Gary Benson <\/a> The Benson lab develops algorithms and software for biological sequence comparison and repeat detection in genomic sequences. The focus is understanding the occurrence and functional effects of tandem repeats (TRs), and especially, those with variable copy number, also known as variable number of tandem repeats (VNTRs). The lab has developed an analysis tool, VNTRseek, to identify VNTRs, using high-throughput sequencing data, but it is limited to those TRs that \ufb01t within a sequencing read. This project will develop new algorithmic and statistical methods to permit detection of longer VNTR repeats and the use of longer read sequencing technologies. Additionally, an online database will be created to store and analyze the variant data. Students will gain knowledge in human genetic variability and DNA repeats, and skills in analyzing high-throughput sequencing data, algorithm design and testing, and database development. <\/div>\n<\/div>\n<\/p>\n Ethan Deyle<\/a> Many of the tools scientists use to quantitatively study the world were developed for engineered systems and laboratory experiments, where a single cause produces a single effect independent of other variables (“linear separability”). Natural systems, whether individual cells or entire ocean ecosystems, do not always follow these expectations. Instead, interactions are often state-dependent, where the action of a cause depends on the context around it (i.e. it depends on the state of other variables). This nonlinear state-dependence can interfere with the comfortable, correlative approaches to studying systems, but also presents rich opportunities. This project will center on applying nonlinear causal inference to identify interaction between scales of complexity in natural systems (e.g. single fish populations and ecosystem functioning or single cell expression and organism physiology). Options are available to focus on applied data study of neuronal gene expression, aquatic food-webs, or marine fishery management. It is also possible to focus entirely on numeric simulation data. Students will gain hands-on experience in data processing, non-parametric statistics, and time-series analysis using R or Python (based on preference). Previous coding experience is not a strict requirement but will affect the scope of the project. <\/div>\n<\/div>\n<\/p>\n Josee Dupuis<\/a> The Dupuis lab develops statistical approaches to identify specific genes or genetic variants that in\ufb02uence complex phenotypes through their associated quantitative traits, which are traits that can be measured numerically, such as height or blood pressure in humans, and seed size or oil content in plants. This project involves developing statistical analyses which combine genome wide association results with prior information from \u201comics\u201d studies (gene variant functionality, gene expression, methylation, metabolomic data, and proteomic data) to determine regions with common or rare genetic variants that are potentially causally associated with traits of interest. Students will become familiar with genetic studies and software for genetic analysis, and will explore publicly available databases to assign putative function to sets of variants. <\/div>\n<\/div>\n<\/p>\n W. Evan Johnson<\/a> The Johnson lab studies the human microbiome, i.e., microbial communities which live in and on the human body and play a vital role in health and disease. This project involves the development of statistical tools and software for jointly analyzing microbial and host data from sequencing experiments, in order to determine community content, microbe-microbe interactions, and host-microbe relationships. Students will help develop tools and work\ufb02ows to compile annotated libraries of genes and genomes, curate functional associations between genes and microbes in metabolism, and link microbial abundance to host gene\/pathway expression and other outcomes. <\/div>\n<\/div>\n<\/p>\n Jennifer Bhatnagar<\/a> The Bhatnagar lab studies soil microbiome variation in the context of changing environmental conditions. Soil microorganisms perform a variety of essential roles, including acting as plant symbionts, animal pathogens, and free-living decomposers that recycle nutrients and carbon through the biosphere. Yet, it is unclear that soil microorgamisms will persist in a changing world. This project will develop new bioinformatics tools to predict which soil microorganisms will endure and remain active over space and time. Microbial DNA sequence data will be collected from soils obtained through a national sampling initiative \u2013 the National Ecological Observatory Network (NEON). The data will be analyzed for gene clusters involving biochemical pathways affecting microbial ecology (e.g., the ability to serve as pathogens or symbionts) and used to develop statistical models that predict variance in microbial function based on location, time, temperature, precipitation, soil nutrient content, and plant biomass. Student will learn key steps in metagenome analysis and methods for data visualization. <\/div>\n<\/div>\n<\/p>\n Michael Dietze<\/a> The Dietze lab uses a combination of ecological theory, informatics, statistics, and cyberin-frastructure development to advance the \ufb01eld of predictive ecology, and iterative forecasting, in which new data are used to re\ufb01ne predictive models. Current application areas include: soil microbes, vegetation phenology, land carbon and water \ufb02uxes, aquatic productivity, and algal