paralog copy number [66]. However, both tools have been developed in _Arabidopsis thaliana_ and in rice (_Oryza sativa_), and are difficult to use and test in other species like tomato and potato. QTLSearch uses the HOGPROP algorithm that requires access to hierarchical orthologous groups available in OMA browser [67], to score candidate genes based on trait-related GO terms. As the OMA Browser data graph is cross-referenced in Uniprot, which is part of pbg-ld, the QTLSearch algorithm can be tested on tomato/potato data with the help of pbg-ld. Figure 7 illustrates a candidate gene prediction workflow within a QTL region for the trait of interest, using function annotations and evolutionary genomics data. Input to this workflow is either a QTL region (containing physical location or a genetic location) or a trait of interest. If the input parameter is a trait of interest, the pbg-ld database retrieves all QTL locations for that trait in tomato. After receiving the QTL inputs, this workflow queries the set of all genes occurring within this QTL region. For every gene, this workflow retrieves a set of all GO terms as well as all orthologs and paralogs of these genes. These genome annotations are served as input to the QTLSearch pipeline, which uses the HOGPROP algorithm. This algorithm assigns scores to every gene within a QTL region. It uses GO terms which relate to the trait of interest and GO terms which relate to the genes within a QTL region to assess the distance of these functional annotations along gene phylogenies. This workflow has been developed in a Jupyter Notebook [56]. It is a modular framework in which data are fetched from multiple data sources and can also accommodate new analysis modules as they are being developed by our group or the scientific community. To test the usability of our workflow in predicting candidate genes for metabolic traits, we selected five QTLs for different metabolic traits, i.e, Brix/soluble solids, lycopene beta-cyclase activity and 2-phenylethanol/phenylacetaldehyde (Table 2). Out of the selected five QTLs for metabolic traits, three QTLs which relate to the following traits, soluble solids, lycopene beta-cyclase activity and phenolic compounds (2-phenylethanol and phenylacetaldehyde), have known candidates. Further, these candidate genes are already annotated with related GO terms in publicly available databases such as UniProt. While, for one of the QTL regions which relates to terpenoids, Terpene synthase is a known candidate gene, however, this synthase is not annotated with GO terms which show an association with the trait of interest (i.e., terpenoids). The two GO terms related to this gene are DNA binding and DNA methylation.