Institute for Quantitative and Computational Biosciences

QCBio Research Seminar: Paheli Desai-Chowdhry (Savage), Grad student in Biomathematics
2021-11-17 13:00 - 13:30 (America/Los_Angeles) | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Asymmetric Branching Scale Factors as Features in Neuronal Cell-Type Classification”
ABSTRACT: Neurons are connected by complex branching processes – axons and dendrites – that process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental piece of neuroscience. In previous work, we constructed a biophysical theory that establishes a correspondence between neuron structure and function as mediated by principles such as time or power minimization, using undetermined Lagrange multipliers to predict scaling ratios for axon and dendrite sizes across branching levels. Here, we relax the assumption of symmetrical branching in the model to determine asymmetric branching powers that differ across different cell types due to functional tradeoffs. Furthermore, we use scale factors related to asymmetric branching as features in machine learning classification to distinguish between different cell types. We find significant distinctions in the asymmetric scaling ratios between Purkinje cells and motoneurons. The performance of these classification methods gives us important insights into the correspondence between structure and function across different cell types.

https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/09/Paheli-Desai-Chowdhri.mp4

Bioinformatics/Human Genetics Seminar Series: Katie Peichel, Head of the Division Evolutionary Ecology, University of Bern
2021-11-22 11:00 - 12:00 | ZOOM, CA, United States
“The role of chromosome evolution in adaptation and speciation in stickleback fish”
Hosted by Kirk Lohmueller

Bioinformatics/Human Genetics Seminar Series: Stephen Piccolo, Associate Professor, Biology, Member of Simmons Center for Cancer Research, Brigham Young University
2021-11-29 11:00 - 12:00 | ZOOM, CA, United States
TITLE: “TBD”
Hosted by Sarah Spendlove

Bioinformatics/Human Genetics Seminar Series: Shantanu Joshi, PhD, Assistant Professor, Neurology, Brain Mapping Center, UCLA
2022-01-03 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “Aligning Shape Data from Brain Imaging: applications to fMRI time series, diffusion tractography.”
Hosted by Jason Ernst.

QCBio Research Seminar: Jee Yun Han (Boutros), Graduate Student in MBIDP (Molecular Biology Interdepartmental Doctoral Program)
2022-01-05 11:30 - 12:00 | ZOOM, CA, United States
TITLE: “Comprehensive study of gene expression outliers and their regulation mechanisms in pan-cancer.”
ABSTRACT: With the ultimate aim of improving the management of cancer, many groups have studied the molecular characteristics of tumors. Typically, the heterogeneity of cancer evolves rapidly to adapt to its microenvironment, helping cancer evade selective pressures and progress. These various subclones result in resistance to anti-cancer therapies. The extreme dysregulation of some gene such as oncogene is a prominent feature of cancer that can play a critical role in cancer tumorigenesis and accelerating cancer evolution by providing cancer cells with a selective growth advantage. Further research is needed to understand cancer heterogeneity and the extreme gene dysregulation, and these studies will enable us to solve many limitations and obstacles for the inhibition and effective treatment of cancer. In previous studies, cancer outliers such as BCR-ABL and TMPRSS2-ERG were identified as cancer drivers or drug targets, and those showed strong associations with clinical outcomes. The gene expression outliers of cancer are likely to be caused by the diverse genetic and epigenetic variation frequently occurred in cancer. Despite of the important pathological function of cancer outliers, to date, the researches related to outliers have been only limited to a single type of cancer or a single gene, which is insufficient for characterizing them and comprehending their pathological roles in cancer. To fill the gap of our understanding regarding cancer outliers, this proposal will identify the outliers from various cancer types using our novel statistical method and explore the biological functions of outliers in individual cancer, figuring out the unique feature of each cancer. With integrated analysis of the clinical outcome data and the features of outliers within and across cancer types, it will show how outliers affect the progression of cancer. Comparative analysis transcriptome and proteome data analysis will address whether RNA outliers can be propagated into the protein level, prioritizing candidates. Furthermore, the molecular mechanisms for outlier generation in terms of genetic and epigenetic variation will be investigated in cancer, enabling us to understand the central pathways associated with extremely abnormal gene expression in individual cancers. The proposed study is expected to deepen our understanding of the impact of outliers on different cancers by dissecting their dysregulation, and, in turn, will allow us to identify a novel cancer driver and potential drug target.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Jee-Yun-Han-edited.mp4

QCBio Research Seminar: Jianxiao Yang (Suchard), Grad Student in Biomathematics
2022-01-05 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “Massive parallelization of massive sample-size survival analysis.”
ABSTRACT:
Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time andmemory efficient single-pass parallel scan algorithms for Cox proportional hazardsmodels and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders-of-magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Jianxiao-Yang-edited.mp4

Bioinformatics/Human Genetics Seminar Series: Lauren McIntyre, PhD, Professor, Molecular Genetics & Microbiology, University of Florida
2022-01-10 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “Climate change and maize response to pollutants: gene content, expression and regulation.”
Hosted by Kirk Lohmueller.

