Institute for Quantitative and Computational Biosciences

QCBio/Bioinformatics/Human Genetics seminar: Molly Przeworski, PhD, Professor, Biological Sciences, Systems Biology, Columbia University
2023-02-27 16:00 - 17:00 (America/Los_Angeles) | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Why do human germline mutation rates depend on sex and age.”

QCBio/Bioinformatics/Human Genetics seminar: Jonathan Sebat, PhD, Chief, Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, Professor of Psychiatry and Cellular and Molecular Medicine, UC San Diego
2023-02-13 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “TBA”

QCBio/Bioinformatics/Human Genetics seminar: Haiyuan Yu, PhD, Professor, Biological Statistics and Computational Biology, Cornell University
2023-02-06 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “New perspectives to dissect global dynamics of protein interactome and gene regulation in disease.”

QCBio/Bioinformatics/Human Genetics seminar: Soojin Yi, PhD, Professor, Ecology, Evolution, and Marine Biology, UC Santa Barbara
2023-01-30 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “DNA Methylation in Brain Evolution and Neuropsychiatric Disorders.”

QCBio/Bioinformatics/Human Genetics seminar: Pleuni Pennings, PhD, Associate Professor, Biology, San Francisco State University
2023-01-23 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Part 1: “How important is evolution vs transmission of drug resistance”.
Part 2: “Computing skills for every biology student.”

QCBio Research Seminar: Friedrich Simmel, Department of Bioscience, School of Natural Sciences, TU Munich, Germany
2023-01-13 14:00 - 15:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Electric actuation of DNA-based molecular machines.”
ABSTRACT: A wide range of machine-like molecular assemblies have been generated over the past years. Most of them have been driven (or controlled) by DNA hybridization, utilization of buffer changes, or using chemical modifications such as photoswitches. A more recently explored strategy is the use of electrical fields for the manipulation of DNA devices, which enables fast and computer-controlled actuation. In the talk, several examples of such DNA origami-based „nanorobotic“ systems will be discussed, including a DNA „robot arm“, molecular springs, an origami based Brownian ratchet with rotary movement, and others. On a slightly different topic, also a novel strategy to control microswimmers using AC electrical fields will be presented.

QCBio/Bioinformatics/Human Genetics seminar: Shamil Sunyaev, PhD, Professor, Biomedical Informatics, Harvard Medical School
2023-01-09 16:00 - 17:00 | Boyer 159
TITLE: “Statistics, Biology and Applications of Human Mutation.”

QCBio Research Seminar: Zhiqian Zhai (Li JJ), Graduate Student in Statistics
2022-12-15 13:30 - 14:00 | Boyer Hall 159
TITLE: “Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.”
ABSTRACT: Recently, various computational methods have been developed to analyze the scRNAseq data, such as clustering and visualization. However, current visualization methods, including t-SNE and UMAP, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular, UMAP and t-SNE are not optimal to preserve the global geometric structure. They may result in a contradiction that clusters with near distance in the embedded dimensions are in fact further away in the original dimensions. Besides, UMAP and t-SNE cannot track the variance of clusters. Through the embedding of t-SNE and UMAP, the variance of a cluster is not only associated with the true variance but also is proportional to the sample size. Here, we present supCPM, a robust supervised visualization method, which separates different clusters, preserves the global structure and tracks the cluster variance.

https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/11/Zhiqian-Zhai-edited.mp4

