Bioinformatics and Systems Biology Rotation Projects

Building a mixed docking/pharmacophore based engine for activity prediction

The lab now built a collection of about 1000 pocket ensembles. Computational docking a new compound to them and scoring the compound will predict the activity of it. The project involves building mixed models in which the docking score weights are optimized and the docking score is complemented with a different kind of score based on the continuous pharmacophoric models. The resulting models will be tested for their ability to predict a selectivity profile of the test compounds. This project will involve cluster or distributed computing.

Data visualization: 3D trees

This project will focus on the development of the server for a new kind of data visualization of large data sets. Feel free to contact me for further details.

Development of activity and toxicity models related to anti-targets

Many chemicals and metabolites can cause serious adverse effects we can read about on drug labels. We have gradually accumulated a large body of data relating some of these activities to binding to specific proteins. This project will involved the development and optimization of a specific so continuous pharmacophoric models which can be built without the knowledge of the three dimensional structure of the target. The models will be built for several key anti-targets: hERG, PPARg, ESR, D2, etc., 5HT2c and 2b, M1. The models will be tested against the known therapeutics and their metabolites.

The evolution of human evolution

In principle, Darwinian evolution requires at least two essential ingredients: (i) processes that change the inherited genetic material (i.e., mutation of the germline DNA); and (ii) processes that cause natural section based on the functional/phenotypic results of these genetic changes. Germline mutations are believed to predominately originate from endogenous cellular processes with minor contributions from exogenous processes. Each mutational process imprints a characteristic mutational pattern on the genome, termed, mutational signature. For example, the deamination of 5-methylcytosine to thymine is an endogenous process generating C:G>T:A mutations at CpG dinucleotides, while CC:GG>TT:AA doublet substitutions occurring at dypyrimidines are associated with exogenous exposure to ultraviolet light. Analyses of mutational signatures in thousands of cancer genomes has revealed the signatures of more that 100 mutational processes. Some of these mutational processes are operative throughout the entire lifetime of an individual whereas others are present only at certain stages of life. The signatures of processes gradually accumulating throughout the entire lifetime of an individual are referred to as clock-like mutational signatures, and these include signature 1 (etiology: deamination of 5-methylcytosine) and signature 5 (etiology: unknown). Previous work on de novo germline mutations derived from family trios demonstrated that signatures 1 and 5 can explain the majority of these germline variants, indicating that the clock-like signatures are the main contributors to human evolution. However, the activity of different mutational signatures has never been evaluated in regard to the phylogenetic timeline of human evolution. In this rotation project, we will analyze data from the 1000 genomes project, a database containing the germline genomes of 2,504 individuals from 26 populations. This database includes 84.7 million single nucleotide polymorphisms (SNPs) and 3.6 million short insertions/deletions (indels) phased onto high-quality haplotypes. Using these data, we will build a phylogenetic tree (i.e., a tree showing the evolutionary relationship between individuals) where each leaf of the tree will contain the private germline mutations derived from a single individual. The activity of mutational signatures will be evaluated in each leaf as well as in each node of the phylogenetic three. The analysis will reveal the activity of mutational signatures throughout human evolution.

Understanding the molecular landscape of precancers for preventing cancer

All cancers originate from a single cell that undergoes a transformation from a normally functioning somatic cell into a malignant neoplasm. In most cases, this transformation follows a stepwise process with the somatic cell first expanding into a precancer and, subsequently, becoming an advanced invasive cancer. The progression from a pre-malignant tumor to a malignant neoplasm is due to somatic mutations that can be traced, characterized, and genomically studied. In this rotation, the student will evaluate the mutational burden, driver mutations, copy number changes, mutational signatures, and subclonal architecture of pre-malignant lesions and compare them to molecular events previously identified in advanced invasive cancers. The goal is to reveal the molecular events that are necessary for a precancer to convert into cancer. Independent previously generated drug-screen datasets (e.g., Cancer Cell Line Encyclopedia) will be used to propose potential intervention strategies that can used to target these molecular events in order to halt this conversion and lead to cancer prevention.

Mapping the genetic architecture of polygenic disease, complex traits, and gene expression levels

The Amariuta Lab is happily accepting PhD rotation students. We have a variety of predefined projects but are also open to student-led projects and ideas that fall within the general scope of research in our lab. One potential rotation project involves the investigation of different ways to estimate gene expression heritability (e.g., the proportion of gene expression variance that can be explained by genetics), including using cutting edge fine-mapping algorithms and single cell RNA-sequencing data. A second potential project involves developing a new method to map the cell-type-specificity of the genetic component of gene expression regulation, which is often confounded by strong patterns of co-expression and co-regulation across genes and other cell types. A third potential project involves mapping the causal genes underlying putative causal tissues and cell types mapped via our previously published method called Tissue Co-regulation SCore regression (TCSC). A fourth potential project involves identifying putative causal tissues and cell types underlying the genetic correlation and hence pleiotropy of immune-mediated diseases. Lastly, we are also beginning to explore deep learning models to use sequence data to predict gene expression levels in novel ways and would welcome students who wish to gain skills in any of these areas.

Integrative analysis of multi cell-type gene expression and epigenomic data in tumor immune response

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.

Predictive and comparative modeling of epigenetic gene regulation in different human immune cell types

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.

Statistical methods for inferring functional DNA-DNA contacts from Hi-C and HiChIP/PLAC-seq data

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.

Extrachromosomal DNA analysis

Extrachromosomal DNA formation is an important pathological condition found in nearly a third of cancers and all cancer subtypes. Our lab is developing computational tools to characterize their structural and functional properties of ecDNA and related focal amplifications. 

The interested students should have an interest in learning about,  designing and implementing graph algorithms, and should commit to taking my winter class CSE280A.

Computational Mass Spectrometry

Several rotation projects are available, each requiring a different mix of algorithmic, programming and data analysis skills:

1. Characterization of protein sequence polymorphisms in drug-resistant TB strains.
2. Categorization of post-translationally modified opioid neuropeptides.
3. Discovery of novel post-translational modifications in human cancer
4. Early detection of biosimilar precursors of current and possible therapeutic drugs, including proteomic analysis of monoclonal antibodies and venom proteins.

Duplicated genes and association with disease

Hundreds of duplicated genes in the human genome are duplicated and many are known to be associated with a number of human diseases. However, the short read lengths of current sequencing technologies make the analysis of such genes difficult. We have developed novel tools to genotype the copy number of duplicated genes using whole-genome sequencing. The goal of this project is to analyze large-scale sequencing datasets (using cloud computing platforms) for Mendelian and complex human diseases to identify novel disease associations. 

Haplotype-based variant calling using long-read sequencing

Long-read sequencing technologies have the potential to overcome some of the key limitations of short-read sequencing, particular in long repetitive regions of the human genome, but require the development of new algorithms. We have previously developed computational methods for variant calling (Longshot, Nature Communications 2019) and read mapping in segmental duplications (Duplomap, Nucleic Acids Research 2020) using long-read sequencing technologies. The goal of this project is to implement a haplotype-based model for variant calling using long reads that automatically identifies genomic regions that can be called with high confidence.

Plant systems biology

We have generated the most comprehensive quantitative proteomics description of a higher eukaryote. The data include protein abundance and phosphorylation levels of 20,000 proteins across 34 tissues and stages of development in maize (corn) and it is paired with RNAseq data. Projects are available for students to use machine learning (random generalized linear models) to model the relationships between regulators and targets. For example, we are comparing the protein abundance and phosphorylation levels of transcription factors to the mRNA abundance of all genes. There are many other types of regulators whose roles can be explored using this dataset. We evaluate modeled regulatory relationships using transgene expression in protoplasts. Models are assembled into networks and integrated with pathway annotations to predict phenotypic consequences of synthetic genes. These predictions are tested in transgenic plants. Students of bioinformatics and systems biology who wish to integrate computational with experimental training in chemistry and biology are especially encouraged to apply.

