This page is updated annually. Some projects may already be taken, and new projects may be available. The projects below give an indication of the types of projects available in each lab, but please browse faculty web pages and contact professors directly to discuss current opportunities.
Also check the Undergraduate Research Hub and other campus undergraduate research programs.
We are interested in the analysis and modeling of the three-dimensional chromatin structure from high-throughput sequencing experiments. We develop methods that are based in statistics, machine learning, optimization and graph theory to understand how changes in the 3D genome affect cellular outcome such as development, differentiation and gene expression. We have ongoing interests in the systems level analysis and reconstruction of regulatory networks, inference of enhancer-promoter contacts, predictive models of gene expression and integration of three-dimensional chromatin structure with one-dimensional epigenetic measurements in the context of cancer, malaria, asthma and several autoimmune diseases.
This project will focus on developing regulatory network inference methods for the joint analysis of gene expression and histone modification data from several different types of tumor infiltrating lymphocytes, which are gathered from a cohort of patients with solid tumors.
The goal of this project is to model the natural variation in gene expression across many immune cell types using an already established database at LJI (https://dice-database.org) and to identify cell type-specific epigenetic regulators of important immune genes.
This project focuses on developing computational tools for better analysis of the wealth of data from chromosome conformation capture assays with the ultimate goal of inferring functional chromatin contacts such as those between enhancers and promoters.
We model relationships between the proteotypes and the phenotypes of cells/organisms, with an emphasis on innate immunity in plants. Proteotypes are measured using custom methodology for high-throughput proteomics based on mass spectrometry. Students have an opportunity to integrate training in bioinformatics with chemistry and biology.
The specific state of the proteome in a given cell, tissue, or organism is known as the proteotype. The proteotype integrates constraints imposed by the genotype, the environment, and by developmental history (e.g., a leaf cell has a different proteotype than a root cell with the same genotype in the same environment). The proteotype directly determines phenotype since all molecules are made by and regulated by proteins. Thus, a complete description of the proteotype should define a phenotype at the molecular level. We are constructing an Atlas of Proteotypes that currently includes 162,777 peptides from 41,553 proteins in 65 different tissues and stages of development. In addition, we have identified and measured more than 30,000 phosphopeptides from these same samples. The 65 resultant proteotypes are revealing thousands of unanticipated regulatory relationships. The relationships between mRNA levels and protein levels are fascinating; they indicate that protein levels from some genes are regulated by transcription but that most protein levels are under post-transcriptional control. Inspection of our data explains tissue specific traits such as oil accumulation in the embryo that results from selective accumulation of proteins from common mRNAs.
With the rise of quantitative proteomics it is now possible to measure the absolute number of protein molecules in a cell. We are using multiple reactions monitoring (MRM) with a triple quadrupole mass spectrometer of heavy and light isotope-labeled peptides to quantify signaling and metabolic dynamics with a focus on the post-translational modifications of phosphorylation and acetylation. By placing biology on a quantitative basis we are contributing to several fundamental advances: the ratios of different proteins to each other are being determined (i.e., the stoichiometry); results from our lab can be combined with data from other labs because they are measured in absolute units; the ratios of proteins to metabolites or RNAs can be ascertained. We are constructing pathway proteotypes for signaling and metabolism to identify proteins whose levels are incompatible with a simple role in the process. To contribute to this effort we would like a student to construct an MRM database. The database will store all the heavy peptides we have available in the lab along with information about the proteins from which they are derived. We will store MS/MS information for peptides that we have observed in our proteome surveys using linear ion trap mass spectrometers. The database will store all reaction/transition data that we have obtained for each peptide along with the signal strength for each reaction product. MS1 scans with the triple quadrupole mass spectrometer will be included to evaluate the purity/intensity of the heavy peptides. This database will be of great use to the many labs that are beginning to place biology on a quantitative foundation.
We have a variety of projects ranging from brain mapping to derive optimal brain atlases, integrated omic analyses to identify genetic underpinnings of the brain, to precision medicine approaches for drug response prediction and drug target identification.
A major challenge hindering progress in neuropsychiatric medicine is our limited understanding of the genetics underlying the complexity of human brain structure and function. Our project aims to characterize genetic effects on the brain by multimodal imaging using human biobanks with MRI and genotype data. This will provide insight into shared and distinct genetic influences among different brain regions. Building on improved genetic knowledge of the brain, we will determine genetic relationship between brain morphology and neuropsychiatric disorders using statistical genetics tools. We will estimate effects of neuropsychiatric genetic risks and environmental exposures on deviations of MRI phenotypes from normal neurodevelopmental and aging trajectories.
Our goal is to identify potential drug targets of brain disorders (e.g., Alzheimer’s disease) through gene networks comprising disease-associated genes. Recent genomic studies have advanced our knowledge of the genetics of brain disorders and related traits, which could illuminate the pathogenesis of brain disorders.
The new knowledge provides opportunities for genetic-based strategies for drug target identification. Bioinformatics analyses will be performed to prioritize drug targets and potential drugs for repurposing.