blooms. This project involves the development of computational forecasting work\ufb02ows, including modules for expansion to new data repositories and forecasting data types, statistical model calibration and validation, and forecast visualization. Students will learn about ecological forecasting, high-performance and cloud computing, software containerization, real-time work\ufb02ow automation, databases, and the statistics of iterative model-data assimilation. <\/div>\n<\/div>\n<\/p>\n Joshua Campbell<\/a> The Campbell lab focuses on developing computational methods for characterizing cellular heterogeneity in gene expression using single cell RNA sequencing. Tools include CELDA (CEllular Latent Dirichlet Allocation), which identi\ufb01es hidden transcriptional states and cellular subpopulations in count-based, single-cell RNA-seq data, and DecontX, which estimates contamination by ambient RNA in single cell data. This project involves analyzing publicly available single cell datasets to test and develop new methods of single cell analysis. Students will learn about RNA sequencing for bulk tissue and single cell samples, and will help develop analysis pipelines and data visualizations in the R programming language with the R\/shiny graphical user interface. <\/div>\n<\/div>\n<\/p>\n Sarah W. Davies<\/a> The Davies lab studies how corals and their symbiotic algae maintain and lose symbiosis under varying environmental conditions. Corals meet the majority of their energy needs through life-long symbiotic relationships with single-celled algae. Loss of this relationship leads to coral bleaching and, eventually, colony death. Some corals have a facultative relationship with their algal symbionts, wherein both the host and symbiont can be cultured independently and manipulated in and out of symbiosis. This project involves analyzing gene expression, and in particular, orthologous gene covariance, in such facultative systems under baseline and stress conditions, to help elucidate maintenance and loss of symbiosis. It will use a holobiont (coral + algae) transcriptome developed in the Davies lab. Students will develop knowledge and skills related to ecology, evolution, and RNA-seq analysis. Since stress experiments are ongoing, the project may include an experimental component. <\/div>\n<\/div>\n<\/p>\n Karen Allen<\/a> The Allen lab explores the relationship between protein structure and function using X-ray diffraction and enzyme kinetic studies. In bacteria, a principal mechanism for glycan (complex sugar molecule) assembly on the cytoplasmic face of cell membranes involves polyprenol phosphate (PrenP) phosphoglycosyl transferases (PGTs). PGTs catalyze transfer of a phosphosugar to a membrane-bound PrenP acceptor. Recently, the lab solved the X-ray crystallographic structure of the PGT PglC from Campylobacter concisus, showing that it contains a re-entrant membrane helix (RMH) that penetrates only one lea\ufb02et of the bilayer then re-emerges on the cytoplasmic face. This contradicts computational prediction that the RMH is a transmembrane helix. This project involves developing hidden Markov models (HMM) to predict these \u201cmisannotated\u201d helices in other protein families using data from an in vivo cysteine labeling method to assess whether the N-terminus lies on the cytoplasm or periplasm side of the membrane. Students will gain exposure to protein chemistry, enzyme functional studies, chemoinformatic library analysis, sequence and structural alignment methods and HMM modeling techniques. Since the cysteine labeling studies are ongoing, this project may include an experimental component. <\/div>\n<\/div>\n<\/p>\n Prasad Patil<\/a> I work with sets of datasets that measure the same outcome and overlapping sets of features in multiple patient cohorts. Generally, these are datasets within which patient survival (outcome) and gene expression measurements of 20,000+ genes (features) are recorded for ovarian or breast cancer patients. The goal is to train a prediction rule for risk of cancer progression or recurrence that performs well across datasets and generalizes well for new patients. Oftentimes, there are far more predictors than patients in each dataset, so feature filtration and selection prior to training a prediction rule is a necessary step. A prevailing question is how to ensure that we select features which predict well across studies and avoid features that only perform exceedingly well within a single study. Students will gain experience working with high-dimensional genomic datasets and feature selection and machine learning approaches implemented in the R programming language. <\/div>\n<\/div>\n<\/p>\n Chunyu Liu<\/a> The Liu lab develops statistical approaches and applies those methodologies to identify genetic and life style factors that influence complex phenotypes. Two projects are available: 1) Mitochondrial DNA (mtDNA) sequencing project: Mitochondria are power house in human cells. mtDNA is involves in the major pathway for power production. Students will have the opportunity to use publicly available software to identify mutations in the mitochondrial genome (mtDNA) from whole genome sequencing data in human. In addition, they will also have the opportunities to perform association analysis of the mtDNA mutations with cardiovascular disease. 