Advancing Pediatric Brain Tumor Diagnosis and Treatment through Deep Learning

The implementation of functional precision medicine, which combines genomic profiling with drug sensitivity testing of patient’s tumor cells, has potential to identify personalized and effective treatment options. This project aims to build treatment-response models based on available molecular profiles and patient-matched drug-response data obtained from pediatric brain tumor patients at Rady Children’s Hospital San Diego and at other cancer centers. The goal is to predict novel drug and biomarker pairs to enable better treatment decisions.

Developing next generation CRISPR tools

We have developed two novel technologies for rapidly generating pools of thousands of CRISPR variants and assaying their activities in a single study. Using these methods we aim to improve the efficacy of existing CRISPR tools along with endowing CRISPR tools with new activities.

Exploring protein language models to create enhanced protein variants

Our lab has been exploring the use of protein language models coupled with high-throughput protein variant screens to rapidly generate optimized protein variants. We are interested in applying these tools towards the amelioration of proteins misfolding and the optimization of gene editing tools.

Protein tagging at scale to generate a comprehensive map of the cell

We have recently reported a method for generating pools of cells, where each cell in the pool has a different protein tagged with either an affinity reagents (e.g. FLAG) or fluorescent protein (e.g. mCherry). Using this method, we are collaborating with several other groups on campus to generate a comprehensive map of the cell, elucidating the complexity in protein-nucleic acid and protein-protein interactions at scale and across genotype and environmental conditions.

Genomic study of brain MRI phenotypes

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.

Omic analyses for drug target identification

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.

3D brain organoid model of Alzheimer’s disease revealed by single cell transcriptomics

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.

Single cell transcriptomics and epigenetics of human Alzheimer’s disease 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.

Single cell transcriptomics and epigenetics of the opioid use disorder and HIV syndemic in the human brain

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.

Single Cell Transcriptomics of the Cocaine Use Disorder in the Context of HIV

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.

Deciphering the combinatorial code regulating maternal mRNA polyA tails from oocyte to embryo

mRNA polyadenosine (polyA) tail lengths play a unique and critical role in controlling gene expression in the developmental transition from oocyte to embryos from worms to humans. We have recently generated a large comprehensive profile of polyA tail lengths across the mouse oocyte-to-embryo transition with Nanopore long read sequencing, capturing tail length regulation with isoform-specific resolution (including 3'UTR length, splice isoform, polyadenylation site choice, etc.). We found dynamic changes in polyA tail length across this transition and that these changes in tail length control mRNA translation and stability. But what molecular mechanisms orchestrate these changes in polyA tail length?  In this project, we will apply machine learning approaches to ask which mRNA features (number and position of specific RNA binding protein motifs, mRNA length, mRNA abundance, polyadenylation site choice, 3'UTR length, etc.) are most predictive of (1) polyA tail length at each developmental stage and (2) change in tail length across consecutive developmental stage transitions.  These analyses provide an exciting opportunity to address questions decades-old questions as to the mechanisms driving the earliest stages of mammalian development.

Uncovering the molecular determinants of successful implantation of the human blastocyst

High rates of failed implantation in human embryos represents one of the greatest obstacles in our ability to treat infertility. Significant improvements require significant advances in our understanding of the molecular mechanisms required for successful implantation and ongoing development. The goal of this funded project is to uncover the network of regulatory factors and cell-cell signals that control cellular differentiation, developmental progression and successful implantation during early human embryo development using both human embryo and stem cell-based in vitro models. These analyses will provide the first “ground truth” atlas for human embryos of high implantation potential—and for stem cells developed to model differentiation within the embryo--and essential first steps toward development of an in vitro model for human embryo implantation.

Ancestry-aware genome-wide association studies

For the past 500 years, the American continent has been the site of ongoing mixing of Europeans, Native Americans, Africans and Asians, resulting in a significant percentage of Americans carrying ancestry from outside their self-identified race. However, genome-wide association studies (GWAS) and polygenic risk score (PRS) calculations have been performed and optimized on individuals of European descent, with minor exceptions. As part of CAST (Center for Admixture Science and Technology), we are developing methods to perform GWAS and calculate PRS on diverse and admixed populations, using large diverse cohorts, such as the All of Us and Million Veteran Program.

Mapping the mass spectrometry chemical space for structure

The chemical space is enormous, however molecules are related in chemistry behave similar by mass spectrometry. Mass spectrometry generates a output in numbers and these numbers can be correlated. In this project we want to build a comparative network to generate a predictive structural classification. If succesful, this project will transform therapeutic discovery, the investigations of signalling molecules and will benefit disease diagnosis.

Predictable disease prognosis network interactions of microbial interactions

For every human cell there are 10 microbes. Microbes are importan for our metabolism but also the proliferation and control of diseases. Our lab has been developing imaging mass spectrometry based tools to understand how neighboring microbes control the proliferation of pathogens. In this project we want to come up with a predictive model of the signalling molecules and microbial interactions.

Characterization of gene regulatory elements using multi-omic data

Characterization of gene regulatory elements using multi-omic data

Epigenetic Variation and Inheritance

The goal of this project is to understand the degree of epigenetic and genetic variation that occurs in Arabidopsis (a reference organism for all plants) . The rotation would involve characterization of DNA methylation, transcription and chromatin pattern in hundreds of individual strains collected from around the world. The project is expected to reveal new roles of epigenetic inheritance in many biological processes, some that affect plant fitness and adapation to climate.

Human ES/iPS Epigenomes

Our aim is to understand the contribution of the epigenome in reprogramming and differentiation of induced pluripotent stem (iPS) cells. We are using novel sequencing approaches to study reprogramming of the epigenomes of somatic cells to an ES-like state and the degree to which epigenomic reprogramming affects iPSC potential. Computational and experimental studies can be combined in this training experience. This project is a collaboration with Ron Evans' laboratory.

Mouse Brain Methylome

Epigenetic regulation of gene transcription, specifically when related to changes in DNA-methylation patterns (methylome), is a plausible mechanism underlying long-term environmental contributions to neuropsychiatric disorders. For example, pharmacological or environmentally-induced methylome alterations may lead to the silencing or aberrant activation of genes involved in the postnatal maturational process of brain circuitry, leading to functional and behavioral alterations appearing when the system reaches maturity. The proposed rotation project will contribute to creating complete maps of mouse brain methylomes, at the tissue and cell-type levels, during the period of postnatal development until adulthood. The goal is to delineate the methylome changes and transcriptional consequences produced by two developmental manipulations that lead to aberrant behaviors in adulthood, at the neuronal and peripheral tissue level. These reference for methylome and transcriptome databases consulted in relation to neuropsychiatric disorders with known and unknown developmental origins. This work is a collaboration with Marga Behrens/Terry Sejnowski.

Species comparisons of brain cell types

Computational analysis of multi-omic single cell data from 4 species (mouse/marmoset/macaque/human)

Dynamics of the vaccine response

Most vaccine studies measure the antibody response pre-vaccination and 1-month post-vaccination, but an ideal response must last for the duration of the influenza season (4 months) and ideally until you get your next vaccine (12 months). We will use pre-vaccination data to predict the response out as far as possible and quantify how additional measurements (e.g. at 1 month) further improve prediction accuracy.

Humans Versus Animal Responses

Most influenza surveillance utilizes antibody responses from ferrets (outnumbering the amount of human data by 10-fold). While it is known that ferret responses can differ from human responses, it is not clear when or how their responses will differ. Using ferret data, we will predict the value±error for human experiments, which will help refine influenza vaccine selection (that is currently determined through ferret studies).

Incorporating Virus Sequences

The influenza vaccine changes every 1-2 years. While we can separately model each vaccine, we would like to train a model on the combined data from all prior studies. Our current approach describes each virus according to its interactions with antibodies. By adding sequence information, we will develop a single unified model that can predict all past vaccines and explore the space of potential future vaccines.