Dr. Cheng’s research focuses on transcriptional regulatory network and aims to develop a comprehensive understanding of how aberrant regulatory circuits contribute to human disease. Dr. Cheng’s laboratory is particularly interested in understanding transcriptional and epigenetic regulation of the interplay between the immune system and central nervous system in neurodegenerative diseases, substance use disorder and HIV infection. Current projects focus on applying single-cell transcriptomics and epigenetics assays to characterize Alzheimer’s disease, HIV and opioid use disorder patient samples, with the goal of finding diagnostic markers and therapeutic targets. Dr. Cheng’s lab also has developed 3D brain organoid models for Alzheimer’s disease and HIV infection. Dr. Cheng received her M.S. degree in Computer Science from Stanford University, and she received her Ph.D. degree in Bioinformatics and Systems Biology from University of California, San Diego. After completing her doctoral study, Dr. Cheng did her postdoctoral training at the Broad Institute of MIT and Harvard.
We developed a novel tau propagation model using 3D spheroid model that rapidly develop tau pathology and neurodegeneration in just three weeks. Single cell transcriptomics of the model reveals cell type specific changes that resemble transcriptomic signatures from Alzheimer’s disease postmortem brain.
To understand cell type specific vulnerability of Alzheimer’s disease, we utilize snRNA-seq to characterize human brain tissues from Alzheimer’s disease patients across different brain regions.
As part of the NIH NIDA SCORCH consortium, we will dissect the dysregulated molecular circuitry in the brains of individuals with opioid use disorder and/or HIV infection. This project aims to identify genes that contribute to opioid use disorder and HIV-associated neurocognitive disorders. These approaches could lead to novel gene therapies to control and perhaps reverse the relentless disease state. We are in the process of generating snRNA-seq and snATAC-seq profiles from more than 300 patient samples across 3 different brain regions.
As part of the NIH NIDA SCORCH consortium, we will dissect the dysregulated molecular circuitry in the brains of individuals with cocaine use disorder and/or HIV infection. This project will focus on understanding how neurovasculature and neuroimmune cells contribute to cocaine use disorder and HIV-associated neurocognitive disorders. We will be generating snRNA-seq and snATAC-seq profiles from more than 300 patient samples across 3 different brain regions.
Our work aims to develop new mass spectrometry based methods to understand the chemistry of microbes, our microbiome and their ecological niche. In short, we develop tools that translate the chemical language between cells. This research requires the understanding of (microbial) genomics, proteomics, imaging mass spectrometry, genome mining, enzymology, small molecules structure elucidation, bioactivity screening, antibiotic resistance and an understanding of small molecule structure elucidation methods. The collaborative mass spectrometry innovation center that he directs is well equipped and now has twelve mass spectrometers, that are used in the studies to investigate capture cellular chatter (e.g. metabolic exchange), metabolomics, metabolism and to develop methods to characterize natural products. These tools are used to defining the spatial distribution of natural products in 2D, 3D and in some cases real-time. Areas of recent research directions are capturing mass spectrometry knowledge to understand the microbiome, non invasive drug metabolism monitoring, informatics of metabolomics, microbe-microbe, microbe-immune cells, microbe-host, stem cell-cancer cell interactions and diseased vs. non-disease model organisms and the development of strategies for mass spectrometry based genome mining and to detect and structurally characterize metabolites through crowd source annotation of molecular information on the Global Natural Products Social Molecular Networking site through the NIH supported center for computational mass spectrometry that is co-developed with Nuno Bandeira. A more detailed biography can be found in this Nature article.
The Dorrestein laboratory is interested in the functional aspects and biosynthesis of post-translational modifications (PTMs). Of particular interest are orphan genes (genes currently assigned to have no known function) that are responsible for the generation of bioactive natural products (e.g. antibiotics, anti-cancer agents etc.) or PTMs. This research aims to understand the functions of such genes by the use of high-resolution mass spectrometry. To achieve this goal, the lab will have the most advanced mass spectrometer on the UCSD campus. Please see the Research page on the lab website for descriptions of current research projects.
The establishment of cell-type identity and specific gene expression patterns is tightly regulated by the interplay between different modalities of the epigenome. These include DNA cytosine methylation, which affects transcription factors’ binding to regulatory elements, and higher-order chromosome structures bridging the distal regulatory elements to the target genes. Studying their diversity across cell types is fundamental for understanding complex human diseases in different tissue contexts.
Characterization of gene regulatory elements using multi-omic data
Computational analysis of multi-omic single cell data from 4 species (mouse/marmoset/macaque/human)
The Gaulton lab studies the effects of human genetic variation on gene regulation and diabetes risk. We use computational and statistical methods to integrate genome sequence information with epigenomic annotation and molecular QTL data.
This project involves dense genetic fine-mapping of diabetes risk loci, integrating fine-mapping data with large-scale genomic and epigenomic maps using published and novel models to identify causal variants, cell types and networks, and applying these predictive models to identify additional diabetes risk loci.
This project involves the development of novel methods for integrating genetic association data with epigenomic annotation, expression QTLs and chromatin QTLs to predict causal genes of diabetes risk variants.