2) Gene expression and alcohol consumption project: Gene expression is the process by which information in a gene is used to generate messenger RNA (mRNA) for protein production. Students will have the opportunity to identify genes that are related to alcohol consumption. In addition, students will explore gene pathway analyses to identify gene networks that are related to alcohol consumption and cardiovascular disease. <\/div>\n<\/div>\n<\/p>\n Cynthia Bradham<\/a> Single-cell RNA sequencing technologies are extraordinarily powerful for dissecting cell compositions in heterogeneous cell mixtures in single biological conditions. However, complications arise when multiple biological conditions are introduced — such as disease status or drug treatment. We have developed a novel algorithm ICAT to more accurately identify shared, as well as distinct, cell-types between treatments in scRNAseq data. Potential BRITE students could look forward to contributing to new features in ICAT including identifying stably expressed genes between treatments, testing performance across different implementations, and creating a Seurat wrapper. Students would get first-hand experience using machine learning to parse large datasets, implementing high performance Python code, and exposing Python packages to R using the reticulate package. Students will also make heavy use of the linux command line and git. This project will be perfect for students interested in algorithm development, Python and R programming, and machine learning, while working at the very intersection of mathematics, computer science, and developmental biology. <\/div>\n<\/div>\n<\/p>\n <\/p>\n <\/p>\n Joshua Campbell<\/a> High-throughput genomic technologies are rapidly evolving including the areas of DNA and RNA sequencing. Novel types of complex data are being quickly generated and require novel methods for quality control and analysis. We are currently focused on developing and\/or applying methods for identifying genomic alterations in cancer, quantifying the mutagenic effect of carcinogens, and characterizing cellular heterogeneity using single cell RNA sequencing. For example, we have developed the Celda framework (CEllular Latent Dirichlet Allocation), which can be used to identify hidden transcriptional states and cellular populations in count-based single-cell RNA-seq data. This project will include application of Celda to new and\/or publicly available single cell datasets using the Single Cel ToolKit \u2013 an interactive R\/shiny app.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Sarah Davies<\/a> Our lab is interested in\u00a0understanding the diversity and dynamics of coral-algal symbiosis. Unfortunately, this symbiotic relationship breaks down in response to\u00a0thermal stressors associated with climate change in a phenomenon known as coral bleaching.\u00a0The current project will utilize RNA-sequencing data to determine how gene expression responds to thermal stress in coral and their algal symbionts. This project will utilize various bioinformatic tools and statistical approaches to disentangle which genes and gene networks are impacted due to thermal stress. The student will ultimately help address questions as to how algal-coral symbiosis operates, and why this relationship breaks down under thermal stress, which is relevant now more than ever under a changing climate.<\/p>\n <\/div>\n<\/div>\n<\/p>\n W. Evan Johnson\u00a0<\/a> Microbial communities which live in and on the human body play a vital role in health and disease. The simultaneous study of microbial function and associated host response is key for understanding metabolism in healthy people and pathogenesis in diseased individuals. Recent innovations in sequencing technologies have enabled the profiling of microbial communities, their function, and the interrogation of host-pathogen interactions at a deeper resolution than ever before.<\/p>\n However, due to the heterogeneity of the microbiome across individuals and the complexity of microbes\u2019 interactions with each other and their hosts, these analyses require advanced computational and statistical techniques and current tools for this purpose are not sufficient. We are developing flexible and powerful statistical tools and software for jointly analyzing microbial and host data from microbiome experiments.<\/p>\n These tools include workflows to compile annotated libraries of genes and genomes, the curation of functional associations between genes and microbes in metabolism and immune response, and linkage of microbial abundance to host gene\/pathway expression and other outcomes. Our software package will also provide a user-friendly R\/Shiny interface with interactive graphics and analysis tools, making the pipeline accessible to users without strong computational backgrounds. We are applying our methods in the context of multiple existing and prospective studies in obesity, kidney disease, lung cancer, and infectious diseases such as HIV and tuberculosis. These studies will result in a deliberate and focused effort to develop a greater understanding of the microbial communities the cohabitate human systems, including the community content, microbe-microbe interactions, and host-microbe relationships.<\/p>\n Researchers on this project will aid in the development of statistical tools and software, and support the analysis of data from microbiome studies from multiple diseases.