bioinformatics minor courses

 

General Information
There are plenty of opportunities for Bioinformatics research projects at UCLA. This program is designed to help interested students find research projects related to Bioinformatics across campus. Typically, these projects are for credit; in exceptional circumstances they may offer funding. Participation in research projects can both significantly improve your chances of admittance into top graduate programs and make you a much more competitive employment candidate. Even better, it gives you something to talk about during an interview. Feel free to contact us even if you do not know exactly whether or not you want to work on a research project or know the field you wish to research in. Please remember that every undergraduate and masters student is welcome to participate in research, regardless of your background or year in the program. Undergraduates are STRONGLY encouraged to participate in research as early as possible in their careers. Ideally, you should start a research project during your sophomore year, but it is never too late or to early to start! Undergraduate students may receive up to 8 units credit toward the minor with enrollment in Computer Science 194/199 or Bioinformatics 194/199.

General Procedure
If you are reasonably sure which project you would like to work on, use the contact information listed under the project to contact the person responsible for the project directly to set up a meeting. If you are not sure, but you are even slightly interested in research, feel free to email us or drop in to help chose an appropriate project. Most students take a project for course credit, although funding may be available in some cases. You can contact Eleazar Eskin (eeskin [at] cs [dot] ucla [dot] edu) if you have any questions.

Research Projects
Below is a list of research projects that are accepting undergraduate researchers.

Polygenic Evolution of Cancer

Project Description
In this project we will look at how tumors evolve under constraints from the human immune system and clinical treatment. It is well known that individual mutations can improve tumor fitness and provide protection from therapy. However, the extent to which polygenic effects alter tumor evolution is an open question. To address this gap we will leverage polygenic risk scores computed from the UKBB and apply them to somatic events in tumors. This offers the opportunity for a motivated student to learn about both germline and somatic genetic variation.
Requirements
One course in programming (such as PIC 10A or CS 31), one bioinformatics core course (such as CM121, CM122, or CM124).
Contact
Noah Zaitlen
nzaitlen [at] ucla [dot] edu
Possibility of Funding?
Yes

O-PTM in Cardiovascular Biology and Medicine

Project Description
In a cardiac cell, the proteome consists of more than 200,000 proteins. Multiple proteins interact with each other to form a biological pathway. Each pathway performs a function and supports a cellular process. Changing the function of an individual protein may lead to alterations on the function of the entire pathway. Post-translational modification (PTM) is a common mechanism regulating protein structure and function. Oxidative stress is a redox imbalance when the generation and accumulation of reactive oxygen species (ROS) exceed the endogenous antioxidant capacity of living organisms. It is often involved with the progression of cardiovascular diseases (CVD). Oxidative stress sensitive post-translational modifications (O-PTMs) are typical features of proteins in human hearts; these O-PTMs are associated with healthy and/or diseased conditions.
Project leaders: Dr. Ding Wang (dingwang [at] g [dot] ucla [dot] edu), Dr. Dominic Ng (dominicng [at] g [dot] ucla [dot] edu), Dr. Howard Choi (cjh9595 [at] g [dot] ucla [dot] edu)
Education goals:
Oxidative stress biology: get familiar with common reactive oxygen species (ROS), ROS-generating enzymes, and antioxidants.
O-PTMs: get familiar with 15 types of O-PTMs, know their AA targets and changes in m/z value.
Extract O-PTM signatures of proteins: get components associated with a CV-relevant biological pathway; get their identification, subcellular distribution, and O-PTMs (e.g., modification type, modification site, occupancy).
Scientific goals: Identify O-PTM changes unique to health and disease conditions of human hearts. The similarity and differences between human and mouse protein homologues will be compared. These findings may offer opportunities to interpret phenotypic observations in human HF and mouse models under stress.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