QCBio Research Seminar: Roni Haas (Boutros), Postdoc in Human Genetics
2022-12-15 13:00 - 13:30 | Boyer Hall 159
TITLE: “Proteogenomic characterization of the molecular determinants of prostate cancer radioresistance.”
ABSTRACT: Prostate Cancer (PC), the second most common cause of cancer death in men, is frequently treated using radiotherapy with curative intent. Despite its effectiveness, radiotherapy often results in aggressive PC relapse characterized by radioresistance. The diversity in therapeutic response to radiotherapy, and the molecular processes underlining radioresistance are not fully understood. Here we integrated genomic, transcriptomic, and proteomic investigations to comprehensively characterize the molecular determinants of PC radioresistance. To model radioresistance, we have created Conventional-Fractionation (CF) and Hypo-Fractionated (HF) isogenic radioresistant cells that represent different therapeutic regimens: the traditional CF radiotherapy with daily small radiation doses over weeks, and the relatively new HF radiotherapy with high radiation doses across several single treatments. We discovered that CF radioresistant cells gained twice the number of somatic single-nucleotide variants than HF. Nevertheless, the gained mutations, irrespective of the treatment schedule, converged on mutational signatures associated with mismatch DNA repair. CF radioresistant cells demonstrated strong and exclusive dysregulation of driver and hallmark-cancer genes in RNA abundance profiles. The differences in protein abundance display cell-fraction-dependent clusters, foremost of which are elevated levels of DNA repair proteins in CF radioresistant cell nuclei. Collectively, we observed a far more aggressive phenotype in CF radioresistant cells compared to HF. The clinical relevance of our top potential therapeutic targets was assessed using a cohort of 380 PC patients. Our study provides a platform for the development of therapies for radio-recurrent PC. Ongoing proteogenomic integration will help understand the relationships amongst radioresistance-associated changes at the DNA, RNA, and protein levels.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/11/Roni-Haas.mp4

QCBio Research Seminar: Guanao Yan (Li JJ), Graduate Student in Statistics
2022-12-07 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “scReadSim: a single-cell RNA-seq and ATAC-seq read simulator.”
ABSTRACT: Rapid advances of single-cell RNA-seq and ATAC-seq technologies have propelled the development of many computational tools, benchmarking of which demands realistic simulators. However, few simulators can generate sequencing reads, and none of the existing read simulators aim to mimic real cells, hindering the benchmarking of low-level computational tools that process reads. To fill this gap, we propose scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that generates synthetic cells which mimic real cells. Trained on real data, scReadSim can generate synthetic data in FASTQ and BAM formats. By deploying scReadSim on sci-ATAC-seq and 10x Multiome (ATAC+RNA) data, we show that the scReadSim synthetic data resemble real data at both read and count levels. Moreover, as a flexible simulator, scReadSim enables users to arbitrarily specify open chromatin regions for the synthetic scATAC-seq reads, and is also capable of allowing varying the cell number and sequencing depths for the synthetic data. To illustrate the versatile usage of scReadSim, we include two exemplar benchmark studies to show that scReadSim provides unique molecular identifier (UMI) counts for benchmarking scRNA-seq deduplication tools and can accommodate user-specified open chromatin regions (“ground truths”) to generate single-cell ATAC-seq data. Our benchmark applications of scReadSim show that cellranger is a preferred scRNA-seq deduplication tool, and MACS3 achieves top performance in scATAC-seq peak calling.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/11/video1156562562.mp4

QCBio Research Seminar: Mark Xiang (Hoffmann), Graduate Student in Bioinformatics
2022-12-07 11:00 - 11:30 | Boyer Hall 159
TITLE: “Heterogeneity in cell states: Is it important whether cell states are heritable or change rapidly?”
ABSTRACT: Cells of the same cell type show molecular and phenotypic heterogeneity. Is the particular cell state of a given cell heritable from one generation to the next? Previous work in liver cancer cells indicates that the molecular network that controls cell death may change within hours, but the data from B-cells shows that their cell state is durable and heritable. In this project, I address this question by formulating a model of B-cell-mediated antibody responses. This model recapitulates the Darwinian selection process to produce high affinity antibodies against an antigen. I then test how heterogeneity in the cell states affects the antibody repertoire, and then what the effect of heritability of those cell states might be. The preliminary results from model simulation will be presented and discussed.