This project involves development of novel mixture model approaches to predicting and quantifying the extent of pleiotropy among diabetes risk variants genome-wide.
We work on many aspects of molecular mechanism, modeling and design. Our core interests are in the physical chemistry and algorithms underpinning computer-aided drug design methods, but we also have much wider interests, such as simple model receptors for studying molecular recognition; how molecule motors work; chemical informatics and its interface with bioinformatics; how membranes work; and synthetic catalysts and nanoparticles.
While working on methods of computing entropy from molecular simulation data, we stumbled on an interesting mathematical angle for studying correlations in high-dimensional spaces. The math can be applied in various realms, and we got some interesting results applying it to residue-residue (and higher-order) correlations within protein sequences.
Thus, this would be an exploratory project to see if we can learn new things about sequence-function relationships in proteins, and maybe about how to design new proteins, by studying residue-residue correlations, particularly in the context of 3D protein structures.
Dr. Glass’ primary interests are to understand transcriptional mechanisms that regulate the development and function of macrophages. Macrophages play key roles in immunity, wound repair, development and tissue homeostasis. Dysregulation of macrophage functions contribute to a broad spectrum of human diseases, including atherosclerosis, diabetes, neurodegenerative diseases, and cancer. A major effort of the Glass laboratory is to use genomics assays and associated bioinformatics approaches to understand how macrophage gene expression programs are established and how they are influenced by different tissue environments and disease. An important concept to emerge from these studies is that enhancers can be exploited to deduce the transcription factors and upstream signaling pathways that drive context-specific transcriptional outputs. Students are welcome to select projects from current areas of active investigation.
Many lines of evidence, including genome-wide association studies, indicate that non-coding genetic variation plays a major role in determining phenotypic diversity. We were among the first laboratories to define the impact of natural genetic variation on enhancer selection and function (Heinz et al, Nature 2013 PMID 24121437), but at present it remains difficult to predict the impact of non-coding variation on gene expression. In a novel and ambitious effort, we systematically characterized the genome wide patterns of mature RNA (RNA-seq), nascent RNA (GRO-Seq), transcriptional initiation (5’GRO-seq), histone modifications and binding profiles of lineage-determining and signal-dependent transcription factors (ChIP-seq), DNA methylation (bisulfite sequencing), and chromatin conformation (HiC, capture HiC and PLAC-seq), in resting and activated macrophages derived from 5 different inbred strains of mice providing ~60 million single nucleotide variants, ~6 million InDels and several hundred thousand structural variants. This data set provides a unique resource for investigating the impact of non-coding variants on transcription factor binding, enhancer activation and target gene expression. We are currently developing new computational methods for analyses of these data with a goal of explaining effects of non-coding mutations and predicting patterns of gene expression in new mouse strains. Related projects are investigating the relationships of genetic variation between selected mouse strains and their different susceptibilities to metabolic and cardiovascular disease. This general project area is both challenging and open ended and there are a wide range of directions that rotation projects could take. As examples, recent rotation students have implemented machine learning approaches to investigate how sequence variants affect collaborative binding between lineage-determining transcription factors.
Each population of tissue resident macrophages exhibits a distinct pattern of gene expression that is tuned to the developmental and homeostatic needs of that tissue. For example, brain macrophages called microglia produce factors that are trophic for neurons and monitor synapses, functions that require a brain-specific program of gene expression. A key question is how this tissue-specific program of gene expression is achieved. Through analysis of gene expression and enhancer landscapes, we obtained evidence that the microglia-specific molecular phenotype results from instructive signals in the brain that direct the activation of microglia-specific enhancers (Gosselin et al., Cell 2014 PMID 25480297). Of particular interest, delineation of the gene expression patterns and enhancer landscapes of human microglia revealed that a substantial fraction of the genes associated with non-coding GWAS risk alleles are preferentially or exclusively expressed in microglia, and many are brain environment dependent (Gosselin et al. Science 2017 PMID 28546318). These findings raise several important questions that are under active investigation, including what are the environmental factors that dictate the brain specific program of gene expression and how do human genetic variants affect the regulation of genes that are linked to neurodegenerative disease. We are taking a multi-disciplinary approach including studies of in vivo mouse models, in vitro human iPSC-derived microglia, genomic assays of microglia nuclei derived from control and Alzheimer’s disease brains, and direct analyses of the relation of genotype to gene expression in a growing of RNA-seq data base derived from purified human microglia. As an example, a recent rotation project investigated the question of whether there is any relationship between circulating monocytes (a white blood cell that can differentiate into macrophages in tissues) and microglia gene expression patterns from the same individual.
Our overall goal is to understand complex genetic variants that underlie human disease. We are particularly interested in repetitive DNA variants known as short tandem repeats (STRs) as a model for complex variation. Our work focuses on developing computational tools for analyzing and visualizing complex variation from large-scale sequencing data and applying these tools to learn about the contribution of repetitive variation to human disease.
