The rapid development of –omics technologies, including genomics, transcriptomics, proteomics and metabolomics means that big data is at the heart of new discoveries in plant genetics and plant pathology. Bioinformatics is an interdisciplinary field that involves the development and application of computational methods for the analysis of large-scale biological data ranging including the development of database systems, visualization tools and the downstream analysis of experimental data.
Part of the Information and Computational Sciences section at The James Hutton Institute, the Applied Bioinformatics Group, comprises staff with experience in genomics, modelling, software and database development, systems biology and synthetic biology, as well as highly skilled software developers, who can design and implement novel algorithms and flexible analysis & visualization tools, to meet diverse client needs.
Software developed by the Applied Bioinformatics Group at the James Hutton Institute, is used worldwide. This includes desktop applications such as Helium, Tablet and Flapjack; databases including Germinate and mobile apps for the collection of field trial data and analytical pipelines.
Expertise within the Group includes (but is not limited to) pathogen diagnostics, marker development, genome assembly and annotation, high-throughput genotyping, database development, software development, exome capture, RNA-sequencing, small RNAs, regulatory networks, protein engineering, alternative splicing, metabolic reconstruction, mobile applications development, algorithm development, bioinformatics consultancy and bioinformatics training. The Applied Bioinformatics Group applies this expertise and experience to a range of agricultural projects, collaborating with both academic and industrial partners, across a breath of disciplines.
Current projects relate to:
• Crops (including barley, potato, wheat, maize, raspberry, groundnut) • Viral, bacterial and fungal plant pathogens • Pests
This is a unique offering of expertise, specific to agricultural science, but applicable to much more.
Development of tools to aid in the understanding of complex data.
Development of information systems for the storage of biological data.
Development of applications to support data collection and analysis on mobile platforms.
Analysis of genomic and genetic data in crops and pathogens.