QCBio Research Seminar: Jee Yun Han (Boutros), Graduate Student in Gene Regulation, Epigenomics, and Transcriptomics
2022-12-02 14:30 - 15:00 | ZOOM, CA, United States
TITLE: “Comprehensive study of gene expression outliers and their regulation mechanisms in pan-cancer.”
ABSTRACT: Cancer is a disease characterized by remarkable heterogeneity. Gene expression varies drastically between tumours and within cells of a single. This variability can generate extreme outliers: transcripts that show atypically high gene expression in a small percentage of cancers. These outliers increase the molecular and phenotypic diversity between individuals, contributing to tumour heterogeneity. However, the previous research has been limited to a single cancer type or a single gene. To fill the gap of our understanding, this study will identify the outliers from various cancer types using our novel statistical method and explore the biological functions of outliers. With integrated analysis with the clinical outcome, it will show how outliers affect the progression of cancer. This study is expected to deepen our understanding of the impact of outliers on different cancers by dissecting their dysregulation and will allow us to identify a novel cancer driver and potential drug target.

QCBio Research Seminar: Matthew Soldano, Staff Research Associate for the Pellegrini Bioinformatic's Lab
2022-12-02 14:00 - 14:30 | ZOOM, CA, United States
TITLE: “Predicting Biological Aging from Epigenetics.”
ABSTRACT: Epigenetics are a proven measure of cellular health. Therefore, a field of research has emerged that utilizes epigenetics to measure, treat, and potentially reverse biological age in humans. Specifically, DNA methylation, responsible for cell differentiation and gene expression, has the potential to be a barcode for measuring biological age. Current DNA methylation aging tests require blood and are trained to predict chronological aging. To address this, we are developing a buccal swab DNA methylation test that measures fitness and general health. We utilize targeted bisulfite sequencing and are testing several machine learning techniques to generate a reliable test.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/11/video1371794408.mp4

W4: Galaxy for NGS Data Analysis
2022-10-26 13:30 - 2022-10-27 16:30 | 529 Boyer Hall, 611 Charles E Young Dr E,, Los Angeles, CA, 90095, United States
Galaxy (https://usegalaxy.org/) is a web-based infrastructure for bioinformatics application. First, this workshop introduces participants to using Galaxy for analysis of Next-Generation Sequencing data. Next, this workshop covers the structure of Galaxy, data format and manipulation, obtaining and sharing data, and building and sharing workflows. We will use the tools installed on the UCLA galaxy to perform a few types of NGS analysis.

W22: Intro to Cytoscape
2022-10-25 09:00 - 2022-10-27 12:00 | 529 Boyer Hall, 611 Charles E Young Dr E,, Los Angeles, CA, 90095, United States
The workshop introduces Cytoscape (https://cytoscape.org/) – an open source platform for retrieval and visualization of complex biological interactions that are available from public interaction resources: The IMEx Consortium (https://www.imexconsortium.org/), STRING (https://string-db.org/) and BioGIRD (https://thebiogrid.org/). It covers querying for interactions data using built-in search interface, interaction data cleanup and visualization as well as overlying of the in-house generated data (eg. gene expression levels) on top of the imported reference interaction network. The workshop will also provide an overview of the experimental methods of interaction determination and of the information available from the interaction resources, both necessary to critically evaluate imported interaction networks.

QCBio/BIG Summer Research Seminar: Dr. Michael Wells, Assistant Professor, Human Genetics, UCLA
2022-08-05 16:00 - 17:00 | Boyer 159
TITLE: “Exploration of human genetic and phenotypic diversity through cell villages”.
ABSTRACT: Our species is characterized by an immense diversity in neurological and psychological traits. Common and rare genetic variants have been linked to trait differences and disease risk in human populations, though the underlying biology is poorly understood and difficult to study at large scales. In this presentation, I will first describe a novel experimental platform that enables high-throughput investigations into the influence of human genetic variation on the earliest stages of brain development. This system, known as a “cell village” captures genetic, molecular, and phenotypic heterogeneity in a shared in vitro environment, thus facilitating the detection of relationships among human alleles, gene expression, and cellular behaviors. I will then describe how we used cell villages to identify a single nucleotide polymorphism in the IFITM3 gene that could explain over half of the variance in neural progenitor cell susceptibility to the Zika virus, which is a pathogen that causes severe neurodevelopmental disorders. I will conclude with a brief discussion on the future of the village approach and how we plan to deploy this technology to investigate neurodevelopmental disorders of genetic origin in my laboratory at UCLA. Throughout this talk, I will talk about the career decisions that led me to a career in neuroscience.