Bioinformatics Pipelines for Proteomics Data Analyses

Project Description
Bioinformatics tools, including the Integrated Proteomics Pipeline (IP2), in-house generated software packages, are employed to characterize properties of individual protein at the proteome-level, in a high-throughput fashion. Publicly available kownledgebases (e.g., Uniprot & Reactome) support proteomics data analyses and enable further data interpretation.
Project leader: Dr. Howard Choi (cjh9595 [at] g [dot] ucla [dot] edu)
Education goals: Students will be introduced to several bioinformatics tools essential for proteomics data analyses. After the training, they will be able to independently utilize these resources to characterize biological variables of interest (e.g., Proteins, O-PTMs) from raw proteomics datasets.
Scientific goals: Understand the fundamental concepts and/or algorithms of these bioinformatics resources. Get comfortable in applying bioinformatics tools to better characterize biological systems. They should develop a data-driven mindset different to the conventional hypothesis-driven approaches that once dominated biomedical investigations.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

Mass Spectrometry (MS)-based Proteomics in Cardiovascular Research

Project Description
Proteomics is the large-scale study of proteomes within a biological system. Building on advances in mass spectrometry and data sciences, proteomics approaches have offered powerful means in understanding of cardiovascular diseases. Massive mass spectrometry datasets are the intersection between proteomics and data science. In this project, students will learn the proteomics sample processing techniques and gain the knowledge in mass spectrometry for applying downstream data analysis on studying cardiovascular diseases.
Project leaders: Dr. Ding Wang (dingwang [at] g [dot] ucla [dot] edu), Dr. Dominic Ng (dominicng [at] g [dot] ucla [dot] edu), Dr. Howard Choi (cjh9595 [at] g [dot] ucla [dot] edu)
Education goals: Students will learn the fundamental concepts of mass spectrometry, get familiar with sample preparation protocols and data acquisition workflow for MS-based proteomics, and learn how to extract the MS data for downstream data.
Scientific goals: Introduce fundamental concepts of mass spectrometry and proteomics to students. After the training, the students will be able to tell the differences between Top-down and bottom-up approaches, apprehend standard proteomic applications in biomedical research, and know what information can be retrieved from proteomic datasets.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

Knowledge Graph construction and analysis to support heart failure classification

Project Description
New cases of heart failure, or HF, are diagnosed by the millions each year. Not all hearts fail in the same manner, however: HF cases may be categorized by their percentage of healthy ejection fraction, or EF. An EF below 40% is considered HF with reduced EF (HFrEF) while HF with an EF greater than 50% – while often physiologically normal outside the context of disease – constitutes HF with preserved ejection fraction, or HFpEF. HFpEF is increasingly common and is distinguished from HFrEF by a variety of presentation factors, patient traits, comorbidities, and other factors such as systemic inflammation. How may we organize these varied factors in a consistent manner? If clinical and biomolecular correlates with HFrEF or HFpEF are structured as relationships, may we assemble them into a knowledge graph? What may this knowledge graph allow us to infer regarding HF classification?
Project leader: Harry Caufield (jcaufield [at] mednet [dot] ucla [dot] edu)
Education goals: An understanding of the technical methods required to integrate heterogeneous biomedical relationships described in text and knowledge bases. Skills to gain familiarity with include: data retrieval through APIs, text data analysis and natural language processing with Python, and data management in Neo4j. The ability to analyze knowledge graphs (and, by extension, other networks of biomedical relationships) to identify relationships supporting conclusions about cardiovascular disease. Students will also gain knowledge of the symptomology of heart disease.
Scientific goals: Identify specific patterns of biomedical relationships associated with specific subtypes of heart failure, such that text describing heart failure may be classified without explicit definitions being present (e.g., HFpEF may be described implicitly).
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

Constructing an Integrated Cardiovascular Knowledge Graph to Discover Disease Phenotype Relationships

