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<!DOCTYPE html>
<html>
<head>
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<ul class="navbar-nav text-uppercase ml-auto"><li class="nav-item"><a class="nav-link js-scroll-trigger" href="#services">Research</a></li>
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<div class="intro-text"><div class="intro-lead-in"><p>Genomics, computation and sequencing</p>
</div><div class="intro-heading text-uppercase"><p>Mortazavi Lab at UC Irvine</p>
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<h2 class="section-heading text-uppercase">Research</h2>
<h3 class="section-subheading text-muted">There are many diverse projects going on in the lab</h3>
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<h4 class="service-heading"><p>Long-read Transcriptomics</p>
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<div class="text-muted"><p>We are leveraging the PacBio and Oxford Nanopore platforms to sequence full-length transcripts in order to characterize the true extent of alternative splicing. We are both sequencing transcriptomes as well as developing tools to enable these analyses.</p>
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<h4 class="service-heading"><p>IGVF</p>
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<div class="text-muted"><p>We are leveraging mouse genetic diversity to study the impact of genotype on molecular function in a variety of tissues using single-cell resolution.</p>
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<h4 class="service-heading"><p>Model AD</p>
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<div class="text-muted"><p>We are part of a NIA Consortium to build better late-onset Alzheimer’s Disease models in mouse. Our part includes both bioinformatics as well as single-cell transcriptomics in these new models.</p>
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<h4 class="service-heading"><p>FSHD</p>
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<div class="text-muted"><p>We are using functional genomics to understand the mechanisms driving the pathology of Facioscapulohumeral muscular dystrophy using single-cell and single-nucleus techniques.</p>
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<h4 class="service-heading"><p>Gene Regulatory Networks</p>
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<div class="text-muted"><p>We are building models of gene regulatory networks using either bulk or single-cell RNA-seq, and ATAC-seq in human, rodents, and other vertebrates in order to discover how the logic of development is encoded in the genome.</p>
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<h4 class="service-heading"><p>Cellular Differentiation</p>
</h4>
<div class="text-muted"><p>We study myogenesis and macrophage differentiation using functional genomics assays such RNA-seq, microRNA-seq, ATAC-seq, and ChIP-seq in order to understand how gene expression and chromatin states change in time courses of differentiation.</p>
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<h2 class="section-heading text-uppercase"><p>Software</p>
</h2>
<h3 class="section-subheading text-muted"><p>Software produced by the lab used to analyze genomics data</p>
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<h4>Topyifc</h4>
<p class="text-muted">Reproducible latent dirichlet allocation (LDA) for single cell epigenomics data</p>
</div>
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<h4>PyWGCNA</h4>
<p class="text-muted">Weighted Gene Correlation Network Analysis</p>
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<h4>Lapa</h4>
<p class="text-muted">Alternative polyadenylation detection</p>
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<h4>Swan</h4>
<p class="text-muted">Transcriptome analysis and visualization</p>
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<h4>TALON</h4>
<p class="text-muted">Long-read RNA-seq annotator</p>
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<h4>TranscriptClean</h4>
<p class="text-muted">Long-read RNA-seq error correction</p>
</div>
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<h4>SOMatic</h4>
<p class="text-muted">High-dimensional data clustering with SOMs</p>
</div>
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</div>
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<!-- Project Details Go Here -->
<h2 class="text-uppercase">Topyfic</h2>
<p class="item-intro text-muted">Reproducible latent dirichlet allocation (LDA) using leiden clustering and harmony for single cell epigenomics data</p>
<img class="img-fluid d-block mx-auto" src="assets/img/portfolio/Topyfic.png" alt="Topyfic_workflow">
<p><p>An open challenge for the analysis of single-cell data is the identification of distinct cellular programs that may be simultaneously expressed in the same cell based on the interaction of genotypes in environments. Latent Dirichlet allocation (LDA) is a popular statistical method for the identification of recurring patterns in count data (e.g. gene expression), which are referred to as topics. These topics are composed of genes with specific weights that can together explain underlying patterns of gene expression profile for each individual cell. In particular, each cell’s expression profile can be decomposed into a combination of the topics that can be analyzed both globally using topic-trait enrichment as well as in individual cells using structure plots. Due to the random initialization of LDA algorithms, topic definitions can vary substantially each time that the algorithm is rerun, which hinders their interpretability. Therefore, we developed reproducible LDA where we define our topics by analysing their reproducibility across a large number of runs.</p>