QCBio/BIG Summer Research Seminar: Dr. Xia Yang, Professor, Dept. Integrative Biology & Physiology, UCLA
2022-07-29 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Integrative systems analysis, applications, and challenges of single cell multiomics.”
ABSTRACT: Recent advances in single cell multiomics technologies such as single cell RNA-seq, single cell ATAC-seq, and spatial transcriptomics have brought enormous opportunities that enable our understanding of the molecular underpinnings of pathophysiology at a single cell resolution. However, integrative analysis across single cell multiomics domains and further incorporation of genetics information have proven challenging. I will introduce our recent efforts in single cell multiomics integration, network modeling, and eQTL analysis, present computational tools to carry out these analyses, and showcase application examples in studies of traumatic brain injury, Alzheimer’s disease, and genetic control of cell-type specific hypothalamic cell types to regulate energy homeostasis. I will also highlight the challenges and future directions of integrative systems analysis of single cell multiomics data.

QCBio/BIG Summer Research Seminar: Dr. Eleazar Eskin, Professor and Chair Department of Computational Medicine, UCLA
2022-07-22 16:00 - 17:00 | 529 Boyer Hall, 611 Charles E Young Dr E,, Los Angeles, CA, 90095, United States
TITLE: “Swab-Seq: Detecting COVID-19 with Genomic Sequencing: From bench to vending machine.”
ABSTRACT: At UCLA we developed one of the only novel technologies for COVID-19 diagnostic testing that was deployed on a large scale. The assay, which we named SwabSeq, performs genomic sequencing of pooled samples tagged with sample-specific molecular barcodes and then uses computational approaches to deconvolve the pooled samples into individual diagnoses, enabling the testing of thousands of nasal or saliva samples for SARS-CoV-2 RNA in a single run without the need for RNA extraction. The efficiency of SwabSeq has enabled a small facility with a handful of staff to perform over 1,000,000 tests, with an analytical sensitivity and specificity comparable to or better than traditional qPCR test with turnaround times of less than 24 h. SwabSeq could be rapidly adapted for the detection of other pathogens.

QCBio/BIG Summer Research Seminar: Dr. Eric Deeds, Associate Professor - Vice Chair, Life Sciences Core, UCLA
2022-07-15 16:00 - 17:00 | Boyer Hall 159
TITLE: “A lack of distinct cellular identities in scRNA-seq data: revisiting Waddington’s landscape”.
ABSTRACT: Single-cell RNA sequencing is revolutionizing our understanding of development, differentiation and disease. Analysis of this data is often challenging, however, and tasks like clustering cells to uncover distinct cellular identities sometimes yields results that fail to align with existing biological knowledge. We analyzed publicly available data where the cell identity for each cell is known a priori, and found that cells of very different types and lineages do not occupy distinct regions of gene expression space. Rather, cells from different lineages overlap extensively with one another, significantly complicating attempts to recover distinct identities within the data. Indeed, our analysis of available epigenetic data for a wide variety of tissues and organisms revealed these data are not consistent with the predictions of Waddington’s landscape, suggesting a need to revisit our picture of gene expression changes during differentiation and development.

QCBio/BIG Summer Research Seminar: Dr. Nandita Garud, Assistant Professor Assistant Professor, Department of Ecology and Evolutionary Biology, UCLA
2022-07-08 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Evolutionary dynamics in the human gut microbiome from infancy through adulthood.”
ABSTRACT: While the ecological dynamics of the infant gut microbiome have been intensely studied, relatively little is known about the evolutionary dynamics in the infant gut microbiome. Here we analyze longitudinal fecal metagenomic data from >700 infants and their mothers over the first year of life and find that the evolutionary dynamics in infant gut microbiomes are distinct from that of adults. We find evidence for almost 100-fold increase in the rate of evolution and strain turnover in the infant gut compared to healthy adults, with the mother-infant transition at delivery being a particularly dynamic period in which gene loss dominates. Within a few months after birth, these dynamics stabilize, and gene gains become increasingly frequent as the microbiome matures. We furthermore find that evolutionary changes in infants show signatures of being seeded by a mixture of de novo mutations and transmissions of pre-evolved lineages from the broader family. Several of these evolutionary changes occur in parallel in multiple infants, highlighting candidate genes that may play important roles in the development of the infant gut microbiome. Our results point to a picture of a volatile infant gut microbiome characterized by rapid evolutionary and ecological change in the early days of life.