Project Description
Modern bioinformatics and biomedical informatics projects rely upon well-curated knowledge bases and data repositories. These resources contain structured information describing proteins (e.g., UniProtKB), biomolecular interactions (e.g., IntAct), or genotype-phenotype relationships (e.g., OMIM), among numerous other topics. Similarly, carefully engineered ontologies and coding systems define relationships between diseases (e.g., Disease Ontology; ICD) or broader sets of biomedical concepts (e.g., MeSH). Though each of these resources are data-rich and highly valuable, we rarely need to use any one of them in their entirety – and we would like to use knowledge curated from multiple sources, even when their structures present obstacles to data integration. By exploring the subset of each knowledge base and ontology through the perspective of cardiovascular disease research, we may identify the most relevant elements and unify them within a single graph structure. The resulting knowledge graph supports asking complex questions about cardiovascular phenomena. With some additional engineering, higher-level representations of these knowledge graphs can drive machine learning approaches for understanding cardiovascular disease.
Project leader: Harry Caufield (jcaufield [at] mednet [dot] ucla [dot] edu)
Education goals: An understanding of the technical methods required to integrate heterogeneous biomedical relationships described in text and knowledge bases. Skills to gain familiarity with include: data retrieval through APIs, text data analysis and natural language processing with Python, and data management in Neo4j. Experience with the data formats and structures used to store biomolecular data and metadata, as well as ontologies (e.g., OBO or OWL formats) and other data (e.g., JSON).
Scientific goals: Assemble a consistently-structured knowledge resource optimized for phenomena relevant to cardiovascular disease, including relationships between disease phenotypes, biomolecules, biomolecular pathways, symptoms, and therapeutics. Identify best practices for merging specific knowledge sources. Develop reusable code for obtaining and integrating knowledge base contents.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

Mapping Collective Knowledge of the Cardiac Proteome

Project Description
By definition, we expect that a proteome lists each protein within a particular tissue or organ. A cardiac proteome, for example, should include identities and amounts of each protein in the heart. This definition becomes clouded once we begin considering specific conditions: how does an unhealthy (e.g., hypertrophic or failing) heart’s proteome differ from that of a healthy one? Does the proteome change over time? How may the proteome vary between hearts from male or female individuals? Our ability to address these questions may be limited by the samples used to define each proteome as well as by inherent experimental variability. We may search across current and past literature to rigorously define and merge differing (and in some cases, conflicting) observations of cardiac protein expression, with the goal of assembling an updated proteome of the human heart. This process requires intensive application of text mining coupled with an understanding of cardiac-specific biological pathways. This project will place particular focus on three types of proteins: contractile proteins, proteins impacted by oxidative stress, and proteins with metabolic functions (especially those involved in branched chain amino acid, or BCAA, metabolism) as these topics are foci of other lab efforts. Assembly of an updated cardiac proteome will produce a crucial reference for classification of a peptide’s relevance to the heart.
Project leader: Harry Caufield (jcaufield [at] mednet [dot] ucla [dot] edu)
Education goals: An understanding of PubMed and the language used in biomedical research literature. Experience with obtaining text data through an API. Familiarity with computational methods for bibliometrics, text mining, information extraction, and natural language processing. Knowledge of biomolecular pathways in cardiac function.
Scientific goals: Construction of a literature-derived cardiac proteome, serving as a comprehensive resource for identification of proteins most relevant to healthy and diseased cardiac phenotypes.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

A study of Covid-19 Knowledge Graphs for different Age Groups and CVD Cases

Project Description
Covid-19 is caused by a coronavirus called SARS-CoV-2 and often presents with symptoms of high fever, cough and shortness of breath. In severe cases, Covid-19 may lead to acute respiratory distress syndrome (ARDS) and multiple organ dysfunction and eventually to death. It is clear that the severity and mortality of Covid-19 is much higher than any other known coronaviruses. New data from Covid-19 cases have indicated that the severity and mortality of this disease are significantly higher in elderly patients and patients with a history of CVD. Applying a Text Mining approach, the students will explore the role of risk factors such as aging and several cardiovascular diseases (e.g., coronary artery disease) on the severity of Covid-19, and unravel possible underlying mechanisms.
Project leaders: David Liem (dliem [at] mednet [dot] ucla [dot] edu), Dibakar Sigdel (sigdeldkr [at] gmail [dot] com)
Education goals: Students will learn how to apply innovative tools in text mining and knowledge graphs (e.g., Neo4J and Spark) for data exploration and for the development of search algorithms with specific tasks in biomedical scenarios.
Scientific goals: Students will learn how to hypothesize meaningful biomedical questions from available tools and databases in CVD and Covid-19. (e.g., Which age groups and pre-existing CVD significantly increase the risk of mortality in Covid-19, and what are the underlying mechanisms?) The search results can be further explored to investigate the underlying age based mechanism.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

