Augmenting Genomic Applications Through Simulation and Machine Learning-based Parameter Optimization

Li, Minghao

Augmenting Genomic Applications Through Simulation and Machine Learning-based Parameter Optimization

dc.contributor.advisor	Long, Quan
dc.contributor.advisor	Yang, Guang
dc.contributor.author	Li, Minghao
dc.contributor.committeemember	De Koning, A. P. Jason
dc.contributor.committeemember	Rancourt, Derrick E.
dc.date	2020-06
dc.date.accessioned	2020-04-07T15:02:00Z
dc.date.available	2020-04-07T15:02:00Z
dc.date.issued	2020-04-03
dc.description.abstract	Certain methods for genomic analyses do not take advantage of the full extent of available biological context to address limited sample sizes. Another noted issue is the gulf in bioinformatics software performance between tool authors and end-users. This project explores two cases where genomic applications can be augmented: power estimations for low-prevalence condition studies and haplotype reconstruction. Power is a key statistic for predicting the success of genomic sequencing projects. Low-prevalence conditions are not amenable for usage with existing power estimation frameworks designed for common conditions and consequently will appear underpowered. SimPEL is a tool for simulation-based power estimation for sequencing studies of low-prevalence conditions. It meets an unmet need in the field and augments power estimation through the inclusion of unused genomic aspects of low-prevalence conditions. Elements of low-prevalence condition studies are input into SimPEL and a simulated cohort is applied to calculate the likelihood of identifying the true causal gene(s). SimPEL demonstrates competitive performance on single causal gene conditions and viable performance in instances of heterogeneity. PoolHapX is a haplotype reconstruction tool capable of reconstructing haplotypes and their corresponding frequencies from mixed populations. Its extensive parameter set is laborious to optimize by hand when applied toward unknown use-cases. Pattern recognition algorithms allow for the delegation of this parameter tuning process to machine learning. SLiM, an evolutionary simulation framework, provides the biological basis for the haplotype reconstruction task. Stochastic simulation of PoolHapX parameter values within a defined space generates the large-scale datasets required for supervised learning. Mapping genomic sequencing features to an optimal PoolHapX parameter set is a multi-task learning problem. A novel two-model scaffold has been designed to address this. A gradient boosted decision tree model, mapping PoolHapX parameter sets to a quantitative performance metric, is nested as the cost function of a multi-head feedforward neural network, which in turn takes an input set of summary statistics from aligned genomic data and outputs PoolHapX parameters. Hyperparameter tuning is enabled by Bayesian optimization techniques. This workflow and framework is parallely extendable toward any PoolHapX extension in the future.	en_US
dc.identifier.citation	Li, M. (2020). Augmenting Genomic Applications Through Simulation and Machine Learning-based Parameter Optimization (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/37669
dc.identifier.uri	http://hdl.handle.net/1880/111782
dc.language.iso	eng	en_US
dc.publisher.faculty	Cumming School of Medicine	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Machine Learning	en_US
dc.subject	Deep Learning	en_US
dc.subject	Simulation	en_US
dc.subject	Parameter Optimization	en_US
dc.subject	Power Estimation	en_US
dc.subject.classification	Bioinformatics	en_US
dc.title	Augmenting Genomic Applications Through Simulation and Machine Learning-based Parameter Optimization	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Medicine – Biochemistry and Molecular Biology	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US