Extracting Signal from Noise: Big Biodiversity Analysis from High-Throughput Sequence Data
Surveying biodiversity is critical for environmental health and for managing natural resources. It helps to assess the impact of resource development, but also to identify pests, invasive species, and pathogens in a rapid and cost-effective manner. It is essential to Canada’s economic growth in the forestry, agriculture, and fishery sectors and to decision-making in public health. Genetic methods of surveying biodiversity, such as high-throughput sequencing, are being broadly adopted, but bioinformatics has not kept pace with the data being generated. In addition, current methods are geared toward bacteria and similar organisms, rather than multi-celled plants and animals that need monitoring as well.
Drs. Sarah Adamowicz and Paul Hebert, along with colleagues from the University of Guelph, are creating new bioinformatics tools that will facilitate the rapid and accurate processing of DNA data resulting from high-throughput sequencing. The tools will enable the simultaneous analysis of bulk samples, which are made up of many different species. It will include a de-noising tool to detect errors; a method to cluster DNA sequences into species-like units to permit biodiversity analysis; and a method for assigning sequencing data to higher taxonomic categories to unlock functional biological information. The team will combine these various tools into a biodiversity informatics pipeline that can be incorporated into existing web-based platforms for uptake by a broad variety of users.
The new biodiversity informatics tools will support large-scale biodiversity research by academics; efficient, accurate, and cost-effective environmental assessments for the mining and pulp-and-paper industries; enhanced capacity and accuracy of regulation; and more rapid and accurate biodiversity data for government and private-sector decision-makers.