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Paola Sebastiani<\/a> Dr Sebastiani is the statistician of two studies of human longevity: the New England Centenarian Study 1 and the Long Life Family Study2. Both studies aim to discover the genetic and environmental factors that promote long and healthy lives. The two studies are complementary in terms of data structure (one is a population based study with more than 2,000 centenarians and one is a family based study with approximately 550 families demonstrating clustering for exceptional longevity). Both studies, funded by the National Institute on Aging, investigate genetic and environmental factors that affect aging and individual susceptibility to age-related diseases and disability. Key long term objectives of the studies are<\/p>\n (1) Identify genetic risk factor as well as life styles that can effect aging and use this information to design interventions that reduce the burden of morbidity and mortality in older people. <\/div>\n<\/div>\n<\/p>\n Jennifer Bhatnagar<\/a> Soil microorganisms have a variety of functions in our natural ecosystems \u2013 from acting as plant symbionts to animal pathogens to free-living decomposers that break down dead material (like plant litter) and recycle nutrients and carbon through the biosphere. This project will develop new bioinformatics tools to answer the questions, \u201chow well will our natural ecosystems function in the coming century and do we know enough about microorganisms in the environment to predict which ones will persist \u2013 and how active they will be \u2013 over space and time?\u201d We will obtain microbial DNA sequence data collected from soils across a new national sampling initiative \u2013 the National Ecological Observatory Network (NEON). This data will be analyzed for biosynthetic clusters that code for specific biochemical pathways in microbial genomes that affect their ecology (e.g. ability to serve as pathogens or symbionts). The data will be used to develop a statistical model that partitions the variance in microbial function on orthogonal axes of space and time, as well as across environmental variables like climate (temperature, precipitation), soil nutrient content, and plant biomass.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Andrew Emili<\/a> Dr. Emili develops and applies advanced proteomic, molecular genetic, genomic and bioinformatic technologies to investigate the molecular associations and biological roles of the many varied macromolecules present in different cells, tissues and organisms. His group aims to generate comprehensive maps of the physical and functional \u201cinteractomes\u201d of informative model organisms.\u00a0 These maps are expected to lead to breakthrough mechanistic understanding of how cells and tissues function at a fundamental molecular level, and serve as valuable resources for the broader research community. Ultimately, the aim is to translate this basic knowledge into novel diagnostic and therapeutic tools, with an emphasis on cancer, cardiovascular disease and neurodegenerative disorders.\u00a0 Students on this project will aid in the development of software and tools that support the analysis of interactome data.<\/p>\n <\/div>\n<\/div>\n<\/p>\n <\/p>\n <\/p>\n Gary Benson<\/a> The Benson lab develops algorithms to detect genetic variation using next-generation sequencing data. Our main focus is human genetic variation in tandem repeat sequences, called variable number of tandem repeats (VNTRs) and large structural variations (SVs), including inversions, translocations, duplications, insertions and deletions. We use publicly available human sequencing data, especially high coverage, long read data, to detect and characterize variants. We are interested in where the variants occur, their frequency in populations, and how they may affect gene function or chromosome function. Besides detection tools, we also develop online databases to store and share information about the variants we detect. A variety of student projects related to this work are available.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Michael Dietze<\/a> The world is changing in many ways that impact ecosystems and the essential services they provide to human health and well-being. In the face of such change, it is imperative that scientists provide the best available information about likely impacts, and responses to decision alternatives, to help society both anticipate and adapt to change. Ecological forecasting is a transformative, interdisciplinary research area concerned with predicting the future states and distributions of ecosystems and their services to humans. This project involves an automated workflow for integrating multiple data streams into predictive models. Students will have to opportunity to contribute to the overall cyberinfrastructure (R packages, Docker+RabbitMQ stack, database) and to active forecast projects in: tick-borne disease, ticks, and small mammal hosts; harmful algal blooms; land surface CO2 and water fluxes; leaf phenology.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Kirill Korolev<\/a> Humans like all other animals and plants are colonized by thousands of microbial species. This microbiome helps us digest food, trains our immune system, protects us from pathogens, but also plays an important role in disease. Detecting species responsible for diseases is a major goal in microbiome research. This project will study how the microbiome changes along the GI tract in controls and patients with different inflammatory diseases of the digestive system. One of the goals will be to develop prognostic and diagnostic biomarkers that can distinguish disease subtypes or guide the choice of treatment.