Investigate fetal-specific cardiac regulatory variants and their overlap with cardiac GWAS lead variants

We have derived iPSC-CVPCs from 180 individuals and showed that their transcriptomes are more similar to fetal heart than to adult cardiac tissues. Our goal is to leverage these data in combination with WGS to perform eQTL analyses. We plan to assess whether fetal-specific eQTLs are associated with complex adult cardiac traits, by colocalizing eQTLs with summary statistics from GWAS (cardiac traits.) Our preliminary analyses show that eQTLs in iPSC-CVPCs identifies cardiac disease GWAS variants that are active in the fetal but not adult heart, indicating that they play a role in development. Our findings provide genetic evidence supporting the fetal origins of the cardiovascular disease hypothesis and highlight the importance of investigating genetic associations across stages of development (i.e. fetal and adult tissues) to fully understand the genetic underpinnings of complex traits and disease. We are looking for rotation students to conduct QTL analyses using large ATAC-seq and ChiP-seq for H3K27ac datasets generated from the iPSC-CVPCs.

Genetic and epigenomic fine-mapping of diabetes risk loci

This rotation 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. 

Predicting causal genes at diabetes risk loci

This rotation project involves 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

Predicting genome-wide pleiotropic effects of diabetes risk variants

This rotation project involves development of novel mixture model approaches to predicting and quantifying the extent of pleiotropy among diabetes risk variants genome-wide.

Natural genetic variation and macrophage gene expression

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.

Nature and nurture of microglia

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. 

Develop dynamic query system for supporting phenotype mining in genetic studies

Rady Children's Hospital San Diego is the second largest children's hospital in the country and has recently adopted an advanced electronic medical record system. The goal of this project is to develop code to search the clinical data archive to identify patients appropriate for clinical research in collaboration with other clinical bioinformatics groups on campus.

• Integrate hospital electronic medical record with an open-source environment for data mining to create a query system aimed at supporting identification of patients for research.
• Combine different conditions relative to the electronic medical record data (e.g., the presence of a particular pathology) to identify those most appropriate for study.
• Develop a multidimensional database to store/retrieve clinical data and update dynamically.

Informatics approaches in deciphering exomes

We are in the middle of analyzing exome data from an accruing group of >1500 individuals with brain disorders, and are looking to develop better algorithms to uncover pathogenic mutations.

• Develop code to sift through the 30 megabases of sequence per patient.
• Identify novel genes involved in disorders like epilepsy and autism.
• Develop platforms for storage, visualization, annotation, comparison and recovery of these large datasets.
• Improve ability to predict patient outcomes and identify "actionable items"

Characterize cell cycle asociated chromatin dynamics and epigenetic memory

Both DNA sequence and the organization of DNA associated proteins are transmitted during cell divisions. This organization is disrupted during the cell cycle, and the original structure of chromatin must be restored after each cell division, a process termed ‘epigenetic memory’. This project, in collaboration with the Simon and Gymrek labs, focuses on the mechanisms that provide cellular epigenetic memory. We merge cell cycle synchronization methods, automated ChIP-seq and computational framework to model temporal dynamics of CRs and histone modifications during the cell cycle and predict critical regulators of cell cycle epigenomic maintenance. We will functionally evaluate these predictions using an inducible degradation system to perturb specific CRs and then follow the dynamics of the histone modifications in the perturbed cells.

Study the principles of the genomic CR organization and recruitment

Chromatin structure, the genome-wide organization of DNA-associated proteins, plays a key role in cellular fate decisions. Histone modifications (HMs) are set (‘written’), removed (‘erased’) and bound (‘read’) by enzyme complexes termed Chromatin Regulators (CRs). Multiple recent studies have demonstrated that aberrations in the function of CRs are highly implicated in diseases such as cancer and developmental disorders. In our previous efforts we initiated a systematic study of the genomic organization of CRs. Following up on this, we employ our high-throughput automated ChIP-seq approach to map CRs along differentiation and use the data to computationally identify and predict key developmental CRs. We then test these predictions by perturbing the protein levels of these candidate CRs by inducible degradation and assaying the effects on differentiation.

Analyzing repeat expansions in human genomes

Tandem repeats (TRs) have been implicated in more than 40 Mendelian diseases, such as Fragile X Syndrome and Huntington's Disease. While a variety of tools can now profile short TRs from next generation sequencing data, these approaches do not immediately expand to longer TRs that cannot be spanned by a single sequencing read. In this project, we will model properties of read alignments at long TRs in order to build a statistical framework to detect expanded repeats in the genome. We will also evaluate the ability of long read (e.g. Nanopore, PacBio) and synthetic long read technologies (e.g. 10X Genomics) to capture long repeats.

Genome-wide association studies of short tandem repeats

Genome-wide association studies (GWAS) have uncovered thousands of genetic loci associated with human phenotypes. However, in most cases these loci fail to explain the majority of trait heritability. Importantly, GWAS has focused largely on simple single nucleotide polymorphisms (SNPs), and therefore are likely to miss important contributions from more complex types of variants. In this project we will (1) measure the power of SNP-based GWAS to detect repeat associations (2) evaluate the ability of haplotype-based tests to identify underlying repeat associations and (3) use these results to inform a method to perform STR association tests.

Phasing and imputing short tandem repeats

Imputation is a vital step of genome-wide association studies. It leverages the correlation structure in the genome induced by recombination to learn about genome-wide polymorphisms by only genotyping a small subset of variants. While imputation of single nucleotide polymorphisms (SNPs) has proven to be quite robust, statistical phasing and imputation of tandem repeats (TRs) in unrelated samples is challenging, largely because TRs and SNPs have diminished linkage disequilibrium due to the fast mutation rates and high prevalence of recurrent mutations in TRs. The goal of this project is to evaluate phasing and imputation techniques at TRs by leveraging two types of data: (1) family-based data, which allows tracing inheritance patterns to infer phase and (2) long read and synthetic long read technologies, which allows physical phasing of TRs with nearby SNPs.

Understanding the design principles of gene regulatory networks

Understanding the design principles of gene regulatory networks using systems and synthetic biology approaches. In particular, we will study the function of parallel transcription factor systems in single cells.

Understanding the feedback regulation of MAPK system

We will use the MAPK system in yeast as a model to study the function of feedback regulation, in particular, how feedback loops contribute to signal specificity and cellular information encoding. We will combine single-cell imaging analysis, quantitative biochemical assays, and computational modeling in the project.

Modeling the dynamics of gene regulation

This rotation project will involve two questions in the area of modeling the dynamics of gene regulation. We have generated fluorescence data from a system where a promoter is driven periodically with an external inducer and the resulting gene expression has been measured using GFP. The first question involves the deduction of a mesoscopic dynamical model from the data, and how this approach can be generalized to characterize a library of promoters for use in constructing genetic circuits. The second question is how the data and the resulting mesoscopic model can be used to constrain a large set of parameters that define a microscopic model.

Bionformatics of Mass Spectrometry Peptide Spectral Libraries for Identification of Neuropeptides

Identification of small peptides by mass spectrometry is a distinct discipline compared to protein identification. Small ‘peptides’ in biological tissues encompass a large range of amino acid lengths from about 3-40 residues; whereas, ‘tryptic peptides’ derived from proteins have a narrow range of length of about 7/8-20 residues. Current mass spectrometry instrumentation and bioinformatics are not well designed for ‘peptides’, but have been designed for ‘proteins’ that are identified by tryptic digests. The bioinformatic challenge in this project is to design new algorithms based on peptide spectral mass spectrometry data for identification of peptides of unusual lengths. This project has tremendous relevance to all biological systems, because we are now missing identification of an entire class of peptides. The computational peptide spectral library has high impact for the biological and bioomedical sciences. This project is being conducted as a collaboration with the laboratories of Dr. Vivian Hook and Dr. Nuno Bandeira.

Neuropeptidomic Profiles in the Brain and Neurological Diseases

Multiple peptide neurotransmitters are are essential for cell-cell communication among neuronal cells of the nervous system for regulation of brain and physiological organ systems. The bioinformatic challenge to address in current neuropeptide research is how to define profiles of neuropeptides, neuropeptidomes, utilized in health and diseases of the nervous system? These ‘neuropeptidomes’ will be investigated by nano-LC-MS/MS mass spectrometry with particular attention to organization of bioinformatic tools to optimize peptide identifications. An example of this research was published this year (Gupta et al., 2010). This research is conducted as a collaboration by the laboratories of Dr. Vivian Hook, Dr. Pavel Pevzner, and Dr. Nuno Bandeira.

Quantitative studies of bacterial physiology

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.