A major result from recent genome-wide association studies (GWAS) is that the majority of genetic variants driving common human disease lie in regulatory, rather than protein-coding, regions. While it is relatively straightforward to predict the consequences of mutations in coding regions, we are far from being able to interpret and sift through the large number of non-coding variants arising from whole genome studies. Recent studies have leveraged population-wide genetics datasets to determine genes that are depleted of variation, or "constrained", and thus presumably important for human health. In this project we will develop statistical tests using large panels of human genetic variation to systematically measure constraint for a variety of regulatory annotations and evaluate the utility of these annotations for prioritizing variants from medical genetics studies.
The Oncogenomics laboratory is located in the Moores Cancer Center. Its research program is focused on the identification of genetic and epigenetic markers for cancer prevention and progression as well as drug response. The laboratory is a humid laboratory, combining both wet-lab techniques and bioinformatics analysis to study cancer samples from patients and animal models of cancer. The laboratory is also an important partner for multiple principal investigators at the Moores Cancer Center, collaborating on the design, analysis and interpretation of their genomic experiments.
Genetic information is considered protected health information (PHI) and as a consequence the highest security standards need to be applied for its storage, analysis and sharing. The oncogenomics laboratory is using state of the art iDASH compute cloud for its main computation. As a consequence, we participate in the development of optimal workflows and virtual machines for the analysis of patient-derived genomic datasets such as whole exomes, whole genomes, RNA-seq or genotyping arrays.
In this project we will develop robust provisioning methods to establish virtual machines capable of running popular human genomic analysis workflows. We will benchmark these machines and workflows and convert some of them into standard recipes for production-grade, reproducible genomic analysis.
Cisplatin (cDDP) is the most commonly used chemotherapeutic drug, but most cancer eventually become resistant, leading to tumor recurrence. Several biological processes may modulate cDDP sensitivity: Drug import, export, detoxification, DNA repair, apoptosis. Drug resistance is transmitted to daughter cells, and one can build up resistant cell lines in vitro using sequential treatments. We are interested in identifying the genetic mutations that mediate this resistance. For this, we have derived resistant cell-lines from single clones of a cDDP sensitive ovarian cancer cell line. Using exome sequencing as well as target sequencing, we propose to determine mutations in genes and pathways that drive drug resistance. We will then expand the findings to the TCGA samples, using time to recurrence as an indicator of drug sensitivity.
The role of germline or inherited variation in cancer has been studied in selected families and led to the identification of genetic variants that are dominant and responsible for cancer syndromes. Similarly, rare recessive variants with lower penetrance are responsible for the increased risk in breast and ovarian cancer (BRCA1/2). More common variants in the population have also been identified through GWAS, and have revealed multiple SNPs associated with a modest increase in cancer risk. Despite these advances, multiple variants of intermediate allelic frequency in the population, or carried by patients with undocumented family history still remain variants of unknown significance (VUS) and can still play a role in tumor development. In addition, the contribution of variants located outside of the coding region has been underexplored and can now be reexamined in the light of recent maps of the regulatory landscape. The long-term goal of this research is to utilize germline genetics variation in cancer prevention and care to better stage patients or predict their response to treatment.
We propose to identify the germline variants in the UCSD Cancer center patients (targeted gene panel) as well as in the public TCGA/ICGC datasets (whole genomes). We will then test these variants, alone or in combination to identify the ones that impact cancer onset, the tumor somatic landscape or tissue-specific regulatory network. The project will involve the processing of high throughput sequencing data, population genetics, and statistical analysis, in a HIPAA compliant cloud-computing environment.
Terence Hwa, Departments of Physics and Molecular Biology
The Hwa lab (a.k.a. the Quantitative Microbiology Lab) uses a combination of experimental and theoretical approaches to elucidate the organizational principles of living systems. The goal is to quantitatively characterize the physiological behaviors and understand how they arise in terms of the underlying molecular interactions. Our lab focuses on the bacterium E. coli, because it is perhaps the best characterized in terms of molecular components and interactions. But we do also study higher organisms together with collaborating labs. Please visit our lab webpage (https://matisse.ucsd.edu) for further information.
An outstanding challenge in making biology quantitative and predictive is how to deal with the millions or even billions of missing parameters that describe the underlying molecular interactions. In recent years, our lab pioneered a top-down approach which exploited a number of phenomenological laws to accurately predict the physiological responses of bacteria to environmental and genetic changes (e.g., nutrients, antibiotics, heterologous protein expression) [DOI: 10.1126/science.1192588]. Furthermore, insight from this quantitative physiological approach is able to pinpoint key missing molecular interactions in long-studied biological processes [DOI: 10.1038/nature12446]. The lab has a number of projects further extending this basic approach to a variety of problems in microbiology, including growth transitions, stress response, antibiotic resistance, and biofilm formation.
Cells must continuously maintain integrity and compartmentalization with demands for cellular remodeling throughout development, immunity, aging and disease. Using functional genomics, genetics and cell biological approaches in the fruit fly, Drosophila, we are studying the central roles for membrane regulation of dynamic cell structure. We have identified novel endocytosis and autophagy membrane trafficking pathways that control macrophage and muscle remodeling, with relevance to human disease. Current projects in the lab aim to discover new mechanisms of cellular remodeling through functional genomic and proteomic approaches, and to better understand the pathway networks and dynamics during cellular remodeling.