A study of Drug to Cardiovascular Disease (CVD) Associations with SemRep and Deep Learning

Project Description
Starting with well defined oxidative stress categories (e.g., Initiation, Regulation and Outcome of Oxidative Stress) and a list of drugs in cardiovascular disease (CVD), we will explore SemRep to extract all relevant SPO- triplets. We further build knowledge graphs with these triplets and prepare a muli-order association matrix to represent graph data structure. Using this graph structure, we will build a sequence prediction model for drug to CVD association. This project will provide a detailed analysis of drugs to CVD association with both qualitative evidence and quantitative scores.
Project leaders: David Liem (dliem [at] mednet [dot] ucla [dot] edu), Dibakar Sigdel (sigdeldkr [at] gmail [dot] com)
Education goals: The students will learn how to work with innovative text mining tools (e.g., SemRep, CaseOLAP, Neo4J) for biomedical documents and machine learning approach (RNN, LSTM) for model development and implementation to answer important biomedical questions.
Scientific goals: The students will explore knowledge graphs for drug and CVD associations with a focus on oxidative stress categories (e.g., Initiation, Regulation and Outcome) and underlying molecular mechanism.
Requirements
No required experience.
Contact
Peipei Ping
ppingucla [at] gmail [dot] com
Possibility of Funding?
Yes

Genomic analyses of 150k genomes within the UCLA ATLAS

Project Description
New and existing computational methods for ancestry inference, mixed models association, fine-mapping, transcriptome-wide studies(TWAS), mendelian randomization will be developed and applied to a collection of 150k genomes from UCLA patients linked to their electronic health records.
Requirements
One year of programming coursework (such as PIC 10C or CS 32).
Contact
Bogdan Pasaniuc
pasaniuc [at] ucla [dot] edu
Possibility of Funding?
Academic Credit, Paid

Analysis of complex behavior in mice

Project Description
​​Social interactions between individuals and among groups are a hallmark of human society as we know it and are critical to the physical and mental health of a wide variety of species including humans. Our lab studies how animal social behavior is regulated in the brain. This project involves analysis of complex behavior during animal social interaction. Work can be done remotely.
Requirements
No required experience, but some basic skills of MATLAB would be great.
Contact
Weizhe Hong
whong [at] ucla [dot] edu
Possibility of Funding?
Academic credit and work-study positions for eligible students.

Genetic architecture of neuropsychiatric traits

Project Description
We use genomic data to study the genetic architecture of psychiatric disorders such as schizophrenia and bipolar disorder. Bioinformatic tools are used to decipher clinical features as well as genetic susceptibility, epigenetic features and regulation of gene expression. Student projects are tailored to the interest and skill set of the student.
Requirements
One year of programming coursework (such as PIC 10C or CS 32).
Contact
Roel Ophoff
ophoff [at] ucla [dot] edu
Possibility of Funding?
Academic Credit/Volunteer Only

Structure-function relationship of chromatin architecture in cell quiescence

Project Description
We are recruiting a student to work on a project that aims to study the changes in global chromatin accessibility and structure when the proliferating cells enter and exit a non-dividing quiescent state. We have previously observed widespread gene expression changes between proliferating and quiescent cells and one of the goals of the project would be to understand the link between gene expression and chromatin architecture changes during quiescence entry and exit. The student will primarily work on analyzing next-generation sequencing datasets that we have generated as well as publicly available data. Previous experiences in R, Python, shell scripting, RNA-seq analysis, handling big genomic datasets, and using UCLA Hoffman2 cluster are strongly desired.
Requirements
Coursework in programming or bioinformatics, basic statistics and linear algebra
Contact
Hilary Coller
hcoller [at] gmail [dot] com
Possibility of Funding?
Yes