Isoform transcriptome of Cul3-HET mouse model

The project deals with constructing the isoform-level co-expression and protein interaction networks for predicting functional impact of mutations in high risk autism gene Cul3. We have collected RNA-seq and TMT-proteomics data from various brain regions of Cul3+/- transgenic mouse. We are aiming at integrating isoform-level RNA-seq data with quantitative proteomic (peptide-level) data from the same samples to understand the impact of Cul3 mutation.

Isoform transcriptome of patient-derived cerebral organoids from 16p11.2 CNV carriers with autism

Copy number variants (CNVs) represent significant risk factors for Autism Spectrum Disorders (ASD). One of the most frequent CNVs involved in ASD is a deletion or duplication of the 16p11.2 CNV locus, spanning 29 protein-coding genes. Despite the progress in linking 16p11.2 genetic changes with the phenotypic (macrocephaly and microcephaly) abnormalities in the patients and model organisms, the specific molecular pathways impacted by this CNV remain unknown. We generated bulk RNA-seq and TMT proteomic data from patient-derived cerebral organoids (3 deletion, 3 duplication and 3 control patients). The goal of the project is to analyze isoform-level RNA-seq data, as well as proteomics data to investigate functional impact of 16p11.2 CNV.

Computing a minimal set of genes required for life

A long standing question in biology is how many (and which) genes are required for life. This essential core set of genes, or minimal genome, makes up the cell's “life support system” or “chassis and power supply” on which more complex functions and processes are built. This set of genes is of keen interest in the field Synthetic Biology, which aims to synthesize the complete minimal genome of an organism and add additional functions to this genome for biotechnological, pharmaceutical and agricultural ends. This project will attempt to use our whole-cell model of the networks and pathways in a cell to predict which genes and gene combinations are essential for life and, conversely, which genes and gene combinations can be removed. If successful, this project will be able to predict minimal genomes for synthesis and testing. It will also address whether there actually is a single “minimal genome” or whether there exist many different configurations all of which are near or at the global minimum.

Prerequisites: Computer programming or scripting skills.
Optional: Experimental laboratory skills, which would allow student to make tests of model predictions.

Development of a software pipeline for generating cell function hierarchies from genomic data

We have developed algorithms (NeXO and CliXO) by which systematic datasets are used to organize genes into a gene ontology, reflecting the hierarchical organization of cellular structures and molecular pathways in the cell. Currently these algorithms are coded in Python; however, a user-friendly and expandable interface would allow end-users to quickly build and update gene ontologies from new data sets. Coding of this interface is the main goal of this rotation; If successful, this tool could seed a thesis project to construct a gene ontology for a particular cellular process (e.g. DNA damage response) or disease (e.g. cancer) of interest.

Prerequisites: Computer programming or scripting skills; some knowledge of genomic biology.

Experimental mapping of the DNA damage response

Cell colonies on agar grow in a near linear fashion with growth rates reflective of their "fitness". The laboratory has developed an experimental platform that can make continuous measurements of growth rates via time-lapse image capture of thousands of specific genetic mutant strains, enabling us to determine the relevance of every gene in the response to stimuli such as DNA damage via radiation or chemotherapy. During the rotation the student will grow ~50,000 cell colonies in parallel and capture their growth curves using digital images and intermittent radiation exposure. The project includes working in Matlab for the analysis of growth curves and the elucidation of DNA damage response pathways. If successful, the project could be developed into a thesis which uses these data to construct a hierarchical model of DNA damage responses.

Prerequisites: Prior experience in a genetics or biochemistry experimental laboratory.

Using a hierarchical cellular model to analyze tumor genetic mutations

The student will explore whether a hierarchical model we have recently constructed for predicting growth of simple cells can be translated to predict aggressiveness of human cancer. The model will be provided, along with access to tumor exomes from both public and internal sources. The goal is to determine, over a 10 week rotation, whether and to what extent the model can be used to analyze a patient's exome. If so, this project could be readily developed into a PhD thesis.

Prerequisites: Computer programming or scripting skills; some knowledge of genomic biology.

Single-cell Physiology

We will use the “mother machine,” a microfluidic continuous cell culture device developed in our lab, to follow thousands of individual cells for hundreds of consecutive generations. We will analyze the images using high-throughput image analysis methods that use advanced algorithms. We will focus on the single-cell physiology in the context of cell size control, cell cycle control, and cell death.

Autophagy protein and functional networks

• Expand on our ongoing co-immunoprecipitation and mass spectrometry datasets to identify protein-protein interactions involved in autophagy.
• In collaboration with the Ideker lab and the SDCSB Network Assembly Core, analyze coIP results and incorporate functional data into an ‘autophagy network’.
• Test new insights predicted from network by in vivo autophagy assays.
• Future directions include a hierarchical analysis of dynamic protein-protein interactions in an autophagy timecourse, and RNAi screens of the autophagy network and for new autophagy gene functions.

High-throughput image analysis of cell morphology

• In collaboration with the Tsimring lab at the BioCircuits Institute, optimize newly developed machine learning image analysis algorithms to quantify cell shape and cell shape changes.
• Conduct new RNAi screens to test optimized image analysis algorithms, and employ established methodology to identify new and modifying (enhancer/suppressor) gene functions in cellular remodeling.
• Future directions for long-term projects include use of successful image analysis algorithms in other applications, development of related methods for dynamic analysis of cell shape changes, and/or the analysis of large-scale RNAi screen image data into functional networks.

Machine Learning for the Microbiome

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).

Multi-omics integration

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.

Optimizing microbiome algorithms

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).

Analysis of synthetic lethality in mammalian cells

A number of genetic defects that cause increased cancer susceptibility, such as mutations in the breast cancer susceptibility gene BRCA2, cause defects in DNA repair and lead to increased genome instability. We have used yeast to identify genes that when mutated cause increased genome instability and have also identified other genes, so called synthetic lethal genes, in which mutations specifically kill cells that contain a mutation causing increased genome instability cause lethality. We are using RNAi knockdown and chemical genetic approaches with human tumor cell lines containing defined genetic defects to determine if the same synthetic lethal relationships can be demonstrated in human tumor cells. This project will provide experience in basic molecular biology, yeast genetics and mammalian genetic approaches.

Sakai, W, Swisher, EM, Jacquemont, C, Venkatapoorna Chandramohn, K, Couch, FJ, Langdon, SP, Wurz, K, Higgins, J, Villegar, E, and Taniguchi, T. Functional restoration of BRCA2 protein by secondary BRCA2 mutations in BRCA2-mutated ovarian carcinoma. Cancer Res. 2009;69:6381-6.

High throughput yeast genetics analysis of pathways that prevent genome instability

We have developed assays that allow us to measure the rates of accumulation of genome rearrangements such as translocations in the yeast Saccharomyces cerevisiae as well as different assays that allow us to measure DNA damage responses that underlie the formation of genome rearrangements, such as a fluorescence based assay for phosphorylation-dependent degradation of Sml1 that can be followed microscopically. We have adapted these assays for use in a high-throughput robotic mating scheme against a selected subset of the yeast deletion collection to systematically generate single and double mutants containing these assays. Analysis of these mutants will allow elucidation of the genetic networks that prevent genome instability as well as those that function in the DNA damage response. Variations on this project include performing a global mutant analysis, investigating specific pathways such as double strand break repair and their interacting pathways, and bioinformatics analysis of the genetic data for network construction and using next generation sequencing methods for characterizing the structure of genome rearrangements. This project will provide experience in basic molecular biology, yeast genetics, basic cell biology methods, bioinformatics network construction, next generation DNA sequencing and the use of robotic methods.

Putnam, CD, Hayes, TK, and Kolodner, RD. Specific pathways prevent duplication-mediated genome rearrangements. Nature. 2009; 460:984-989.

Discovery of biomarkers of pregnancy complications

Using extracellular RNA, proteomic, and metabolomic analysis of maternal blood, we are discovering biomarkers associated with a variety of pregnancy complications, including preeclampsia, preterm birth, and fetal growth restriction.