QCBio/BIG Summer Research Seminar: Dr. Harold Pimentel, Assistant Professor in Computational Medicine & Human Genetics, UCLA
2022-07-01 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Model driven design and analysis of functional screens”
ABSTRACT: A little over a decade ago biomedicine was revolutionized by a conceptually simple insight: DNA sequencers could be used as molecular counting machines to measure a multitude of molecules beyond DNA. Through the years, sequencing has continued to become cheaper and more efficient (faster than Moore’s Law) and thousands of clever assays have been developed. This development has led to thousands of experiments done regularly with new data modalities arising continuously. However, this development has also led to a number of interesting computational problems: how does one design an experiment such that reasonable power is achieved? What can actually be measured? And finally, how does one even perform an analysis on such data once it has arrived?

QCBio Research Seminar: Ha Vu (Ernst), Graduate Student in Bioinformatics
2022-05-25 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “Universal annotation of the human genome through integration of over a thousand epigenomic datasets.”
ABSTRACT: Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative ‘stacked modeling’ approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.
Using a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type specific activity and is more predictive of locations of external genomic annotations.
The full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Ha-Vu-edited.mp4

QCBio Research Seminar: Connor Razma (Hoffmann), BS/MS Student
2022-05-25 11:30 - 12:00 | ZOOM, CA, United States
TITLE: “Baseline MEthylation Patterns prior to flu vaccination.”
ABSTRACT: Influenza affects millions worldwide each year with responses varying from individual to individual. Influenza can be broken down into subtypes specifically H1N1, H3N2, Yamagata, and Victoria. One way to measure the immune response to influenza is to measure a person’s antibody response to influenza. To measure how many antibodies are present in a sample, a hemagglutination inhibition assay (HAI) is used. DNA methylation is an epigenetic mechanism used to regulate gene expression in cells. Its mechanism of action is the addition of a methyl group to cytosine at a cytosine-guanine pair. DNA methylation has been shown to change in response to stimuli such as viral or bacterial infections. DNA methylation can be measured by bisulfite sequencing, specifically reduced representation bisulfite sequencing in our case. In this study, data was taken from patients who had the flu vaccination. Their antibody data was measured using the HAI assay by the University of Georgia and their methylation data was measured using reduced representation bisulfite sequencing by the Pellegrini and Reed lab at UCLA. Using various statistical learning algorithms we were able to find methylated sites that were good predictors of vaccine response. Elastic net regression proved to be a particularly good predictor of vaccine response, and after further analysis, it was revealed that the best prediction happened with only a few significant sites. Some of these significant sites seem to be involved in regulating immune response and membrane function. Further work will be done to determine the prediction accuracy of these algorithms with just these sites. Ideally, after this future work and other experiments, these methylated sites can be used as biomarkers to indicate response to flu vaccination.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Connor-Razma-edited.mp4

QCBio Research Seminar: Jackson Chin (Meyer), Graduate Student in Bioengineering
2022-05-18 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “Tensor Factorization for Interpreting the Mechanisms of MRSA Persistence.”
ABSTRACT:
Methicillin-resistant Staphylococcus aureus (MRSA) bacteria is an increasingly common and life-threatening infection. While some antibiotics resolve MRSA infections in vitro, these same antibiotics often fail to clear an infection when used to treat patients, suggesting that MRSA persistence is a confluence of both host and bacterial factors. While recent research has identified critical genetic and proteomic determinants of MRSA persistence, the mechanisms that drive MRSA persistence are still poorly understood. Here, to better understand these mechanisms, we implement tensor-based factorization to integrate genetic and proteomic data collected from two cohorts of patients with MRSA infections. We find that our factorization process identifies patterns across biological modalities and is able to explain 75% of the variance observed in genetic and proteomic data with just 8 components. Additionally, this data integration improves persistence prediction accuracy as prediction models trained on these integrated factors demonstrate accuracies as high as 80% over subsets of the cohorts. Interpretation of these components further reveals mechanisms that drive the infection response and highlight processes critical for MRSA persistence. Overall, these results suggest that tensor-based factorization can identify the mechanisms underlying MRSA persistence across host factors and improve our ability to predict and understand MRSA persistence.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Jackson-Chin-edited.mp4

