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.

Machine Learning Empowered Cardiovascular Multi-Omics Research

Project Description
Developing technological platforms and computational tools for in-depth analysis of protein temporal dynamics, protein post-translational modifications, and metabolite. The computational platform will address issues like missing data, sparsity, curse of dimensionality, heterogeneity, etc., facilitating temporal clustering, molecular signature extraction and integrative analysis.
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Peipei Ping
admin [at] heartbd2k [dot] org
Possibility of Funding?
Yes

Computational Analysis in O-PTM Biology

Project Description
Oxidative post-translational modifications (O-PTMs) of proteins are highly prevalent cellular features that elicit critical effects on human health and disease. We aim to develop bioinformatic tools to analyze redox proteomic data and explore the patterns of oxidized amino acid residues during cardiac remodeling..
Requirements
One course in programming such as PIC 10A or CS 31
Contact
Peipei Ping
admin [at] heartbd2k [dot] org
Possibility of Funding?
Yes

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
 

Upcoming Events

  1. Fall 2019 Quarter Ends

    December 13
  2. UCLA Winter Campus Closure

    December 23, 2019 - January 1, 2020

Recent Student Publications

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures.
Graham, Minasyan, Lomova, Cass, Balanis, Friedman, Chan, Zhao, Delgado, Go, Beck, Hurtz, Ng, Qiao, Hoeve, Palaskas, Wu, Muschen, Multani, Port, Larson, Schultz, Braaz, Christofk, Mellinghoff, Graeber
Molecular Systems Biology. Mol Syst Biol. 2017; 13(2):914

Reelin Deficiency Delays Mammary Tumor Growth and Metastic Progression
Khialeeva, Chou, Allen, Chiu, Bensinger, Carpenter
Journal of Mammary Gland Biology and Neoplasia. 2017; 22(1):56-69

© 2015 UCLA Bioinformatics. All Rights Reserved.

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.