Discovery of regulatory network driving cellular differentiation

Integrative genomic approaches, including DNA methylation and chromatin analyses, as well as bulk and single-cell RNA sequencing, are used to study the stepwise progression of human pluripotent stem cells during differentiation to the pancreatic, hepatic, and placental lineages.

Integrating chromatin readers, the epigenetic landscape, and specific cellular outputs

The genome wide distribution of chromatin binding proteins is influence by many factors including the local chromatin environment, other chromatin modifications, and the underlying DNA sequence.  Once recruited to chromatin such proteins often further modify chromatin structure and function, setting into motion a complex and dynamic interplay between chromatin and specific cellular machinery that is required to orchestrate diverse biological processes.   We are interested in integrating these genomic and epigenomic features, as assessed using next generation sequencing technology, within the context of specific chromatin readers to gain insight into their biological functions on a genome-wide scale.  Several projects along these lines are available.

aiDose: A foundation model for clinical decision support in hospitalized patients with diabetes

Hospitalized patients with diabetes require daily management by a team of clinicians that must make daily insulin dosing decisions. While each decision is usually made according to standard algorithms, hundreds of such decisions must be made daily by a few providers leading to errors and burnout; this project will address this clinical burden by developing aiDose, a foundation model for inpatient diabetes management that can incorporate medical records and previous clinician notes to provide decision support for inpatient diabetes services.

Longitudinal metabolic analysis of Diabetes Prevention Program (DPP) participants to identify patient subgroups with differential micro and macrovascular complication risk

Type 2 Diabetes (T2D) complications cause morbidity and mortality, but occur heterogeneously among those at risk and thus are difficult to predict. Previous studies to identify individuals at risk of diabetic complications focus on single timepoint data for a few features and do not examine phenotypic variables over time. This project will analyze multiple longitudinal clinical phenotypes to identify clusters of individuals at risk of diabetic complications.

Pre-adipocyte cell fate reprogramming

Adipose tissue secretes cytokines to regulate essential functions, but comprehensive study is prevented by difficult-to-access depots such as visceral epicardial adipose tissue and differences between donor sources and methods to generate cell lines. This project will start with an isogenic population of cultured human preadipocytes and reprogram them to specific types of adipocytes using a combinatorial process analogous to that of induced pluripotent stem cells. This project includes working with single cell (sc)-RNAseq and sc-ATACseq datasets using XGBoost and other models.

Analysis of gene network perturbations in human cells

In this project we will develop new methods for perturbation and analysis of gene networks in pluripotent stem cells using the CRISPR-Cas systems. The student will have an opportunity to understand the experimental procedures for human genome engineering, to learn the state-of-the-art methods for bioinformatics analysis ranging from computational reagent design to next generation sequencing to network modeling, and to also develop innovative strategies for enhanced multiplexed reverse genetic screens in human cells.

Computer-aided Drug Discovery

Current drug discovery projects are targeting SARS-CoV-2, TB and other pathogens.

Systems Biology of Hypoxia Tolerance and Susceptibility

This project combines bioinformatics and systems biology to design experiments, analyze high-throughput/high­‐content data (on DNA sequence, gene expression, protein‐DNA interactions and metabolomics), reconstruct network models and develop new hypotheses on the molecular mechanisms of hypoxia susceptibility and tolerance in Drosophila, mouse and humans. This project includes the opportunity to interact with investigators from several labs and to participate in experimental studies and data acquisition as well as performing systems biology analysis. Individual rotation projects will address smaller questions within this overall theme based on the specific interests of the student.

A genetic approach to identifying enhancer-promoter interactions

Enhancers activate genes from a distance, but little is understood about how they find the correct promoters to regulate. Enhancers will often activate non-adjacent genes and will 'skip-over' the promoters of nearby genes. The goal of this project is to develop a method to identify genetic variants that jointly affect enhancer activity and transcription from promoters (i.e. joint enhancer-promoter QTLs). This will allow us to determine which enhancers regulate which genes and learn about the sequences that are involved in enhancer-promoter interaction.

Discovery of genetic variants that disrupt enhancer-promoter looping

Enhancers-promoter interaction is believed to occur through chromatin looping. The goal of this project is to identify genetic variants that disrupt chromatin looping between enhancers and promoters using Hi-C experimental data.

Using chromatin to identify important cell types for Systemic Lupus Erythematosus (SLE)

SLE is a complex autoimmune disease that is poorly understood. The goal of this project is to use cell-type specific chromatin marks and GWAS data to determine which cell types are most relevant to this disease. This project will involve development and/or application of methods that can correctly take into account linkage disequilibrium and sub-genome-wide significant GWAS hits to identify cell-type specific enrichments of GWAS signals. SLE is far more common in women than in men, and potentially it will be possible to use sex-specific chromatin accessibility or marks, (depending on what samples are available).

Metabolic flux profiling in cancer

Systems biology rotation projects are available to probe various metabolic pathways in cancer cells, including carbohydrate, fatty acid, and amino acid metabolism as well as redox pathways. Students will have the opportunity to build network models and generate MFA data in engineered cancer cells.

Genome skimming (for ecology)

In this project, we aim to develop cheap methods for evaluating ecology using shallow sequencing. Methods such as genome skimming have the potential to enhance the study of biodiversity, but new algorithms are needed to enable such studies. That's the goal of this effort. Most methods we develop are kmer-based. The work involves both working with real data and simulations. We work with partners from outside academia as well. The work is shared with Prof. Bafna.

Machine learning for phylogenomics

In this broadly defined project, we develop new machine-learning methods to infer or use phylogenies. The goal is to see if machine learning methods can beat existing methods in specific tasks such as phylogenetic placement or outlier detection. The focus is on finding innovative ways to update machine learning methods or adopt them to phylogenetics. We also work on incorporating phylogenies as prior data into machine learning methods for answering other questions (e.g., metagenomic classification).

Phylogenomic methods

In this broadly defined project, we develop novel methods for inferring phylogenetic trees from genome-wide datasets. The project involves modeling evolutionary processes, developing scalable algorithms, analyzing their statistical properties in theory, applying them to large simulated and biological data to evaluate them, and providing software to the community.

Analysis of rat genetic and genomic data

The Palmer lab hosts a NIDA-funded National Center of Excellence to examine the role of genetic differences in a variety of complex rat behaviors related to drug abuse (www.ratgenes.org). As of March 2020, we have already phenotyped and genotyped over ~7,000  rats as part of this project, and have secured funding to collect an additional 8,000 rats. In addition, we have previously collected similar data from more than 4,000 rats from a different population as well as several thousand mice and are now in the process of collecting similar data on over 5,000 zebra fish. In addition to genotypes at millions of SNP markers, and complex behavioral observations spanning weeks to months, we are also examining RNASeq (including single cell RNASeq), Epigenetic, microbiome and metaboloic data from these populations. The analysis, integration and development of new methods for analysis create numerous opportunities for bioinformatics projects and interactions with the bench scientists who are creating these data. We also collecting human genetic data and are exploring various methods for integrating human and non-human datasets. 

Method development and quantitative data analysis for understanding cellular physiology

Projects are regularly available for developing novel systems biology methods for analyzing cellular physiology. Key application areas include cellular resource allocation, principles of evolutionary optimization, designing microbial cell factories, and human health applications in both bacterial and mammalian systems [doi:10.1038/nrg3643]. Additionally, our lab runs experiments using automated adaptive laboratory evolution (ALE) to observe bacterial evolution in real-time [doi:10.1093/molbev/msu209]. A number of research projects are available for new students to participate in ALE experiments and analyze strains optimized by ALE.

Error-correction in next generation sequencing data: applications to structural variations

The recent emergence of high-throughput sequencing technologies has revolutionized genomics by providing a new wealth of data for biologists to learn from. It has facilitated our ability to discover the genomic sequence of novel species through a process called sequence assembly. However, even for a human, whose genome has been already assembled, different individuals have slightly variant genomes, accounting for a myriad of important phenotypical differences. Discovering this variation is often the starting point for finding the underlying genetic causes of diseases, and has, among other things, improved our abilities for early detection of cancer. Despite these exciting applications, the growth of sequencing technology has outpaced the development of downstream algorithms. Many assembly and variation discovery projects have been done without properly addressing a key property of the data – it is prone to errors and is not always reliable. Algorithms for error- correction have been inadequate in many assembly projects and simply non-existent in most variation discovery projects. Accurate error-correction algorithms will have a wide-effect on the quality of downstream algorithms, since they correct potential mistakes at the very early stages.