QCBio Research Seminar: Yue Wang (Chou), Postdoc, Department of Computational Medicine
2022-05-18 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “Stochastic Model and Optimization of SELEX.”
ABSTRACT: Systematic Evolution of Ligands by EXponential enrichment (SELEX) is a process to select the best aptamer sequence in a huge aptamer library that binds a specified target molecule with the highest affinity. There has been a deterministic model of SELEX, and we develop a fully discrete stochastic model to obtain more accurate results when the mass action law does not hold. Specifically, we find that the optimal SELEX protocol in the stochastic model differs from that predicted by the deterministic model.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Yue-Wang-edited.mp4

QCBio Research Seminar: Heather Zhou (Li JJ), Graduate Student in Statistics
2022-05-11 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “PCA outperforms popular hidden variable inference methods for QTL mapping.”
ABSTRACT: Estimating and accounting for hidden variables is widely practiced as an important step in quantitative trait locus (QTL) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use. The preprint is available at https://www.biorxiv.org/content/10.1101/2022.03.09.483661v1.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Heather-Zhou-edited.mp4

QCBio Research Seminar: Keunseok Park (Park), Grad Student, Chemical and Bimolecular Engineering
2022-05-11 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “G-Flux: a metabolic flux and free energy analysis software for interpreting 13C, 2H, 18O, and 15N isotope tracing data.”
ABSTRACT: Metabolic fluxes offer insights into pathway utilization, kinetics, and thermodynamics. Stable isotope tracing and metabolic footprinting are widely used for inferring metabolic fluxes. However, quantitative flux measurement across broad metabolism remains challenging due to our inability to convert multiple isotope distributions into fluxes and integrate exometabolomics data. Here we develop a software application, G-Flux, for computing metabolic fluxes by tracing 13C, 2H, 18O, and 15N. Using the ratio of forward to backward fluxes, G-Flux can compute reaction Gibbs free energies (ΔG). We use G-Flux to compute fluxes and ΔG in E. coli and mammalian cells. Using these results and G-Flux’s ability to simulate isotope labeling, we find that [6-18O1]glucose is ideal for quantifying ΔG of glycolytic reactions and dual tracing of [U-13C6]glucose and [U-15N2]glutamine for quantifying fluxes and ΔG of transaminase and amino acid degradation. Thus, G-Flux facilitates comprehensive metabolic flux and free energy analysis.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2022/05/Keunseok-Park-edited.mp4

W28: Introduction to Phylogenetics
2022-04-12 00:00 - 2022-04-14 00:00 | 529 Boyer Hall, 611 Charles E Young Dr E,, Los Angeles, CA, 90095, United States
Phylogenetics – the reconstruction of the evolutionary history of a the variety of species known to us, is among the most fundamental and traditional tasks in Biology. With the dramatic developments in molecuar biology and the exponential growth in annotated genetic sequences, novel directions and goals have been developed in this seemingly stagnant field. Phylogenetic analysis is now an integral part not only in traditional areas like Evolution, Zoology, or Ecology, but also in imunology, medicine, and other disciplines.
In this workshop, I will review the scientific basis behind the various approaches in phylogenesis, in particular statistical/quantitative approaches that became standard in any modern molecular biology research. A chapter dedicated to the COVID-19 pandemic will be included.

11th Annual Southern California Systems Biology Symposium
2022-04-02 10:00 - 19:00 | Geffen Hall, 885 Tiverton Drive, Los Angeles, CA, 90095, United States
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Bioinformatics/Human Genetics Seminar Series: King Jordan, PhD, Professor and Director, Bioinformatics Graduate Program
2022-03-07 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Genomics for precision public health in Colombia”
Hosted by Juan De La Hoz.