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Paola Sebastiani<\/a> Dr Sebastiani is the statistician of two studies of human longevity: the New England Centenarian Study 1 and the Long Life Family Study2. Both studies aim to discover the genetic and environmental factors that promote long and healthy lives. The two studies are complementary in terms of data structure (one is a population based study with more than 2,000 centenarians and one is a family based study with approximately 550 families demonstrating clustering for exceptional longevity). Both studies, funded by the National Institute on Aging, investigate genetic and environmental factors that affect aging and individual susceptibility to age-related diseases and disability. Key long term objectives of the studies are <\/div>\n<\/div>\n<\/p>\n Jennifer Bhatnagar<\/a> Soil microorganisms have a variety of functions in our natural ecosystems \u2013 from acting as plant symbionts to animal pathogens to free-living decomposers that break down dead material (like plant litter) and recycle nutrients and carbon through the biosphere. This project will develop new bioinformatics tools to answer the questions, \u201chow well will our natural ecosystems function in the coming century and do we know enough about microorganisms in the environment to predict which ones will persist – and how active they will be \u2013 over space and time?\u201d We will obtain microbial DNA sequence data collected from soils across a new national sampling initiative \u2013 the National Ecological Observatory Network (NEON). This data will be analyzed for biosynthetic clusters that code for specific biochemical pathways in microbial genomes that affect their ecology (e.g. ability to serve as pathogens or symbionts). The data will be used to develop a statistical model that partitions the variance in microbial function on orthogonal axes of space and time, as well as across environmental variables like climate (temperature, precipitation), soil nutrient content, and plant biomass.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Joe Zaia<\/a> The Zaia lab studies protein glycosylation, a mechanism whereby glycans (complex sugar molecules) are attached to proteins through a series of biosynthetic enzyme reactions. Because the reactions do not go to completion, the result is populations of mature protein molecules heterogeneous with respect to glycosylation and diverse with respect to biological activities. Many families of proteins contain domains (known as lectin domains) that bind to glycan substructures (known as epitopes, containing 1-5 monosaccharide units). Glycosylation of a protein strongly influences its binding to other proteins via lectin domains. The lab is studying the mechanisms whereby viruses alter glycosylation of surface glycoproteins in order to escape host immune response. We have generated large datasets measuring the expression of glycans and glycoproteins from virus evolution experiments, using liquid chromatography-mass spectrometry and tandem mass spectrometry. <\/div>\n<\/div>\n<\/p>\n <\/p>\n <\/p>\n Douglas Densmore, PhD<\/strong><\/a> The project involves the engineering of biological systems in a field called synthetic biology. A key challenge in synthetic biology is to control numerous environmental conditions and genetic network interactions. Microfluidics has the potential to address this challenge when used as the platform on which synthetic biological systems are explicitly specified, designed, built, and tested. Undergraduate researchers will work on algorithmic methods for designing microfluidic primitives by extracting analytical models from the datasets of mass-prototyped microfluidic chips developed in the CIDAR Lab.<\/p>\n <\/div>\n<\/div>\n<\/p>\n Jos\u00e9e Dupuis, PhD<\/strong><\/a> <\/div>\n<\/div>\n<\/p>\n Kirill Korolev, PhD<\/strong><\/a> <\/div>\n<\/div>\n<\/p>\n
\nDepartments of Biology and Computer ScienceGenetic variation and linkage to phenotype<\/h4>
\n
\nDepartment of BiologyQuantifying cross-scale interaction in complex natural systems<\/h4>
\n
\nDepartment of BiostatisticsFine-mapping of genetic loci for quantitative traits<\/h4>
\n
\nDepartments of Biostatistics and MedicineProfiling human microbial communities<\/h4>
\n
\nDepartment of BiologyEcological forecasting: Predicting changes in soil microorganisms<\/h4>
\n
\nDepartment of Earth and EnvironmentNear-term ecological forecasting<\/h4>
\n
\nDepartment of Computational BiomedicineSingle cell transcriptomics<\/h4>
\n
\nDepartment of BiologyGenes and pathways regulating symbiosis in corals<\/h4>
\n
\nDepartment of ChemistryDetermining modes of protein-membrane interaction<\/h4>
\n
\nDepartment of BiostatisticsMulti-Study Feature Selection<\/h4>
\n
\nDepartment of BiostatisticsGenetic and Life Style Factors for Complex Phenotypes<\/h4>
\n
\nDepartment of BiologyIdentifying Cell-Types Across Treatments in Single-cell RNA Sequencing Data<\/h4>
\n
\n2019<\/h3>\n
\n
\nDepartment of Medicine
\nDivision of Computational Biomedicine<\/p>\n<\/strong>Single Cell Sequencing Analysis<\/strong><\/h4>
\n
\nDepartment of Biology<\/p>\n<\/strong>Coral symbiosis and Climate Change<\/strong><\/h4>
\n
\nDepartments of Medicine and Biostatistics<\/p>\n<\/strong>Tools and software for profiling microbial communities in multiple human diseases<\/strong><\/h4>
\n
\nDepartment of Biostatistics<\/p>\n<\/strong>Biomarkers of Healthy Aging<\/strong><\/h4>
\n(2) Translate the discoveries from genetic studies into risk prediction models that can help identify genetic signatures of healthy aging and their interaction with the environment.