The proposed project is to design an effective error-correction algorithm (with an eye towards variation detection and assembly). The design of such an algorithm is a challenging problem, and though we have general ideas, most design decisions remain to be made. The project therefore requires a motivated student with a superb understanding of algorithms and a good handle on programming. Since the datasets for this problem are hundreds of gigabytes in size, an understanding of data structures is crucial. Some background in either machine learning or graph theory is a plus, though not required. No biological knowledge is required.

The successful completion of this project will ideally lead to a publication in a leading conference or journal. More importantly, it is a good stepping stone into understanding the field of assembly and variation detection and into other related projects in Pavel Pevzner’s lab.

RNA regulation of immunity

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.

Changes in chromatin accessibility during differentiation

To study changes in chromatin during cell fate determination, we are using several models of differentiation in the haematopoietic system as well as transdifferentiation and reprogramming.  The combination of RNA-seq, ChIP and ATAC-seq (Assay for Transposase-Accessible Chromatin, [PMID: 24097267]) has revealed a striking interplay between chromatin regulators involved in histone and DNA modifications.  Additionally, we are using RNA-interference with shRNAs targeting transcription factors and chromatin-related genes to characterize changes in chromatin and gene expression.

Identification of long-range interactions in chromatin mediated by transcription factors (ChIA-PET)

Regulatory elements that control gene expression are often positioned far away from the coding regions of genes. The regulatory regions can be “looped”, bringing them and their associated DNA binding proteins close to a gene’s promoter or other regulatory element. We recently identified a unique structural homodimer of the transcription factor FOXP3 that enables this dimer to bind two sites in DNA that may be separated by long distances [PMID: 21458306]. FOXP3 controls the function of regulatory T cells, and mutations in FOXP3 that abolish dimer formation are associated with an autoimmune syndrome called IPEX, owing to defects in T regulatory cell function [PMID: 22224781]. We are using a method known as ChIA-PET [PMID: 22926262] and several next generation sequencing strategies to assess how the FOXP3 homodimer mediates long-range interactions in DNA and controls T regulatory cell function.

Interplay between histone and DNA modifications

Histone ubiquitylation is an important modification that is less studied than the more standard histone modifications (methylation, acetylation, phosphorylation).  The two histones that are modified by ubiquitylation are H2A and H2B.  H2A ubiquitylation and deubiquitylation are controlled by several enzymes, many of which are frequently mutated or otherwise dysregulated in different cancer.  Because the DNA methylcytosine oxidase TET2 [PMID: 24220273] and ASXL1, a component of an H2A deubiquitinase complex [PMID: 20436459], are frequently co-mutated in haematopoietic cancers, we are studying the relation between H2A ubiquitylation and DNA methylation status in normal cells and during cancer development.

Mapping DNA methylation and oxi-mC distribution

Contribution of TET proteins to DNA methylation patterns and gene expression during normal differentiation and in cancer (mapping DNA methylation and oxi-mC distribution).  Cytosine methylation is an important modification in DNA and controls gene expression by altering transcription factor binding and local chromatin structure.  Our lab discovered the enzymatic activity of Ten-eleven translocation (TET) proteins [PMID: 19372391], dioxygenases that modify DNA methylation patterns by oxidizing 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxycytosine (5-caC).  We have developed tools to map the location of these modified bases in DNA by next-generation sequencing [PMID: 21552279, 24474761], and have generated mice conditionally deficient for each of the three TET proteins.  We are using these methods and reagents to explore how TET proteins, 5mC and the oxidised methylcytosines (5hmC, 5fC and 5caC, together termed oxi-mC) influence cell differentiation and cancer.

Mapping enhancers to interrogate the role of single nucleotide polymorphisms (SNPs) in immune diseases

We have recently completed a study in which we used ChIP-seq for the enhancer-associated modification H3K4me2 to identify enhancer associated with T cell differentiation in healthy subjects as well as patients with asthma [Seumois G et al., Nature Immunology, in press].  Similar studies will be ongoing over the next few years.

Modelling of single-cell RNA biology

We are using single-cell RNA-seq to investigate the regulation of gene expression during normal and dysregulated differentiation of mouse embryos and embryonic stem cells. To identify key regulatory factors of gene regulation, it will be crucial to apply existing computational methods and develop novel computational methods for the analysis of these single-cell RNA-seq data, including methods for unsupervised clustering, differential expression, and time-series analyses.

Animating spatial transcriptomics

Current approaches profiling the transcriptome of cells in tissues offer a rich yet immobile snapshot of biology. In collaboration with the Kosuri Lab at the Salk Institute, we are developing approaches to infer cell kinematics and tissue infiltration timelines to obtain a finer temporal view of key processes that impact T cell differentiation and function during an anti-tumoral response. This project will rely on the development of both computational tools and wet lab protocols.

Leveraging spatial multi-omics to the study of tissue immunity

Understanding how tissue immune networks operate require obtaining the position, transcriptional state and environmental context of each cell within an organ. We use spatial transcriptomics, combined with protein stains and other approaches, to survey immune cell responses in mouse and human tissues using models of infection and cancer. Ongoing efforts in the lab are focused on establishing pipelines that enable analysis of these large datasets. We employ two different spatial transcriptomics technologies: optically-read probe-based transcript capture at single-molecule resolution (Xenium) and array-barcoded probe-captured sequencing at ~2 um resolution (Visium-HD). Your project will focus on developing tools to obtain biological insights, including: custom probe set design based on training data (feature selection algorithms), advanced probe set design (sequence optimization algorithms), cell segmentation and image registration, integration of spatial transcriptomics and protein readouts (Python and R), batch correction and multi-tissue integration, custom binning and pseudo-single nucleus analysis (Visium-HD), cell typing, neighborhood enrichment analysis, spatial gene set enrichment analysis, cell networks and ligand-receptor interaction analysis. These tools will be applied to the analysis of an ongoing consortia initiative aimed at profiling every immune cell in the mouse (ImmGenMaps). In addition to developing the computational aspect of this work, you will be trained and encouraged to design and execute experiments to generate your own datasets.

Dynamic chromatin landscapes in differentiating human ES cells

As part of the NIH Roadmap epigenome project, my lab has comprehensively mapped various chromatin modification status genome-wide in the human ES cells and several partially differentiated cell types derived from the ES cells. Rotation project will be focused on understanding how chromatin modifications dynamics is related to differentiation and cell fate determination.

Study of the role and regulation of long-range genomic interactions in the human genome

Many gene regulatory elements can regulate target genes located at a long distance, and this has been thought to occur through DNA-looping. However, the exact role and mechanisms of long-range DNA interactions in human cells have remained to be resolved. We have been able to generate comprehensive maps long-range DNA interactions for different cell types, and are finding interesting principles of genome organization in mammalian cells. Rotation projects will be focused on understanding the relationships between long-range DNA interactions and regulation of gene expression, and the development of predictive models of gene expression.

Systematic identification and characterization of transcriptional regulatory elements in the mouse genome

The laboratory, as part of the mouse ENCODE project, is in the midst of producing the first comprehensive map of cis-regulatory elements in the mouse genome. Computational analyses are being conducted to identify the regulatory sequences controlling tissue-specific gene expression, determine the evolutionary conservation of these sequences, and predict potential transcription factors involved in lineage specification and animal development.

Constraints and selection that drive gene family evolution

High quality genome sequences are increasingly available that cover entire genera. This facilitates fine scale investigations of the forces that drive protein evolution. We are using the Caenorhabditis (roundworm) genus and the Saccharomyces (yesat) genus to study the role of selection and constraint on gene family evolution in the context of developmental and physiological networks. A rotation project would include phylogenetic analyses of gene families, evolutionary tests for selection and constraint, and integration with mechanistic and functional data from systems biology.