Comparative epigenetic studies of aging in 150 mammalian species

Project Description
Help us find the the secret behind differences in maximum lifespan across mammalian species.
Why does a shrew live for less than 2 years while a bowhead whale can live for more than 200 years? Why do rats live for less than 7 years while the naked mole rat can live for more than 30 years? Help us to annotate genomic locations and chromatin states in many species.
What can we learn from DNA methylation sites that correlate to maximum lifespan and age in different species.
Requirements
One Bioinformatics core course such as CM121, CM122 or CM124
Contact
Steve Horvath / Jason Ernst
shorvath [at] mednet [dot] ucla [dot] edu / jason [dot] ernst [at] ucla [dot] edu
Possibility of Funding?
Yes

Epigenetic Biomarkers

Project Description
Our lab is interested in the development of DNA methylation biomarkers for health and disease. This includes biomarkers for aging from saliva and blood as well as biomarkers for organ specific diseases from plasma. We develop tools to analyze DNA methylation data and develop biomarkers using machine learning.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Matteo Pellegrini
matteope [at] gmail [dot] com
Possibility of Funding?
No

Development of Statistical and Computational Methods for Single Cell Genomics

Project Description
The recent experimental advances in single cell biology have allowed us to learn highly granular biological information of disease at single cell resolution. Despite the availability of a plethora of methods to better tease apart true biological signals from technical noise, there are numerous challenges in addressing complex biological questions. We strive to develop computational, mathematical, and statistical models to better harness single cell genomics to advance our understanding of disease mechanisms using our in-house data sets as well as provide tools to the larger biological community for robust data analysis. Projects include batch correction, extraction of gene regulatory networks, prediction of transcriptional response to perturbations, and others!
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Xia Yang
xyang123 [at] ucla [dot] edu
Possibility of Funding?
No

Harmonizing Tissue and Single Cell Multi-omics to Elucidate Regulatory Networks in Disease and Treatment

Project Description
More and more evidence suggests that most diseases are the cultivation of complex molecular interactions in the form of regulatory networks within select cell types and between cell types. We use both tissue and single cell level multi-omics to understand the tissue- and cell type specific mechanisms behind diseases as well as potential therapeutic treatments that aim to reverse these disease networks. Our research involves the investigation of a broad range of complex diseases encompassing cardiometabolic diseases (heart disease, diabetes, obesity, fatty liver disease) and brain disorders (Alzheimer’s disease, traumatic brain injury, and neuropsychiatric disorders), to identify the underlying molecular networks within and between diseases.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Xia Yang
xyang123 [at] ucla [dot] edu
Possibility of Funding?
No

Evolution in the Microbiome

Project Description
While the taxonomic composition of the human microbiome has been extensively studied, little is known about how these microbes evolve. In the Garud lab (garud.eeb.ucla.edu), we are studying the evolutionary forces within and between hosts that shape microbiome genetic diversity (recombination, drift, selection) (e.g., see Garud et al. 2019 PLoS Biology). The lab develops statistical and computational methods to gain insight into evolutionary processes from population genomic data.A variety of projects are available and can be tailored to the student’s interest. A few of them include:
1. Quantifying selective sweeps across human host using linkage disequilibrium statistics
2. Estimating the distribution of fitness effects across hosts using site frequency spectrum statistics
3. Quantifying adaptation within a host using spatial metagenomic data collected along the mouse gut.
Projects will include a combination of data analysis, simulations, and literature search.

