Xuan Guo | Computer Science and Engineering

High-throughput omics technologies are revolutionizing many aspects of modern biology. We are entering the era of Big Data in biology research. The massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including storage bottleneck, measurement errors, noise accumulation, and spurious correlation.

We study data mining, machine learning, big data analysis and data fusion. The primary focus of our research is on developing advanced frameworks and algorithms to help biologists solve practical problems in systems biology, biomedicine, and natural sciences and enable them to make full use of massive, high-dimensional data for various biological inquiries.

Metaproteomics Search

Microbial communities drive nutrient cycling in aquatic and terrestrial ecosystems and influence the health of human, animal, and plant hosts. The metabolic activities of a microbial community can be inferred from the proteomes of its constituent microorganisms. In a typical metaproteomics experiment, total proteins are extracted from environmental samples of a microbial community and then measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a “shotgun” proteomics approach. Thousands of organisms in complex communities lead to a much smaller number of peptide and protein identifications in metaproteomics analyses of complex communities than comparable proteomics analyses of single organisms. In this study, we aim to develop novel database searching and filtering algorithms.

Software: Sipros Ensemble.

Genome-wide Association Studies

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. In this project, we design fast and powerful methods detect genome-wide multi-locus epistatic interactions across multiple cases.

Software: DCHE, DAM, MSCD.

Sequence Assembly

Next-generation sequencing platforms not only decrease the cost of metagenomics data analysis but also greatly enlarge the size of metagenomic sequence datasets. A common bottleneck of available assemblers is that the trade-off between the noise of the resulting contigs and the gain in sequence length for better annotation has not been attended enough for large-scale sequencing projects, especially for the datasets with moderate coverage and a large number of nonoverlapping contigs. To address this limitation and promote both accuracy and efficiency, we develop a novel metagenomic sequence assembly frameworks and algorithms by taking advantages of high-performance computer and cyberinfrastructure.

Software: DISCO, DIME.

fMRI Analysis

An intriguing quest regarding the brain science is: what are the origin and the principles behind the functional architectures, which define who we are and what we are to a great extent. Compared to other methods, functional Magnetic Resonance Imaging (fMRI) is one of the most common ways that can explore the functional activities of the whole brain. After decades of active research, there have been numerous evidence that the brain function is realized and emerges from the interaction of multiple concurrent neural processes or brain networks. We design novel statistic models to analyze functional brain dynamics among the nodes of brain networks along time periods.

Software: BCCPM.