QCBio Research Seminar: Evan Maltz (Wollman), Graduate Student in BMSB (Biochemistry, Molecular and Structural Biology)
2022-01-12 11:30 - 12:00 | ZOOM, CA, United States
TITLE: “Phenotypic consequences of gene expression variability.”
ABSTRACT: The central dogma of biology proposes that information flows from genes to RNA to protein, determining protein identity and abundance. Single cell transcriptome measurements provide ample information about RNA abundance in cells. While the expression of individual genes clearly matters for determining phenotype, they do not work independently. How the information in RNA abundances is combined to determine cellular phenotype remains unclear. Here we address this question for a single cellular phenotype, the dynamic Ca2+ signaling response to a ligand. Using an information theory approach, we estimated the mutual information between the expression of subsets of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found a high degree of dependency that corresponds to 60% of Ca2+ signal entropy, yet most genes have redundant information. Furthermore, a relatively small group of genes preserve most of the dependency. Our results demonstrate a high degree of regulatory elasticity with small changes in RNA abundance informing an emergent cellular phenotype.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Evan-Maltz-edited.mp4

Videos can also be viewed on the QCBio YouTube Channel

QCBio Research Seminar: Dongyuan Song (Li JJ), Graduate Student in Bioinformatics
2022-01-12 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “scDesign3: an all-in-one statistical framework that generates realistic single-cell omics data and infers cell heterogeneity structure.”
ABSTRACT: The generation of realistic synthetic data is essential for benchmarking numerous computation tools developed for single-cell omics data. Here we propose an all-in-one statistical framework that generates single-cell omics data from various cell heterogeneity structures, including discrete cell types, continuous cell trajectories, and spatial cell locations. Our framework uses a unified probabilistic model with accessible likelihood. This probabilistic formulation is advantageous in that it enables a straightforward discernment of the heterogeneity structure that best fits a single-cell omics dataset, by leveraging the statistical model selection principle.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Dongyuan-Song-edited.mp4

Bioinformatics/Human Genetics Seminar Series: Jennifer Wilson, PhD, Assistant Professor, Bioengineering, UCLA
2022-01-24 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “Deriving network parameters for understanding drug effects.”
Hosted by Jason Ernst.

Bioinformatics/Human Genetics Seminar Series: Doc Edge, PhD, Assistant Professor, Quantitative and Computational Biology, USC
2022-01-31 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “The new forensic genetics: ‘long-range’ search and genetic privacy.”
Hosted by Nandita Garud.

QCBio Research Seminar: Xinzhou Ge (Li JJ), Postdoc in Statistics
2022-02-02 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “P-value-free solution to fix exaggerated false positives by popular differential expression methods.”
ABSTRACT: We report a surprising phenomenon that popular bioinformatics methods for identifying differentially expressed genes (DEG) between two conditions have unexpectedly high false discovery rates (FDRs) on large-sample-size RNA-seq datasets. Failed FDR control is likely due to the invalid p-values which rely on unrealistic assumptions. To address this issue, we use a general statistical framework Clipper to control the FDR in DEG analysis without relying on p-values.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Xinzhou-Ge-edited.mp4

Bioinformatics/Human Genetics Seminar Series: Dana Pe'er, PhD, Chair, Computational and Systems Biology Program, Sloan Kettering Institute
2022-02-07 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Cellular plasticity in Cancer.”
Hosted by Jason Ernst.

QCBio Research Seminar: Apeksha Singh (Hoffmann), Graduate Student in Biomathematics
2022-02-09 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “Characterizing distinct cell states based on stimulus-response dynamics.”
ABSTRACT: Macrophages show remarkable functional pleiotropy that is dependent on microenvironmental context. Prior studies have characterized how polarizing cytokines alter epigenetic or signaling mechanisms, but how they affect specific macrophage functions has not been characterized systematically. One hallmark function of macrophages is to mount immune-threat appropriate responses, in part via the signaling dynamics of transcription factor NFκB. Here, we measured single-cell nuclear NFκB trajectories in macrophages polarized into 6 states and stimulated with 8 different stimuli resulting in a vast dataset. Linear Discriminant Analysis revealed how NFκB signaling codons compose the immune threat level of stimuli, placing polarization states along a linear continuum between the M1/M2 dichotomy. Machine learning classification revealed losses of stimulus distinguishability with polarization, which reflect a switch from sentinel to effector functions. However, the stimulus response dynamics and discrimination patterns did not fit the M1/M2 continuum. Instead, our analysis suggests macrophage functional niches within a multi-dimensional polarization landscape.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Apeksha-Singh-Edited.mp4