The Knight lab has broad interests in the human microbiome, the collection of trillions of microbes that inhabits our bodies, especially in developing techniques to read out these complex microbial communities and use the resulting data to understand human health, links between humans and the environment, and to prevent and cure disease. We offer a fast-paced environment with many collaborative opportunities on different projects.
We have amassed a database of microbial DNA sequences from hundreds of thousands of biological specimens. Understanding how these changes relate to disease requires a range of machine learning and multivariate statistical approaches. There are many opportunities ranging from entry-level (benchmarking classifier performance on specific sample sets) to extremely challenging (using deep learning to infer the structure of global sample set relationships).
An increasing need is to integrate data from different "omics" level, e.g. genomes, metagenomes, metatranscriptomes, metaproteomes, metabolomes, immunological profiling, etc., into a single coherent picture separating healthy and disease states. Improved methods for performing this task, either directly or via intermediate representations such as mapping to metabolic and regulatory pathways, is essential for improving understanding. Projects in this category range from simple (testing where existing techniques like correlation networks or Procrustes analysis do/don't connect two specific data layers) to challenging (use transfer learning to integrate heterogeneous data layers and improve the underlying network annotation). An especially exciting emerging research direction here is XAI (explainable artificial intelligence), which can provide for clinical applications a better justification for a specific classification or suggestion.
Many algorithms used in microbiome studies, especially in metagenomic assembly, are extremely computationally expensive. Opportunities exist for either exploiting new hardware architectures to accelerate existing algorithms, or for developing new approximate algorithms, to tackle problems in the workflow including inferring taxonomy and function from DNA sequence data, genome and metagenome assembly and annotation, computing community distance metrics from sparse compositional data, and high-level analyses of hundreds of thousands of microbiomes. Again these projects range from entry level (compare results of two multiple sequence alignment techniques for subsequent community analysis) to advanced (use non-von Neumann architectures to perform pattern classification in real time at the whole community level for disease detection).
Dr. Koola is a physician scientist specializing in Biomedical Informatics and Hospital Medicine. He specializes in the area of big data machine learning for predictive analytics. In particular, he is interested in using electronic health records to improve care delivery--particularly for patients with advanced liver disease. Using risk prediction models in a healthcare context requires understanding of: (i) the healthcare system of intended use; (ii) risk model building; (iii) risk model assessment; and (iv) risk model re-calibration. Additionally, Dr. Koola is interested in visual analytics, data modeling, and health services research.
In 2012 the Institute of Medicine released a desiderata for a learning healthcare system, where evidence informs practice and practice informs evidence. Though the randomized clinical trial (RCT) serves as the gold standard for informing clinical decisions, flaws exist in terms of achieving recruitment, overly stringent inclusion/exclusion criteria, and lack of patient-centered decision making. Observational cohort studies have grown as an important complement to RCTs allowing comparative effectiveness research and patient-centered trials. The surge of Electronic Health Records (EHR) and its resulting zettabyte of data5 allows us to realize this vision for the first time. Despite the growth of observational cohort studies, challenges still remain bringing the knowledge from the bench-to-the-bedside; moreover, model performance degrades when used in a cohort outside of its development.
To ameliorate these difficulties, we propose to launch and study a novel “informatics consult” service. The service would allow clinicians, when no clear evidence based guidelines exist regarding care decisions, to query the UCSD clinical data warehouse by identifying patients similar to the index case. First proposed in the seminal “Green Button” paper by Longhurst et al., such a system would leverage our ability to truly deliver personalized, patient-centered care. Small-scale limited efforts have been put into practice to answer questions regarding treatment of melanoma8 and systemic lupus erythematosus complications. We note, however, the opportunity for a much larger service with broad impact starting with insights borne of data from UCSD, and potentially mining insights from the entire state-wide UC Health data warehouse.
We note several novel challenges to this proposed system: (i) Performing semi-automated phenotyping so that we can identify clinical outcomes of interest10. (ii) Identifying patients that are similar to the index patient (often called clustering). (iii) Incorporating automated, computable search regarding guideline recommended care. (iv) Performing visual analytics to understand similarity of cohorts. (v) Communication of probability and statistical information to healthcare professionals so they can effectively manage uncertainty.
Student responsibilities:
Unhealthy dietary choices—a lack of nutritious foods and an excess of unhealthy food—was shown as the major contributor in the 400,000 U.S. deaths in 2015 from cardiovascular diseases (CVD). Eating more nuts, vegetables, and whole grains, and less salt and trans fats, could save tens of thousands of lives in the U.S. each year. Obesity is one critical outcome of poor diet, which also contributes to heightened CVD risk. Thousands of smartphone apps are available to download for weight loss, but these apps primarily focus on caloric intake, rather than the overall quality of diet and lifestyle critical for CVD prevention.