The lab is situated in the Ecology and Evolutionary Biology Department and has close interactions, including joint lab meetings, with Dr. Kirk Lohmueller’s lab (there may be options for pursuing a project that is co-advised by Dr. Garud and Dr. Lohmueller). The lab is affiliated with the Microbiome Center at UCLA, the Institute for Quantitative and Computational Biology, and the California NanoSystems Institute at UCLA.

Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Nandita Garud
ngarud [at] ucla [dot] edu
Possibility of Funding?
No

Epigenetic Biomarkers of metabolic health

Project Description
Our lab is interested in the development of DNA methylation biomarkers. These biomarkers can be used to study aging and human health. We have developed experimental approaches to carry out targeted bisulfite sequencing to measure the DNA methylation of specific sites of interest. We have also developed computational methods to model the epigenetic state of a sample based on its methylation level. The combination of these techniques can be use to estimate the age and health of individuals from blood or saliva samples. We would also like to develop approaches that combine genetic and epigenetic data to better model the epigenetic changes in an individual.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Matteo Pellegrini
matteop [at] mcdb [dot] ucla [dot] edu
Possibility of Funding?
Yes

Projects in Cancer Data Science

Project Description
We study cancer, trying to understand how it originates and what makes it lethal. We use data arising from DNA & RNA sequencing, mass-spectrometry, clinical records and images. To analyze them, we develop and apply biostatistical and machine-learning approaches. We try to generate clinically-useful tools, while simultaneously discovering new areas of cancer biology.The team is a multi-disciplinary group of computer scientists, software engineers, statisticians, biologists, chemists and clinicians. People come to the team with all levels of programming, of statistics and of cancer biology. We’re used to training people in the areas they don’t know, and have projects suited to all levels of experience.

For software-engineering focused students, typical projects will involve creating dev-ops infrastructure (e.g. CICD), optimizing high-performance code, containerizing software for cloud-based deployment or developing web-services.

For data-science-focused students, projects will involve optimizing ML-based workflows (e.g. hyper-parameter tuning), applied-ML on high-dimensional datasets, or developing new algorithms for quantifying specific features of cancer.

For biology-focused students, projects will involve pre-processing and analyzing high-throughput experimental data, and linking it to fundamental aspects of cancer biology like hypoxia or cell proliferation.

Recent publications from undergrad or medical students in our team:
https://www.ncbi.nlm.nih.gov/pubmed/31161221
https://www.ncbi.nlm.nih.gov/pubmed/30665349
https://www.ncbi.nlm.nih.gov/pubmed/30390622
https://www.ncbi.nlm.nih.gov/pubmed/30253747
https://www.ncbi.nlm.nih.gov/pubmed/30216362
https://www.ncbi.nlm.nih.gov/pubmed/29385983

Requirements
Projects available at all levels
Contact
Dr. Paul C. Boutros
pboutros [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
Yes

Discovery of cell-type specific expression signals conritbuting to cardiometabolic disorders in humans

Project Description
Cardiometabolic disorders, such as type 2 diabetes and non-alcoholic fatty liver disease, are major causes of morbidity and mortality world-wide. We are developing and applying integrative genomics approaches utilizing genome-wide variant and single cell RNA-sequencing data from metabolic tissues to decompose cell-type proportions and cell-type specific expression of genes and their connections to cardiometabolic traits.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Paivi Pajukanta
ppajukanta [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
Yes

Decoding Neural Signals for Brain-Computer Interface Communication

Project Description
Patients with neuromuscular disorders such as ALS lose the ability to communicate. The goal of this project is to restore this ability by translating neural signals recorded by EEG into computer commands. Several projects are ongoing, involving programming (C++, MATLAB, and Python), machine learning, natural language processing, and experimental design.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
William Speier
Speier [at] ucla [dot] edu
Possibility of Funding?
No

Analysis of Whole Exome Sequencing Data of Patients with Undiagnosed Neurological Disorders

