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Poster presented at the Dutch Bioinformatics & Systems Biology Conference, BioSB 2020, on October 27th-28th, 2020., This work was supported by the Netherlands eScience Center (Grant Number: 027016G03) and was carried out on the Dutch national e-infrastructure with the support of SURF Foundation.

This is a guide to software development and projects at the Netherlands eScience Center. It both serves as a source of information for exactly how we work at the eScience Center, and as a basis for discussions and reaching consensus on this topic.

Poster presented at the Dutch Bioinformatics & Systems Biology Conference, BioSB 2019, in Lunteren, the Netherlands, on April 2nd-3rd, 2019.

Genetics research is focusing more and more on mining fully sequenced genomes and their annotations to identify the causal genes associated with specific traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each with hundreds of genes positively or negatively affecting the desired trait(s). To help breeders to rank candidate genes, we developed an analytics platform that provides semantically integrated geno- and phenotypic data on Solanacea species. This Linked Data platform combines both unstructured data from scientific literature and structured data from publicly available biological databases. In particular, QTLs were extracted from tables of (open access) articles using our recently developed tool called QTLTableMiner++ while the genomic annotations were obtained from the Sol Genomics Network (SGN), UniProt, and Ensemble Plants databases. The resulting RDF graphs include cross references to many other relevant databases such as Gramene, Plant Reactome, InterPro and KEGG (KO) etc. Users can query or analyse the linked datasets through a web interface, SPARQL and RESTful services (APIs). Our aim is to provide a plant-oriented resources according to FAIR principles that aids breeders in predicting candidate genes for complex traits using available knowledge in the databases and literature.

Poster presented at the Dutch Bioinformatics & Systems Biology Conference, BioSB 2018, in Lunteren, the Netherlands, on May 15th and 16th, 2018.

Genetics research is focusing more and more on mining fully sequenced genomes and their annotations to identify the causal genes associated with specific traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each with hundreds of genes positively/negatively affecting the desired trait(s). Our aim is to develop a Big data analytics & semantic interoperability infrastructure for candidate gene prioritization that will aid breeders in the design of an optimal genotype with a desired trait(s) for a given environment.

PIQMIe is a web-based proteomics tool that aids in reliable and scalable data management, analysis and visualization of semi-quantitative mass spectrometry based experiments., {"references":["http://piqmie.biotools.nl/"]}