Image segmentation and nuclei identification using deep learning

C. elegans embryos are powerful model systems for studying developmental variation using microscopy because they are transparent and can be fluorescently marked. However, automatic detection/segmentation of fluorescently marked nuclei is a challenging image informatics problem. This rotation would consist of applying deep learning techniques to this problem based on a corups of images that have been previously manually curated. Experience with python is useful and previous exposure/experience with deep learning would also be useful.

Simulations of genetic network evolution

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.

Statistics on the morphogenesis of hybrid inviability

Hybrids between different species do not, as a rule, do well. We are using microscopy, image informatics, and statistics to understand the developmental biology of hybrid incompatibility. We are imaging the complete development (3D position of every nucleus over time) of hybrid worm embryos in trying to determine whether there are particularly sensitive parts in development where genomes of different species prove particularly incompatible, even if other parts of development proceed properly. A rotation project on this topic would include image informatics in processing the microscopy images and statistical investigation of variation in this complicated trait.

Bioinformatics Rotation Projects

Potential projects include:

• Projects employing use of genome-wide technologies, including ChIP-seq, GRO-seq, CLIPseq-, RNA-seq, and ChIRP-seq, to elucidate molecular mechanisms of regulated enhancer lncRNA actions in cancer and stem cells;
• Roles and mechanisms of enhancer actions in prostate and breast cancers;
• Enhancer-based model of neurodevelopment and CNS disorders;
• New mechanisms of long non-coding RNAs dictating physiological gene regulation in cancer transcriptional programs;
• Understanding subnuclear structures: Roles of relocation of transcription units between subnuclear architectural structures in regulated gene expression;
• Chemical library screens to gene signature and translocation responses as an approach toward new cancer therapeutic reagents;
• Roles of epigenomic regulators and expression of DNA repeats in stem cells, neuronal differentiation and in senescence.

Systems Biology and Engineering of Environmental and Drought Tolerance in Plants

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 heavy metal stress. These environmental (“abiotic”) stresses have substantial negative impacts and reduce global plant growth and biomass production. 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 recent research advances are being used in the biotechnology industry with the goal of enhancing stress resistance of plants and crop yields.

A rotation project will be pursued to model and identify the drought stress 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 21^st Century.  See https://labs.biology.ucsd.edu/schroeder/ for more information on the Schroeder lab.

Selected publications

• Nishimura et al., Science (2009).
• H.H. Hu et al., Nature Cell Biol. (2010).
• T.H. Kim et al. Current Biol (2011).
• Xue et al., EMBO J. (2011).
• F. Hauser et al. Current Biol (2011).
• B. Brandt et al., PNAS (2012).
• R. Waadt et al., eLife (2014).
• A.M. Jones et al., Science (2014).
• C.B. Engineer et al., Nature (2014).
• B. Brandt, S. Munemasa et al. eLife (2015).
• See also: https://labs.biology.ucsd.edu/schroeder/publications.html

Complex Genetic Inheritance of Rare and Common Variation in Autism

To get a more granular understanding of how rare variants and common variants contribute to quantitative variation in psychiatric traits, my lab is leading the whole genome analysis of rare genetic diseases that are being deeply characterized with dimensional measures of psychopathology. In collaboration with clinical research groups at several institutions, we have formed a consortium of rare disease research groups called the Genes to Mental Health Network (G2MH). This project will investigate how multiple genetic factors influence clinical features of autism using rare variant genotypes and polygenic risk scores derived from whole genome sequencing. The trainee on this project will learn how to carry out statistical genetic analysis of large whole genome and phenotype datasets.

Whole-genome Analysis of Autism by Pacific Biosciences HiFi genome sequencing

Using new 3rd-generation DNA sequencing technologies, it is now possible to perform complete assembly of chromosomes from telomere to telomere.  This project will investigate new categories of genetic variation that are detectable using the PacBio Sequel II whole genome sequencing (WGS) platform that are not detectable by traditional Illumina WGS, including structural variants (SVs) and tandem repeats (TRs). The trainee on this project will become familiar with basic tools for long read HiFi sequence analysis and will innovate upon current statistical genetic methods to investigate the association of SVs and TRs with disease with a focus on genes located within new regions of the human genome that have recently been assembled.

COVID-19 recovery monitoring

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.

Diversity within physiological data

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.

Improving women’s health outcomes

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.

Cholinergic neurotransmission in the central and peripheral nervous system

Dr. Palmer Taylor's laboraotry studies cholinergic neurotransmission in the central and peripheral nervous system. Studies are directed to the structure and function of nicotinic acetylcholine receptors and acetylcholinesterase. The ultimate object is to use structure guided drug design to develop more efficacous drugs in the study of nervous system development and disorders of ageing. Various pharmacological and analytical techniques are employed. Examples can be found in Dr. Taylor's curriculum vitae and his publications.

Rotation Project for Systems Biology

PKA is a broad spectrum kinase that has many protein substrates. It consists of regulatory (R) and catalytic (C) subunits and assembles into an inactive tetramer (R2C2) in the absence of cAMP. Binding of cAMP to the dimeric R-subunits unleashes the catalytic activity. There are four functionally non-redundant R-subunits (RIα, RIβ, RIIα, and RIIβ) and three C-subunits (Cα, Cβ, and Cγ). The GPCRs are the most abundant gene family encoded for by the human genome and many of these couple to cyclases that generate the cAMP second messenger. In addition to PKA R and C-subunits and PKA substrates, the PKA proteome includes scaffold proteins called A Kinase Anchoring Proteins (AKAPs) that target PKA to specific sites in the cell at the correct time. This dynamic spatio-temporal aspect is essential for correct PKA signaling in cells. One of our goals is to identify the proteins that are part of this PKA proteome and to establish how this proteome is altered in response to stress signals such as starvation and to the cell cycle. In addition, we hope to establish how the proteome varies as a consequence of disease or of genetic perturbation. To initially profile the PKA proteome we will be using two cell/tissue types, S49 mouse lymphoma cells and heart tissue. Eventually we will extend this analysis to mouse macrophages (RAW cells). In each case we have perturbed the PKA signaling pathway. In the S49 cells we have generated a mutant cell line that makes no active C-subunit. Although the protein is expressed in these cells it is not active, is not soluble, and remains associated with particulate fractions. In RAW cells we have silenced the Cα and Cβ genes. Our initial goal is to compare each of the wild type S49 cells lines with the cell lines where PKA function has been perturbed. To do this we will use mass spectrometry to identify the proteins that change as well as the phosphoproteome, and these changes will be compared to changes in gene expression. The S49 project will be done in collaboration with Paul Insel (Pharmacology) and Nuno Bandiera (Pharmacy/Computer Science) while the macrophage study will be done in collaboration with Mel Simon (Pharmacology). Similar profiling is being done for cardiac myocytes, and this will be done in collaboration with Hemal Patel (Medicine) and Andrew McCulloch (Bioengineering).

Bioinformatics Rotation Projects Available

There are three projects available for Summer 2009.

1. The first project is in comparative genomics and concerns genome rearrangements. I work on algorithms for comparing rearranged genomes, and on applying them to real data sets. Expertise in algorithm development and discrete mathematics is preferred.
2. The second project is joint with Pavel Pevzner and concerns genome assembly algorithms. Our current focus is on single cell assembly.
3. The third project is joint with Vineet Bafna. We are developing a method to find pairs of genes that interact with disease status in GWAS microarray data. Finding simple diseases caused by one gene can be done efficiently by brute force in time O(mn) (m = # genes, n = # patients), but brute force for pairs of genes would require time O(m² n), which may be prohibitive. We developed a new mathematical technique for computing χ² of a contingency table that allows us to find the interacting pairs more easily. This project involves statistics, abstract algebra, and programming.

Hardware acceleration of computational genomics

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. 

SARS-CoV-2 Phylogenetics

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.

Elucidating Gene Regulation by Leveraging Genetic Polymorphisms and Epigenetics to Uncover Disease Mechanisms

Our research focuses on understanding how genetic polymorphisms influence disease by affecting transcriptomic and epigenomic profiles in the immune system. However, most of these genetic polymorphisms are located within the non-coding regions of the genome, which are areas that do not directly code for proteins. This makes their interpretation particularly challenging, as their functional role is not immediately apparent. We use next-generation sequencing technologies to identify genetic polymorphisms associated with molecular traits, like changes in gene expression, and leverage information from epigenomic data (ChIP-seq, ATAC-seq, etc.) to pinpoint disease mechanisms.