\n(3) Identify biomarkers of aging that can be used as prognostic and diagnostic tools.<\/p>\n
\n
\nDepartment of Biology<\/p>\n<\/strong>Predicting changes in the Earth microbiome; near-term ecological forecasting of critical soil microorganisms<\/strong><\/h4>
\n
\nDepartment of Biochemistry<\/p>\n<\/strong>Biomolecular Interactomes<\/strong><\/h4>
\n
\n2018<\/h3>\n
\n
\nDepartments of Biology and Computer Science<\/p>\n Algorithms for detecting genetic variation<\/strong><\/h4>
\n
\nDepartment Earth & Environment<\/p>\n Near-term forecasting of ecological processes: integrating multiple data streams and models<\/strong><\/h4>
\n
\nDepartment of Physics<\/p>\n Temporal and spatial variation of microbiota in inflammatory bowel disease<\/strong><\/h4>
\n
\nDepartment of Biostatistics<\/p>\n Biomarkers of Healthy Aging<\/strong><\/h4>
\n(1) Identify genetic risk factor as well as life styles that can effect aging and use this information to design interventions that reduce the burden of morbidity and mortality in older people.
\n(2) Translate the discoveries from genetic studies into risk prediction models that can help identify genetic signatures of healthy aging and their interaction with the environment.
\n(3) Identify biomarkers of aging that can be used as prognostic and diagnostic tools.<\/p>\n
\n
\nDepartment of Biology<\/p>\n Predicting changes in the Earth microbiome; near-term ecological forecasting of critical soil microorganisms<\/strong><\/h4>
\n
\nDepartment of Biochemistry<\/p>\n Glycan Abundance Imputation through Clustering and Machine Learning<\/strong><\/h4>
\nREU students will design and implement computer programs to cluster glycan compositions, which they will then use as composite baselines for imputing missing glycopeptide abundances. They will perform and evaluate the imputation through machine learning algorithms; this will build off of published data and currently unpublished work within the lab. Students will learn how glycans modulate interactions with glycan binding proteins, and will gain experience with data structures, statistical evaluation, and scientific programming in the context of clustering algorithms and machine learning methods.<\/p>\n
\n
\n2017<\/h3>\n
\n
\n<\/strong><\/p>\n Data-Driven Methods for Automated Design of Lab on a Chip Devices<\/h4>
\n
\n<\/strong><\/p>\n An integrated genomics information resources platform <\/h4>
\nGenome-wide association analyses have identified a large number of genetic variants associated with complex traits. However, most susceptibility variants discovered to date fall outside of gene regions, and identifying the true causal genetic variants remains a challenge. The goal of this project is to build an annotation pipeline that will integrate information from various public genomics databases to help prioritize functional studies on genetic variants found to be associated with one or several traits of interest.<\/p>\n
\n
\n<\/strong><\/p>\n Temporal and spatial variation of microbiota in inflammatory bowel disease <\/h4>
\nHumans like all other animals and plants are colonized by thousands of microbial species. This microbiome helps us digest food, trains our immune system, protects us from pathogens, but also plays an important role in disease. Detecting species responsible for diseases is a major goal in microbiome research. This project will study how the microbiome contributes to inflammatory bowel disease (IBD) and develop prognostic and diagnostic biomarkers for this disease. Unlike most previous studies, we will analyze not only bacterial, but also fungal and archaeal communities because we previously found strong microbial imbalance in the fungal as well as bacterial communities.<\/p>\n
\n