Project Description
Our lab is interested in improving genomic testing methods to improve the diagnosis of rare neurogenetic conditions in patients presenting with neurodegenerative diseases, specifically cerebellar ataxia. Among our projects is the development of a large searchable data repository where we can re-evaluate previously performed exome sequencing with the latest analysis and annotation pipelines periodically to identify rare diseases as well as create large datasets to evaluate risk alleles and genetic modifiers in this patient population.
Requirements
One Bioinformatics core course such as CM121, CM122 or CM124
Contact
Kathie Ngo or Brent Fogel
kjngo [at] mednet [dot] ucla [dot] edu or bfogel [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
No

Human brain genomic investigation of psychiatric disorders

Project Description
Most neuropsychiatric disorders, such as autism, bipolar disorder, or schizophrenia, are highly heritable. Recent large-scale genetic association studies have begun to identify robust genetic variants associated with these disorders, with thousands likely contributing. However, none of these variants are individually sufficient to cause these disorders, and likely hundreds to thousands of variants contribute within a given affected individual. Our group uses multiple genetic and genomic approaches to understand this polygenicity of psychiatric traits and the neurobiological mechanisms through which risk is conferred. In particular, we perform next generation DNA and RNA-sequencing on human brain samples from individuals with psychiatric diagnoses and matched controls, to characterize differential patterns of gene expression and co-expression networks. In addition, we characterize the impact of genetic risk for psychiatric traits in large-scale population-level biobanks and electronic health records. These large-scale datasets provide tremendous opportunity for motivated students to develop or apply bioinformatic/computational methods to gain new insights into the biological basis of psychiatric disorders. Helpful skills include: basic understanding of genetics and statistics, familiarity with linux and some programming experience.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Michael Gandal
mgandal [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
Yes

Genetic regulation of human brain development

Project Description
The human brain is the most complex organ in existence. Brain development is genetically encoded yet our understanding of the complex genetic programs controlling this process are limited. Notably, the genes expressed in the brain are the longest in the genome, with the largest number of exons, and the greatest degree of alternative splicing — compared with other human organ systems and across species. This suggests that regulation of transcript-isoform expression during brain development is an important mechanism, yet this has not been explored in detail. This project seeks to characterize genetic regulation of transcript-isoform expression in human fetal and adult brain, using next generation DNA and RNA-sequencing technologies. We will apply statistical and computational methods such as elastic net regression to train weights linking specific genetic variants with nearby transcript isoform expression. These weights can then be used to run a transcriptome wide association study (TWAS), to prioritize candidate causal risk genes/isoforms for brain-relevant traits such as psychiatric disorders.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Michael Gandal
mgandal [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
Yes

Using Machine Learning to Integrate RNA-Seq and Lipidomics Datasets to Discover Novel Gene Regulation

Project Description
We have gathered a wealth of RNA-seq and lipidomics data from the livers of mice exhibiting early-stage phenotypes of non-alcoholic fatty liver disease (NAFLD). The aim of the project is to identify a novel set of transcriptomic and lipidomic biomarkers in NAFLD regulation. We are looking for motivated individuals who are interested in analyzing RNA-seq data and applying machine learning approaches towards deciphering biological processes.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Thomas A. Vallim
tvallim [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
Course Credit Possible

Methods for Analyzing the Non-Coding Human Genome

Project Description
We are interested in developing computational methods to better annotate and understand the non-coding human genome,
and more specifically applying methods to analyze rare non-coding variation from whole genome sequencing data studying psychiatric disorders and other traits.
Potential projects could involve integrating large-scale epigenomic data, comparative genomic data, and/or high-throughput functional testing data
with whole genome sequencing data.
Requirements
One year of programming coursework such as PIC 10C or CS 32, plus one bioinformatics core
Contact
Jason Ernst
jason [dot] ernst [at] ucla [dot] edu
Possibility of Funding?
Yes

Identifying loci for regulation of RNA splicing in mice

Project Description
We have obtained deep RNA sequencing data from a panel of inbred mouse strains. The genome of these strains is well characterized, allowing fine mapping of loci involved in regulating gene expression. The project is to identify loci involved in splice site selection.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Des Smith
DSmith [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
No