QCBio Research Seminar: Richard Law (Park), Graduate Student in Chemical and Biomolecular Engineering
2022-02-09 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “Quantitative flux analysis reveals redistribution of glycolytic pathways in dynamic nutrient environments.”
ABSTRACT: Optimal operation of metabolic fluxes is critical for an organism to be evolutionarily competitive. Textbook glycolysis is a conserved pathway that optimally utilizes carbohydrates for growth. However, it is unclear why some organisms simultaneously possess the parallel Entner-Doudoroff (ED) pathway, which has a lower bioenergetic yield. By integrating stable isotope tracing, mass spectrometry, and mathematical modeling, we measure fluxes of these pathways in near-real time. Here, we identify the benefits of the ED pathway under transitory environments. We utilized these tools for flux analysis to study central carbon metabolism in dynamic nutrient conditions. Specifically, we hypothesized that parallel pathways enable cells to rapidly upshift their overall glycolytic flux to benefit growth in response to sudden nutrient availability. Our studies revealed that parallel yet specialized pathways enable dynamic redistribution of metabolic fluxes that are linked to rapid changes in metabolism and broader biological phenotypes.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Richard-Law-Edited.mp4

Bioinformatics/Human Genetics Seminar Series: Leila Jamal, PhD, Genetic Counselor and Bioethicist, Associate Director for Cancer Genomics, National Cancer Institute and the Johns Hopkins Bloomberg School of Public Health
2022-02-14 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Walking the line between progress and paternalism in genetic counseling.”
Hosted by Christina Palmer.

QCBio Research Seminar: Matthew Heffel (Luo), Graduate Student in Bioinformatics
2022-02-23 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “Multimodal Single-Cell Epigenomic Sequencing of the Developing Human Cerebral Cortex.”
ABSTRACT: Single cell epigenomic technologies allow the measurement of unique molecular signatures within cells, however cell type complexity remains highly enigmatic. Emerging methods have enabled multiple modalities of epigenomic sequencing to be gathered from the same cell. Single-nucleus methyl-3C sequencing (sn-m3C-seq) delivers the capacity to capture chromatin conformation and DNA methylation information of 5’-methylcytosines with single cell fidelity. Developing neurons accumulate significant DNA methylation at non-CG sites (mCH), adjust patterns of CG methylation (mCG), and endure rearrangements of chromatin domains. These patterns of mCH, mCG, and chromatin interactions are specific to neuronal subtypes. We collected prefrontal cortex samples from 13 individuals at several developmental time stamps divided into four major age groups, second trimester (2T), third trimester (3T), infant, and adult. Applying sn-m3C-seq to our data of >29,000 cells we identify 27 adult cell types and their developmental trajectories from five 2T cell types. The methylation features allow for intricate, deeply specific cell type annotations and while the 3C modality highlights less specific cell types, it can pick up forward trajectory signals that methylation either does not or that resolve as a single heterogeneous cell type in methylation; We observe developing glial progenitor cells identified in 3C that cluster as radial glia in the methylation feature space alone. The temporal forward stepping of the 3C feature set further allows us to validate cell types in different sample age groups. The integration of single cell modalities in sn-m3C-seq allows for the highly robust cell type classification as well as strong downstream analysis of epigenetic divergences of developing cell lineages.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Matthew-Heffel-edited.mp4

QCBio Research Seminar: Chenghao (Trevor) Zhu (Boutros), Postdoc in Human Genetics
2022-02-23 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “moPepGen: a fast custom database generator from multi-omics data for proteogenomics.”
ABSTRACT: Cancers are driven by genomic variants such as SNV (single nucleotide variants) and INDEL, often accompanied by many transcriptional variants. Modern mass spectrometry based proteomics is able to identify and quantify peptides and proteins comprehensively, however the variant-harboring peptides that are absent in canonical databases are largely under-studied. A common problem in custom database construction is the large number of combinations of variants to consider. Thus major proteogenomic studies often choose a strategy that only captures peptides harboring a single variant. Existing algorithms also suffer from limited sources of variation. We developed moPepGen (multi-omics peptide database generator) that aims at accelerating proteogenomic researching by generating custom peptide database from variety of genomic and transcriptional variants. MoPepGen uses a graph-based algorithm that achieves a linear time complexity in integrating variants in contrast to the exponential complexity of exhaust searching. MoPepGen integrates variants from a variety of sources including SNV, indel, transcription fusion, alternative splicing, RNA editing, and circRNA. It is able to generate peptides harboring any combinations of variants on the single peptide. MoPepGen is also highly extensible for other types of genomic and transcriptional variants.

Bioinformatics/Human Genetics Seminar Series: Jason Moore, PhD, Founding Chair of the Department of Computational Biomedicine, Cedars-Sinai
2022-02-28 16:00 - 17:00 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Automated genetic analysis”
Hosted by Nandita Garud.

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.

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

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: 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: 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: 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: 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/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/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. 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. 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.