Mobile Health (mHealth) applications also have not been systematically tested for their effectiveness and are criticized for not having an evidence-based foundation. In this study, we adapt the design of mHeart to communicate automatically with the UCSD Electronic Health Record to help healthcare providers have access to psychosocial aspects of patient's care outside of the direct hospital system. In particular, the provider will be able to view logs of patient activity, dietary choices, and other lifestyle choices. The provider will also be able to send feedback to the patient to alter behavior.
Student opportunities:
Patients with cirrhosis, a late stage of chronic liver disease, are at increased risk of hospitalization and hospital readmission. Although several studies have looked at models for predicting readmission for patients with cirrhosis, they are limited by small sample sizes, limited candidate predictor variables, and limited evaluation of discrimination and calibration. A systematic review and meta-analysis of available evidence can help shed new light on the problem, and help identify modifiable risk factors.
Student responsibilities:
Dr. Tsung-Ting Kuo is an Assistant Professor of Medicine in University of California San Diego (UCSD) Health Department of Biomedical Informatics (DBMI). He is mainly conducting biomedical, healthcare and genomic studies based on blockchain and predictive modeling. His research focuses on blockchain technologies, machine learning, and natural language processing.
Predictive modeling can advance research and facilitate quality improvement initiatives and substantiate research results, especially when data from multiple healthcare systems can be included. However, current, state-of-the-art privacy-preserving predictive modeling frameworks are still centralized, in other words, the models from distributed sites are integrated in a central server to build a global model. This centralization carries several risks, e.g., single-point-of-failure at the central server. To improve the security and robustness of predictive modeling frameworks, we will develop and implement novel and advanced algorithms on decentralized blockchain networks (a distributed ledger/database technology adopted by crypto-currencies such as Bitcoin and Ethereum) to build better models. The outcome will be algorithms that improve the predictive power of data from multiple healthcare systems through a distributed system. Selected references: PMID 36402113, 34923447, and 31943009.
Our goal is to identify genes causing insulin resistance in humans in order to find new therapeutic targets for diabetes and cardiometabolic diseases. Our approach to discovery is grounded in human genetics, clarified through systematic, high throughput experimentation in human cells, and calibrated by its relevance to clinical disease. We use massively parallel genome engineering to re-create mutations identified in patients and develop high-throughput assays to interrogate function in human cell models. We apply bioinformatics and statistics to make sense of this data integrating 1) human mutations, 2) cellular function, and 3) metabolic/glycemic phenotypes of the individuals who harbor them. Using this approach, we have discovered novel missense mutations that greatly increase risk for type 2 diabetes. As a complementary aim towards precision medicine, we develop tools for clinical genome interpretation powered by high-throughput experimental data.
Insulin resistance causes diabetes, heart disease and many cancers. Only one major class of drugs, thiazolidinediones (TZDs), treats insulin resistance, but causes serious side effects. RNA-sequencing has been performed on patient adipose and muscle tissue before and after TZD treatment as well as in multiple other clinical situations where insulin resistance is altered (e.g. weight loss, gastric bypass). The goal of this project is to develop a framework for jointly analyzing these multiple datasets to identify a core of common gene expression changes. This common core would be enriched for causal mediators of human insulin sensitivity changes and will be experimentally validated in laboratory based experiments to credential new therapeutic targets for insulin resistance.
Research in the Mesirov Lab focuses on cancer genomics applying machine-learning methods to functional data derived from patient tumors. The lab analyzes these molecular data to determine the underlying biological mechanisms of specific tumor subtypes, to stratify patients according to their relative risks of relapse, and to identify candidate compounds for new treatments. The overall goal is to treat patients as individuals specific to their tumors. Importantly, the lab is committed to the development of practical, accessible software tools to bring these methods to the general biomedical research community.
We seek an undergraduate student to write software to enhance our Molecular Signatures Database (MSigDB), a repository of gene sets utilized by a worldwide genomics community of over 180,000 users. These gene sets are used to identify and better understand activated pathways underlying human disease, e.g., cancer, and to interpret experimental results. The NIH recently retired the Cancer Genome Anatomy Project, which included the BioCarta collection of curated pathway diagrams. These images and associated metadata are only accessible via the Internet Archive “Wayback Machine”. The project involves retrieving this data from the archive to provide visualizations for entries in the MSigDB. The results of this work will have a great impact on biomedical investigations worldwide. Interested students should have some familiarity with either programming and/or web technologies such as HTML and a willingness to learn Python and how to use libraries such as urllib and BeautifulSoup to perform web scraping. Expertise in biology is not required. The project can be for class credit or as an internship which may lead to other research experience.
Immunotherapy, a class of drugs that enable a patient’s immune system to fight cancer, has emerged as a promising area of cancer drug development in recent years. However, not all patients respond to these treatments, and many patients who do will have a recurrence of their cancer. The biological mechanisms behind these differences in response to immunotherapy are currently poorly understood. However, recent improvements in sequencing technology now allow scientists to examine the behavior of genes in individual cells and how those cells resemble or differ from other cells around them. With this new data also comes the need to create new computational methods to analyze it. On this project, the student will work at the intersection of algorithm development and cancer biology to interpret single-cell sequencing data of cancer samples with the goal of understanding how cancer cells interact with the nearby immune cell populations and how these interactions affect response to treatment. Students will work in a multidisciplinary environment, collaborating with biologists, software developers, and experts in the fields of immunology and oncology. Interested students should have prior experience with programming, preferably in Python or R. This project can be done for class credit or as an internship.