Investigating Differential Isoform Expression with Cell Cycle Exit

Project Description
We generated next generation sequencing datasets that provide information on the expression of different isoforms of genes in cells that are cycling and cells that have exited the proliferative cell cycle. We are recruiting a student to assist with the analysis of these datasets and determining the biological importance of changes in isoform expression. The following skills would be help the student to be most successful in the project: familiarity with programming in R, basic statistics, RNA-seq analysis, motif searching.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Hilary Coller
hcoller [at] ucla [dot] edu
Possibility of Funding?
No

Human microbiome data analysis

Project Description
16S and metagenomic data analysis of the human microbiome
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Huiying Li
huiying [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
No

Genomic studies of psychiatric disorders

Project Description
We use genomic data to study the genetic architecture of psychiatric disorders such as schizophrenia and bipolar disorder. Bioinformatic tools are used to decipher clinical features as well as genetic susceptibility, epigenetic features and regulation of gene expression. Student projects are tailored to the interest and skill set of the student.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Roel A. Ophoff
ophoff [at] ucla [dot] edu
Possibility of Funding?
No

The evolutionary dynamics of cephalopods

Project Description
Living cephalopods (octopuses, squid, and nautiluses) comprise over 700 species but their evolution is thought to reflect a series of “arms races” with other marine predators including sharks, marine reptiles, and ancient and modern fishes that has led to the waxing and waning of species richness through time. I am seeing an undergraduate student with some programming experience to compile occurrence data from fossil databases and conduct comparative evolutionary analyses that will measure changing rates of speciation and extinction and test arms race hypotheses.
Requirements
One year of programming coursework such as PIC 10C or CS 32
Contact
Michael Alfaro
michaelalfaro [at] ucla [dot] edu
Possibility of Funding?
No

Building and analyzing the fish tree of life

Project Description
We are currently assembling the largest phylogenetic tree of vertebrates based upon published gene sequences and seek one or more students to assist with scripting and analysis. This project involves creating multi gene alignments from genetic databases, reconciling Genbank taxonomy with published classifications, phylogenetic reconstruction, and macroevolutionary analyses.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Michael Alfaro
michaelalfaro [at] ucla [dot] edu
Possibility of Funding?
No

Crowdsourcing of phenotypic data

Project Description
We are developing software tools through Amazon mechanical turk to enable crowdsourced collection of shape data on a massive scale. This project will involve development of software protocols for data collection and analysis of geometric morphometric data.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Michael Alfaro
michaelalfaro [at] ucla [dot] edu
Possibility of Funding?
No

Application of integrative omics analysis pipelines for cancer systems biology and immunity studies

Project Description
Recent advances in cancer biology have shown massive changes in the transcriptome, proteome and metabolome of tumor specimen in response to drug treatment and acquired resistance. Our lab studies the complexity of mis-wired cancer cells, and the elegance of systems programs enacted by immune cells to accomplish their specialized anti-tumor functions. We aim to understand the governing principles that result in global changes during tumorigenesis and therapy resistance acquisition; with the end goal to identify new therapeutic vulnerabilities in the evolving cancers.
To this end, we are conducting multi-omics experimentation for systems biology analysis. This includes NGS sequencing approaches for transcriptomics, DNA mutation profiling, DNA copy number alteration (CNA) profiling, DNA methylation, chromatin accessibility (ATAC-seq), as well as in lab metabolomics and proteomics analyses of cancer cell lines and tumors using top-of-the-line mass spectrometry equipment.
This project will develop custom bioinformatic analysis pipelines to address clinic-linked cancer biology questions. The project includes creation of bioinformatic algorithms and pipelines for analyzing multi-omic data, and collaboration with biologists in the analysis and interpretation of data.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Thomas Graeber
tgraeber [at] mednet [dot] ucla [dot] edu
Possibility of Funding?
Can evolve to a paid position.