Improving Bioinformatic Methods For Studying Gene Regulation In Health And Disease
Funder
National Health and Medical Research Council
Funding Amount
$463,652.00
Summary
New methods for analysing genome-wide data will be developed to ease the data analysis bottleneck that currently exists in medical research. Modelling variation in gene expression from single cells, in screens designed to uncover gene function and assays that measure the factors that turn genes on or off will be the focus. Free software will be developed and made available to researchers worldwide to help them interpret the large and complex data sets that are now routine in genomic medicine.
The Stemformatics gene expression compendium: development of multivariate statistical approaches for cross platform analyses. Scientific data is gathered in many different forms, but there are significant gaps in our ability to analyse multiple datasets when generated on different pieces of equipment. This project will study three typical research questions in stem cell biology to develop new analytical approaches to help solve this major data gap.
High-throughput genetic assays are commonly used to study the molecular basis of disease and such technology requires sophisticated data analysis methods that account for significant biological and experimental complexity. Specialized methods will be developed in free public software that will greatly benefit future genetic profiling studies.
Vertically integrated statistical modelling in multi-layered omics studies. This project will develop an adaptive statistical modelling framework that uses information from many omics data to discover a collection of stable and clinically significant biomarkers. Results will enable researchers to better understand the underlying biological system of complex diseases such as cancer, Alzheimer and diabetes.
Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically an ....Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically and clinically significant discoveries in biomedical research. This project will help Australian researchers in statistics and users of statistics (from fields as diverse as biology, ecology, medicine, finance, agriculture and the social sciences) to make better predictions that are easier to understand.Read moreRead less
Evolution and functional impact of gene silencing by hairpin derived RNAs. This project aims to study RNA-mediated gene silencing in genome evolution. RNA interference (RNAi) has been widely used as an experimental tool since its Nobel Prize-winning discovery in 1998, but little is known about endogenous RNAi or its evolution. This project uses bioinformatics, high-throughput sequencing and molecular approaches to study hpRNAs, a class of small interfering RNAs, their adaptive evolution across f ....Evolution and functional impact of gene silencing by hairpin derived RNAs. This project aims to study RNA-mediated gene silencing in genome evolution. RNA interference (RNAi) has been widely used as an experimental tool since its Nobel Prize-winning discovery in 1998, but little is known about endogenous RNAi or its evolution. This project uses bioinformatics, high-throughput sequencing and molecular approaches to study hpRNAs, a class of small interfering RNAs, their adaptive evolution across fly species and vertebrates, and their functional effect on testis morphogenesis and distortion of female/male sex-ratio. The project also studies splicing-dependent small RNAs and miRNA-target interaction. This research could have applications from animal development to human pathology.Read moreRead less