<p><p>Topyfic is a Python library designed to apply rLDA to single_cells/bulk RNA-seq data to recover meaningful topics that involve the key genes like transcription factors involved in different steps.</p>
<p>Author: Narges Rezaie</p>
<ul class="list-inline">
<!-- <li><a href="">Topyfic manuscript</a></li> -->
<li><a href="https://mortazavilab.github.io/Topyfic/html/index.html">Topyfic website</a></li>
<li><a href="https://github.com/mortazavilab/Topyfic">Topyfic GitHub</a></li>
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<h2 class="text-uppercase">PyWGCNA</h2>
<p class="item-intro text-muted">Python library designed to do weighted gene correlation network analysis (WGCNA)</p>
<img class="img-fluid d-block mx-auto" src="assets/img/portfolio/PyWGCNA.png" alt="PyWGCNA_workflow">
<p><p>PyWGCNA is a Python library designed to do weighted correlation network analysis (WGCNA). It can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Users can also compare WGCNA networks from different datasets, or to external gene lists, to assess the conservation or functional enrichment of each module.</p>
<p>Author: Narges Rezaie</p>
<ul class="list-inline">
<li><a href="https://www.biorxiv.org/content/10.1101/2022.08.22.504852v1.abstract">PyWGCNA manuscript</a></li>
<li><a href="https://mortazavilab.github.io/PyWGCNA/html/index.html">PyWGCNA website</a></li>
<li><a href="https://github.com/mortazavilab/PyWGCNA">PyWGCNA GitHub</a></li>
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<h2 class="text-uppercase">LAPA</h2>
<p class="item-intro text-muted">Alternative polyadenylation detection from diverse data sources such as 3'-seq, long-read and short-reads.</p>
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<p><p>Alternative polyadenylation (APA) is a major mechanism that increases transcriptional diversity and regulates mRNA abundance. Existing computational tools to analyze APA have low precision because these tools are designed for short-read RNA-seq, which is a suboptimal data source to study APA. Long-read RNA-seq (LR-RNA-seq) accurately detects complete transcript isoforms with poly(A)-tails, providing an ideal data source to study APA. However, current computational tools are incompatible with LR-RNA-seq.</p>
<p>Author: Muhammed Hasan Celik</p>
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<li><a href="https://www.biorxiv.org/content/10.1101/2022.11.08.515683v1.abstract">LAPA manuscript</a></li>
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<p><p>Swan is a Python library for analyzing and visualizing transcriptomes, with long-read transcriptomes in mind. Swan can find differentially-expressed genes and transcripts, isoform-switching genes, and novel exon skipping and intron retention events.</p>
<p>Author: Fairlie Reese</p>
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<p><p>TALON (<strong>T</strong>echnology-<strong>A</strong>gnostic <strong>Lon</strong>g-read) annotation software is designed to associate transcript identities to reads from long-read RNA-seq datasets. It works equally well for both PacBio and Oxford Nanopore data. It has currently be implemented by <a href="http://encodeproject.org/">ENCODE</a> as a part of their uniform data-processing pipeline.</p>
<p>Author: Dana Wyman, PhD</p>
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<p><p>TranscriptClean is a tool used to correct mismatches, microindels, and noncanonical splice junctions in long reads in a variant-aware manner. It can be used to recover otherwise unusable reads in long read datasets.</p>
<p>Author: Dana Wyman, PhD</p>
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<p><p>SOMatic can be used to cluster high-dimensional data from multiple data modalities into associated modules. Visualization tools enable the user to understand the data in different contexts.</p>
<p>Author: Camden Jansen, PhD</p>
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<p><p><strong>Research Interests:</strong> <br />
I am interested in NGS-focused biotechnology and currently work on integrating bulk & single-cell/single-nucleus RNA-seq and ATAC-seq in an in vitro model of skeletal muscle differentiation.</p><br />
<br />
<p><strong>Links:</strong> <br />
<a href="http://orcid.org/0000-0003-2273-0189"><strong>ORCID</strong></a> <br />
<a href="https://www.linkedin.com/in/elisabeth-rebboah-a33923104/"><strong>LinkedIn</strong></a> <br />
<a href="https://github.com/erebboah"><strong>GitHub</strong></a></p><br />
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I enjoy watching anime and getting takeout when I’m not working.</p><br />
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erebboah {at} uci.edu</p><br />
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<p><p><strong>Research Interests:</strong> <br />
I currently work on long-read transcriptomes, and am specifically focused on developing tools for their analysis.</p><br />
<br />
<p><strong>Software:</strong> <br />
<a href="https://freese.gitbook.io/swan/"><strong>Swan:</strong></a> a Python library for visualizing and analyzing transcriptomes <br />
<a href="https://github.com/mortazavilab/TALON"><strong>TALON:</strong></a> Technology-agnostic long-read annotation and quantification pipeline</p><br />