Our lab specializes in reconstruction of evolutionary histories (phylogenies) from large scale datasets and applications of phylogenetic analyses to downstream analyses. Large-scale datasets include those with many genes and those with many species, and we focus on high accuracy and scalability at the same time. Many projects in this area are available, some of which are described below, but students can contact me to start on other projects as well.
Developing methods for computing a consensus among large numbers of large multiple sequence alignments using the concept of an equivalence class.
Several projects are available, with different emphasis. Other projects in this general area can also be defined based on student interest.
See the Projects page on http://www.ubergrid.org for an extensive list of mass spec projects in the Pevzner lab and in collaborating labs. Project themes include mass spec, T-Rex Fossils, and Comparative Proteogenomics.
RNA epigenetics or epitranscriptomics is an emerging field focused on chemical modifications in RNA. We are interested in understanding how RNA modifications affect the immune system during viral infections, vaccine development, immunotherapy, and in cancer. We employ in vivo models as well as non-human primates and human tissues to investigate genetics and epigenetics mechanisms of multiple disease states. Single-cell studies and data analyses are being performed to generate a single cell transcriptome and epigenome atlas of human brain regions such as prefrontal cortex, striatum, and hippocampus. Commonly used methods in the laboratory include large scale functional perturbation studies using RNAi and CRISPR, Simultaneous single-cell RNA sequencing and single-cell Assay for Transposase- Accessible Chromatin sequencing (scMultiome-seq), patient-specific stem cell derived brain and lung organoids, drug design and pharmacology, and analyses of immune cells’ functions.
The Rifkin laboratory studies how environmental, genetic, and stochastic variation interact to generate phenotypic variation and thereby mold the course of evolution. We use yeasts and nematodes as model organisms and work primarily at the level of gene regulatory and signal transduction networks.
In many population genetic models, mutations are assigned a fitness effect - an assigned genotype -> fitness map. Simulations can then explore how gene frequencies change over time under a number of demographic scenarios. An alternative is to use a generative model - often times a set of differential equations - that use genotypic information to produce a phenotype and then map this phenotype to a fitness value. We are using such a simulation framework to study how genetic networks evolve under different demographic situations. Experience with C++ would be very useful for this project since the basic population genetic framework consists of a library of C++ templates.
Lab Location: CMM-West, Rm. 345
Lab Phone: 858-534-5858
Lab Composition and Activities: Five graduate students from several programs, and a talented group of enthusiastic (also helpful) postdoctoral fellows and a full time laboratory manager. We have one general laboratory meeting, one graduate student-only meeting, and one personal meeting each week. We also have joint lab meetings with two other labs weekly.
Research Interests: Our central laboratory focus this year is to continue to utilize global genomic approaches to uncover and investigate the “enhancer code” controlled by new, previously unappreciated pathways that integrate the genome-wide response to permit proper development and homeostasis, and that also functions in disease and senescence. We have investigated these events in differentiated cells, neuronal development, stem cells, and cancer. Our biological focus is on molecular mechanisms of the “enhancer code” regulating learning and memory; aggressive prostate and breast cancer, and they underlying events of senescence/aging. Epigenomic events studied include non-histone methylation events and non-coding RNAs. We are investigating these events in development, breast and prostate cancers, and in inflammation-based disease, including degenerative CNS disease and diabetes. The emerging importance of non-coding RNAs and regulation of nuclear architecture is rapidly altering our concepts of homeostasis and disease. Our laboratory is “Seq-ing” (RIP-seq, ChIP-seq, RNA-seq, GRO-seq, CLIP-seq, ChIRP-seq), and a new “FISH-seq”, for open-ended discovery of long-distance genome interactions to uncover new “rules” of regulated gene transcriptional programs and new roles for lncRNAs in biology of normal, cancer neuro-affective disorders and aging cells. Coupling this with chemical library screens, we hope to introduce new types of therapies based on targeting specific gene enhancers, histone protein readers and writers, and lncRNAs for cancers and other diseases. Recent surprising findings have been novel roles of lncRNAs prostate and breast cancer, connection between DNA damage repair/transcription and replication, and unexpected roles of enhancer RNAs.
Current interests include:
Potential projects include:
Julian Schroeder's research is directed at discovering the signal transduction mechanisms and the underlying signaling networks that mediate resistance to environmental stresses in plants, in particular drought, salinity stress and CO2 responses in plants. These environmental (“abiotic”) stresses have substantial negative impacts on plant growth and crop yields. These environmental stresses are also relevant in reference to climate change and to maintaining available arable land to meet human needs. Research in Julian Schroeder's laboratory is using multidisciplinary approaches including genomics, bioinformatics, cell signaling, network modeling, proteomics and molecular biological towards uncovering the signal transduction network and receptors in plants that translate drought stress hormone reception, CO2 sensing and salinity stress to specific resistance responses. Some of the recent research advances are being used in the biotechnology industry with the goal of enhancing stress resistance of plants and crop yields. Undergraduate research projects will include systems biology and bioinformatics and innovative analyses of large scale data sets within this research. Undergraduate research projects will be pursued to model and identify drought stress-induced and CO2-induced signaling networks based on “omic” scale data sets. Models will be directly tested by wet lab experimentation.