Identification of genetic variants affecting transcription factor binding

Identifying genetic variants associated with transcription factor binding. We have implemented a published linear regression model to analyze the allele frequency within DNA, enriched using chromatin immuno-precipitation techniques for variant discovery in primary human immune cell subsets. The relevance of this research is to narrow down the molecular mechanism by which non-coding genetic variants may influence gene expression, contribute to complex phenotypes and influence an individual's susceptibility to disease.

Single-cell multimodal profiling of immune cells in head&neck cancers

We have assembled a large cohort of donors with various head&neck cancers, who have kindly provided matched tumorigenic and healthy adjacent tissue samples. We have profiled a set of immune cells from these samples, namely T cells, which have been extensively shown to play an important role in cancer immunotherapy. Analyses of the healthy adjacent tissue samples are undergoing, and we will be reporting on a comprehensive T-cell atlas of the head&neck compartments. Yet, there remain massive data from the tumor-related side of the project to be analyzed. These data include single-cell RNA-seq, surface protein expression and T-cell receptor (TCR)-seq modalities, all spanning multiple anatomical sites as well as different types of head&neck cancers.

Bioinformatics Rotation Projects Available

We are interested in studying the biological and physical principles underlying genetic networks and protein recognition. Rotation projects are available in the following areas. Specific projects will be tailored to fit a student's research interest and scientific background.

1. Decipher epigenetic code: develop computational methods to identify common patterns in the histone modification and DNA methylation data associated with regulatory elements; predict regulatory elements, transcription factor binding sites or non-coding RNAs based on their chromatin signatures.
2. Reconstruct genetic networks: reconstruct physical interactions from genomic and proteomic data; analyze the robustness and landscape of the networks.
3. Decipher protein recognition code: employ structure-based computer modeling to characterize the energetic patterns of protein-protein and protein-ligand interactions; predict the specificity of protein recognition.
4. Design resistance-evading drugs: computer-aided drug design to combat resistance; develop new methods for drug lead optimization.

More information can be found at http://wanglab.ucsd.edu.

Image-based reconstruction of biochemical networks in live cells

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.

Intelligent Diagnosis of Infectious Diseases by Deep Learning

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.

Drug resistance and population analysis of Mozambique malaria samples

Malaria remains a major problem for 40% of the world's population and drug resistance is widespread.  One mechanism for identifying drug resistance determinants is by identifying regions that show unexpected homozygosity in whole genome sequences.  The rotation student will work with physician scientists to align short read sequences to the P. falciparum genome, call variants, annotate variants, run population genetics analyses and produce reports.  

Evolutionary theory

This project develops basic evolutionary theory, with relevance to biomedical applications. For example, we study the evolution and emergence of mutants in spatially structured populations under various assumptions. Much remains to be discovered about the principles of mutant evolution in structured populations, and this has important applications for cancer biology and cancer therapy, since most tumor grow as a mass of cells with strong spatial structure.

Mathematical models on in vivo virus dynamics

The project will be concerned with mathematical models of virus replication within hosts, and the interactions of viruses with immune responses. Much of this modeling work is concerned with human immunodeficiency virus (HIV), due to the availability of experimental and clinical data. Topics include the evolution of HIV within hosts, the effect of spatial lymphoid tissue structure on HIV dynamics and evolution, and the dynamics of HIV during antiretroviral therapy in relation to the latent viral reservoir.

Mathematical Oncology

This project is concerned with mathematical models of cancer initiation, cancer progression, and cancer therapy. This involves mathematical models of tissue stem cell dynamics, clonal cellular evolution in tissues during aging in relation to the development of cancer, and evolutionary models of drug resistance in cancers. Hematological malignancies are a major focus of this work. With respect to therapies and drug resistance, this work involves the use of mathematical models with patient-specific parameters to make personalized predictions about treatment outcome.

Single-cell RNA-seq analysis

We have projects that deal with developing new algorithms for single-cell RNA-seq analysis pertaining to studying heterogeneity in complex mixtures of cells upon environmental challenges.

Automatic Blood Pressure Control with Machine Learning

This project seeks to develop novel deep learning methods to forecast and control patients blood pressure using large-scale sensor data from artificial heart pump.

Allele-specific gene regulations

Each human cell contains two sets of chromosomes of the paternal and maternal origins respectively. Due to the presence of genetic polymorphisms, a same gene on the two chromosome sets might behave differently, which has profound implications in high-level phenotypes, including disease susceptibilities. In this rotation project, the student will analyze RNA sequencing or methylation sequencing data to identify genes that exhibit parental preferences and investigate the functional implications of such phenomena. Towards the end of the rotation, the student will learn a number of latest methods in mapping and analyzing next-generation sequencing data, and how to study transcriptome/methylome in the context of genetic polymorphisms.

Single-cell genome sequencing: de novo assembly and annotations

Single-cell genome sequencing allows us to dissect a heterogenous cell populations and to relate the genome diversity to functions. One major challenge is de novo genome assembly, because the sequencing data from single cells are typically highly biased in locus representation. In this rotation, the student will have an opportunity to understand the experimental procedures for single-cell genome sequencing, to learn the state-of-the-art methods for de novo genome assembly, and to develop innovative strategies for genome assembly and annotations.

Discovery of extracellular RNA biomarkers from human blood serum and plasma

Extracellular RNA (exRNA) sequencing from human liquid biopsy has exhibited the potential for development of future disease diagnosis. Multiple rotation openings are created to characterize the fundamental properties of exRNA based on exRNA sequencing data and discover exRNA biomarkers for Alzheimer's disease and cancer.

Mapping the spatial transcriptome for human blood vessels

Through a close collaboration between the bioinformatician and the molecular biologists, this project aims to develop new methods to sequence RNA with spatial resolution from human tissue. A breakthrough of this project will lead to simultaneously improvements of spatial resolution, the dynamic range and applicable dimensions of human tissue from the state-of-art spatial transcriptomics technologies.

Single-cell multiplex chromatin and RNA interactions in ageing human brain

Dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA. Chromatin complex compositions change during cellular differentiation and ageing, and are expected to be highly heterogeneous among terminally differentiated single cells. We developed the multinucleic acid interaction mapping in single cells (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression and RNA–chromatin associations within individual nuclei (Nature 2024). When applied to 14 human frontal cortex samples from older donors, MUSIC delineated diverse cortical cell types and states. We observed that nuclei exhibiting fewer short-range chromatin interactions were correlated with both an ‘older’ transcriptomic signature and Alzheimer’s disease pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci and a promoter tends to be that in which these cis expression quantitative trait loci specifically affect the expression of their target gene. This project further analyzes these data and other brain single-cell datasets to evaluate the hypothesis of a chromatin “clock” of single-cell ageing, and delineate the cell-type specific gene regulatory programs.

The relationship between genome-wide RNA-chromatin interactions and 3D genome organization

It remains unclear to what extent chromatin-associated RNAs can reflect the 3D organization of the genome. To this end, we used iMARGI to map genome-wide RNA-chromatin interactions in H1, HFFc6, and K562 cells and yielded on average 1.9 billion read pairs per sample. This project will compare these iMARGI data with genome interaction data including Hi-C and PLAC-seq on three different scales. At the compartment scale, we will test whether the A compartment chromatin is associated with large amounts of RNAs, involving both intrachromosomal and interchromosomal RNA-chromatin interactions. At the TAD scale, we will test whether the RNA ends of nearly all RNA-chromatin interactions are confined to within the boundaries of one or of a few consecutive TADs. At the loop scale, we will test whether RNA-chromatin interactions are enriched with PLAC-seq derived enhancer-promoter interactions.

Proteomics analysis of protein complexes involved in genome maintenance

We develop quantitative proteomics technologies to study several key protein complexes involved in DNA replication and chromosome segregation, regarding their composition, dynamics, stability and regulation. Insights from the proteomics analysis are further pursued to study how cells maintain their genome integrity.