Norden-Krichmar Lab

University of California, Irvine

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Research Projects

The overall goal of the lab is to gain a better understanding of the mechanisms underlying the causes of human diseases through the computational analyses of genomic, clinical, and environmental data.

Our research projects involve a wide range of multidisciplinary collaborations, and encompass applied bioinformatics analyses, advanced computational methods, and custom bioinformatics tool development. These bioinformatics analyses have been applied to many human diseases, including addiction, alcohol-induced liver diseases, obesity, longevity, and cancer.

The main areas of research are as follows:

  1. Gene expression analysis and determination of gene regulation mechanisms
  2. Computational methods development, machine learning, and deep learning
  3. Genetic and epigenetic factors in human disease
  4. Metagenomics and metatranscriptomics

Gene expression analysis and determination of gene regulation mechanisms

    • Transcriptomics in Alcoholic Hepatitis (AH).  Dr. Norden-Krichmar was the PI of a NIH National Institute on Alcoholism and Alcohol Abuse (NIAAA) U01 grant entitled “Transcriptomics of Liver in Alcoholic Hepatitis” to study the gene expression profile in participants with alcoholic hepatitis for diagnosis, treatment monitoring, and drug discovery.  This grant was part of the Southern California Alcoholic Hepatitis Consortium (SCAHC), with the sample collection by collaborating clinical sites.  For this project, our lab has overseen the RNA sequencing and analysis for over 200 biospecimens. This project has resulted in the publication of one methods paper, 2 manuscripts currently under review, and in the acceptance of over 16 abstracts at national scientific conferences.
    • Transcriptomics in cardiomyopathy. This project involved RNA sequencing in families with cardiomyopathy, and was a multidisciplinary collaboration at University of California, Irvine, with Dr. Michael Zaragoza’s lab in the Department of Biological Chemistry, Dr. Anna Grosberg (Biomedical Engineering), and Halida Widyastuti (Ph.D. student in Biological Chemistry).
    • Single-cell RNA sequencing to investigate potential biomarkers in the progression to hepatocellular carcinoma.  The goal of this project is to use single-cell RNA sequencing to profile gene expression and find biomarkers that may indicate the progression to liver cancer. Dr. Norden-Krichmar has received several pilot awards to pursue this project, including the UC Irvine School of Medicine and Veterans Administration Long Beach Healthcare System Biumvirate Pilot Grant and the Cancer Health Disparities Pilot Project Award from the Chao Family Comprehensive Cancer Center. The analyses are ongoing.  The preliminary results have been presented as a poster by Ph.D. student Xiaochen Liu at ASHG 2020, 2 posters were presented for ASHG 2021, and 1 scRNAseq poster is accepted for ASHG 2022.

Computational methods development

    • Utilization of state-of-the-art approaches and the development of novel bioinformatics tools for the analysis of gene expression data. Norden-Krichmar worked with John Schomberg, a PhD student in the Department of Epidemiology at UCI, during his thesis project and publication involving the computational identification of a gene expression profile for oral cancer using Monte Carlo cross validation.  Additionally, Stanislav Listopad, a current Ph.D. student in the lab, designed and implemented the “A-Lister” project, which involves finding similarity and differences in gene expression across multiple sample groups.  This project resulted in a publication, and the A-Lister software is released as open source software on Stanislav’s Github site.
    • Predictive computational methods development for disease classification using genomic data. Current lab member Ph.D. student, Stanislav Listopad’s thesis project involves machine learning approaches to classify diseases using gene expression data.  Stanislav has had 3 posters accepted for presentation at the American Society of Human Genetics conferences.   The manuscript “Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples” was published in JHEP Reports journal.
    • Deep learning computational methods with genomic data. We were honored to be chosen as the host lab by a Computer Science graduate student, Tulika Kakati, from Tezpur University in India, who had been awarded a Fulbright-Nehru Fellowship.  She was a visiting scholar in the lab from Sept 2019 – June 2020. During her time in the lab, we collaborated on a deep learning research project (DEGnext) that was accepted for a poster presentation at ASHG 2020. Tulika Kakati also received an ASHG Developing Country Award for this abstract, of which only 25 awards were given.  A manuscript for this computational approach was accepted for publication, and the DEGnext code is released as open source software on Tulika’s Github site.
    • Meta-analysis and integration methods for genomic data. In June 2020, a Ph.D. graduate student from the Department of Biological Chemistry, Chloe Thangavelu, joined the lab.  Her research project uses bioinformatics meta-analyses to explore chromatin accessibility.  She has presented this research as a poster at ASHG 2020, and has an abstract accepted for ASHG 2021.

Genetic and epigenetic factors in human disease using DNA data.

  • Genetic factors in alcoholic hepatitis. Currently, Dr. Norden-Krichmar is a Co-I for an NIAAA U01 grant to study the genetic factors underlying alcohol-associated hepatitis using whole exome DNA sequencing.  The samples have been sequenced and data analysis is currently ongoing.
  • Methylation biomarkers in breast cancer. Dr. Norden-Krichmar was a Co-I on a project with Dr. Hannah L. Park (PI), with funding from the California Breast Cancer Research Program (CBCRP) to study methylation biomarkers from environmental exposures. For more information, refer to Dr. Park’s website for the Markers for Environmental Exposures (MEE) Study. This work has generated 3 abstracts that were presented as posters at national conferences, 2 publications, and another manuscript is also currently under review.
  • Genetic factors in metabolic syndrome. Within the Department of Epidemiology and Biostatistics at UCI, we collaborate with Dr. Karen Edwards’ group to provide bioinformatics analysis on the study of genetic factors in metabolic syndrome.  This collaboration has resulted in 6 abstracts that were accepted for presentation at the American Society of Human Genetics, and the International Genetic Epidemiology Society conference, and 3 publications.
  • Genetic factors in disease in admixed populations. A previous research topic involved obesity and alcohol addiction in Native American and in Mexican American populations. This project required the analysis of low coverage whole genome sequence data, integration with exome genotyping chip data, ancestry analysis, and statistical correlation.
  • Analysis of pooled DNA sequencing. For a genetic variant study of longevity, a pooled DNA sequencing method was utilized. Pooled DNA sequencing reduces sequencing costs, but has high computational complexity.

Metagenomics and metatranscriptomics. Novel bioinformatic techniques were required in several projects that used next generation sequencing in collaboration with the J. Craig Venter Institute, such as the de novo assembly, read mapping, functional annotation, and integration of metagenomics and metatranscriptomics data to determine differential gene expression in a microbial wastewater sample.