Julian Schroeder is Co-Director of the Center for Food and Fuel for the 21st Century. See https://labs.biology.ucsd.edu/schroeder/ for more information on the Schroeder lab.
My research focuses on time series analysis in biological systems, with an emphasis on practical information extraction for translational applications. The lab is divided into applications and approaches, though these all serve each other, and students collaborate routinely. Indeed, a positive attitude and an eagerness to support one another is requisite in the lab. **Applications include but are not limited to: illness detection, prediction, and recovery monitoring; pregnancy detection and outcome forecasting; mental health monitoring; defining sleep in the body (as opposed to EEG); diabetes forecasting; and carbon footprint optimization of distributed computer systems. **Approaches include, but are not limited to: multimodal time series information extraction; differentiating multiple outcome types from random assortment; reduction of high dimensional spaces with both modality, individual, and time series components; explicable machine learning model development; non-stationary signal analysis; novel approaches do diversity mapping and phenotyping from physiology and behavior data. I seek to find a fit with each individual and the lab’s ongoing projects; no one comes in and is just given marching orders – you’ll do better work when it’s the work that you actually want to do!
Some individuals seem to have lingering or failed recoveries after COVID-19 infections. Students comfortable with basic programming or data science skills are encouraged to enhance our description of recovery profiles from TemPredict, and search for features that can contribute to pre-recovery classification.
Algorithms tend to be one size fits all, where as people are similar or dissimilar in complex and unmapped ways. Help map differences in normal routines, as well as in illness and recovery trajectories. These might arise from known demographic information, co-morbid conditions (diabetes, pregnancy, etc.), or be represent different patterns in illness associated with unknown or latent variables.
We have shown repeatedly in humans and animal models that females are as tractable with statistics as males (actually, often more than). Yet female physiology remains inappropriately understudied. Help us refine algorithms, map changes like pregnancy and menopause, and explore diversity within as well as across traditional sex categories.
We're working on a number of projects related to hardware acceleration of computationally challenging tasks in genomics research and looking for highly motivated students with some background in GPU/FPGA programming or high-performance computing to join. Please contact me via email for details on the latest projects.
We’re currently working with an international group of collaborators to perform phylogenetic
analysis of millions of SARS-CoV-2 sequences for viral epidemiology and evolutionary biology applications. Current projects include phylogenetic placement in real time, tree structure optimization, recombination inference, etc. Contact me via email for the latest details.
Our research focuses on molecular engineering for cellular imaging and reprogramming, and image-based bioinformatics, with applications in stem cell differentiation and cancer treatment.
Fluorescence resonance energy transfer (FRET)-based biosensors have been widely used in live-cell imaging to accurately visualize specific biochemical activities. We have developed the Fluocell image analysis software package to efficiently and quantitatively evaluate the intracellular biochemical signals in real-time, and to provide statistical inference on the biological implications of the imaging results. However, important questions arise on how to use these results to reconstruct the quantitative parameters in the underlying biochemical networks, which determine cellular functions and ultimately their fates. In this rotation project, we will integrate optimization-based machine learning approaches with biochemical network models to seek answers to these questions, with applications in cancer treatment against drug resistance.
The diagnosis of infectious diseases often requires tissue biopsy and microscopic examination by pathologists, which is time-consuming, labor-intensive, and error-prone. To develop a software-assisting system for identifying microorganisms on digital images, we utilize the convolutional neural network and transfer learning for training and validating an intelligent software system for the classification of pathology slides. The goal of this project is to provide a diagnosis of pathogens with high efficiency and accuracy. Students will work in an interdisciplinary team, collecting and labelling imaging data, developing deep-learning based algorithms and user interfaces, characterizing and optimizing the accuracy and functionality of the software package.
We have a wide scope of projects ranging from developing novel algorithms for studying RNA processing in diseases, development and personalized medicine, and for analyzing single-cell RNA-seq data.
The Yeo lab is responsible for the identification of the RNA sequence elements bound by 250 RNA binding proteins (RBPs) as part of the newest ENCODE (https://www.genome.gov/10005107) efforts. Various computational projects that pertain to integrating RNA binding sites with functional alternative splicing, RNA stability and translational changes to generate global, genome-wide predictions of what each RBP can do are available for enterprising, hard-working graduate/undergraduate students.
Recent studies of single cells demonstrate that the assumption that all cells of the same "type" are identical is simply inaccurate. Single, individual cells from the same population of cells differ by a lot and these differences underlie phenotypic responses to environmental stimuli. The Yeo lab is using microfluidics-based platforms to study whole transcriptome differences in single cells from a variety of biological systems, ranging from embryonic stem cells to diseased motor neurons. One of our projects is to develop new bioinformatic analytics to study cellular heterogeneity during environmental influences.
