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Department of Mathematics & Statistics

Upcoming seminars in Statistics

Seminars in Mathematics
What on Earth is this? Applying deep learning for species recognition

Varvara Vetrova

University of Canterbury

Date: Thursday 19 September 2019
Time: 11:00 a.m.
Place: Room 241, 2nd floor, Science III building

Can we use a smartphone camera, take a picture of an animal or a plant in the wild and identify its species automatically using convolutional neural networks? What about very similar-looking or rare organisms? How many images are enough? This talk will try to reveal some answers to these questions. This talk is based on the MBIE-funded research project "BioSecure-ID".

Making Better Use of Genotyping-by-Sequencing Data: SNPs vs. ShortHaps

Jie Kang

Mathematics and Statistics, University of Otago

Date: Thursday 26 September 2019
Time: 11:00 a.m.
Place: Room 241, 2nd floor, Science III building

Advances in sequencing technologies enable us to characterise variation in the genome of non-model but agriculturally important species. Approaches such as Genotyping-by-Sequencing (GBS) can produce abundant markers at relatively low cost. This has encouraged implementation of Genomic Selection (GS) to accelerate genetic gains in plant breeding, but has also raised the challenge of how to make better use of the genomic information. Unlike animal breeding, where high-quality reference genomes and well-developed modelling strategies already exist, a versatile analysis pipeline is needed for out-breeding plant species, such as perennial ryegrass (Lolium perenne). In addition, we want approaches that take the highly polymorphic nature of ryegrass into consideration when analysing (low-depth) GBS data. We thus hypothesise that existing GS models can be enhanced by accounting for short haplotypes or 'ShortHaps', that is, multiple variants in small genomic segments such as those captured within a GBS read. In this talk I will describe the bioinformatics workflows associated with ShortHaps calling and discuss why ShortHaps should work better than SNPs in terms of breeding value predictions and relatedness estimation.