<br />
<p><strong>Links:</strong> <br />
<a href="https://fairliereese.github.io/"><strong>Personal Website</strong></a> <br />
<a href="https://scholar.google.com/citations?user=SgA5IcgAAAAJ&hl=en"><strong>Google Scholar</strong></a> <br />
<a href="http://orcid.org/0000-0002-9240-0102"><strong>ORCID</strong></a> <br />
<a href="https://www.linkedin.com/in/fairlie-reese-a930a5b7/"><strong>LinkedIn</strong></a> <br />
<a href="https://github.com/fairliereese"><strong>GitHub</strong></a></p><br />
<br />
<p><strong>Personal Interests:</strong> <br />
When I’m not working (and there’s not a pandemic going on) you can find me at the beach, at rock concerts, or eating Mexican food. At home, I like to play video games, Dungeons and Dragons, and hang out with my cats.</p><br />
<br />
<p><strong>Contact:</strong> <br />
freese {at} uci.edu</p><br />
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<p><p><strong>Research Interests:</strong> <br />
I am currently working on different metabolic labeling strategies and long-read sequencing to study the mRNA transcription.</p><br />
<br />
<p><strong>Links:</strong> <br />
<a href="https://orcid.org/0000-0002-4470-3192"><strong>ORCID</strong></a> <br />
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jsakr {at} uci.edu</p><br />
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<p><p><strong>Research Interests:</strong> <br />
As a person with a computer science background, I’ve always been interested in machine learning methods, network analysis and their applications. Currently, I find the brain as an interesting organ, so I’m trying to identify and classify every functional part of the genome in the brain.</p><br />
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<p><strong>Links:</strong> <br />
<a href="https://nargesr.github.io/"><strong>Personal Website</strong></a> <br />
<a href="https://scholar.google.com/citations?user=xS23yQUAAAAJ&hl=en"><strong>Google Scholar</strong></a> <br />
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<a href="https://www.linkedin.com/in/narges-rezaie-ab9710b8/"><strong>LinkedIn</strong></a> <br />
<a href="https://github.com/nargesr"><strong>GitHub</strong></a></p><br />
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I consider myself as an outgoing person, I like camping and adventure. Most weekends, I go hiking with my friends. At home, I like cooking, playing board games and playing guitar.</p><br />
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<p><strong>Contact:</strong> <br />
nargesr {at} uci.edu</p><br />
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<img class="img-fluid d-block mx-auto" src="assets/img/team/liz.jpg" width="300" height="300">
<p><p><strong>Research Interests:</strong> <br />
I am interested in NGS-focused biotechnology and currently work on integrating bulk & single-cell/single-nucleus RNA-seq and ATAC-seq in an in vitro model of skeletal muscle differentiation.</p><br />
<br />
<p><strong>Links:</strong> <br />
<a href="http://orcid.org/0000-0003-2273-0189"><strong>ORCID</strong></a> <br />
<a href="https://www.linkedin.com/in/elisabeth-rebboah-a33923104/"><strong>LinkedIn</strong></a> <br />
<a href="https://github.com/erebboah"><strong>GitHub</strong></a></p><br />
<br />
<p><strong>Personal Interests:</strong> <br />
I enjoy watching anime and getting takeout when I’m not working.</p><br />
<br />
<p><strong>Contact:</strong> <br />
erebboah {at} uci.edu</p><br />
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<p class="item-intro text-muted">Postdoctoral fellow</p>
<img class="img-fluid d-block mx-auto" src="assets/img/team/fairlie.jpg" width="300" height="300">
<p><p><strong>Research Interests:</strong> <br />
I currently work on long-read transcriptomes, and am specifically focused on developing tools for their analysis.</p><br />
<br />
<p><strong>Software:</strong> <br />
<a href="https://freese.gitbook.io/swan/"><strong>Swan:</strong></a> a Python library for visualizing and analyzing transcriptomes <br />
<a href="https://github.com/mortazavilab/TALON"><strong>TALON:</strong></a> Technology-agnostic long-read annotation and quantification pipeline</p><br />
<br />
<p><strong>Links:</strong> <br />
<a href="https://fairliereese.github.io/"><strong>Personal Website</strong></a> <br />
<a href="https://scholar.google.com/citations?user=SgA5IcgAAAAJ&hl=en"><strong>Google Scholar</strong></a> <br />
<a href="http://orcid.org/0000-0002-9240-0102"><strong>ORCID</strong></a> <br />
<a href="https://www.linkedin.com/in/fairlie-reese-a930a5b7/"><strong>LinkedIn</strong></a> <br />
<a href="https://github.com/fairliereese"><strong>GitHub</strong></a></p><br />
<br />
<p><strong>Personal Interests:</strong> <br />
When I’m not working (and there’s not a pandemic going on) you can find me at the beach, at rock concerts, or eating Mexican food. At home, I like to play video games, Dungeons and Dragons, and hang out with my cats.</p><br />
<br />
<p><strong>Contact:</strong> <br />
freese {at} uci.edu</p><br />
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<!-- Person's Details Go Here -->
<h2 class="text-uppercase">Heidi Liang</h2>
<p class="item-intro text-muted">Lab Technician / Sequencing Specialist, </p>
<img class="img-fluid d-block mx-auto" src="assets/img/team/heidi.jpg" width="300" height="300">
<p><p>Description coming soon</p><br />
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