Institute for Quantitative and Computational Biosciences

Bioinformatics/Human Genetics Seminar Series: Fabian Theis, Head of the Institute of Computational Biology at Gelmholtz Munich, Professor, Technical University of Munich
2021-10-25 11:00 - 12:00 (America/Los_Angeles) | ZOOM, CA, United States
TITLE: “Learning cellular state and dynamics in single cell genomics”
Hosted by Jessica Li

QCBio Research Seminar: Giovanni Quinones Valdez (Xiao), Grad student in Bioengineering
2021-10-27 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “scAllele, a versatile tool for the detection and analysis of variants in scRNA-seq.”
ABSTRACT: Single-cell RNA sequencing (scRNA-seq) data contain rich information at the gene, transcript, and nucleotide levels. Most analyses of scRNA-seq have focused on gene expression profiles, and it remains challenging to extract nucleotide variants and isoform-specific information. Here, we present scAllele, an integrative approach that detects single nucleotide variants, insertions, deletions, and their allelic linkage with splicing patterns in scRNA-seq. We demonstrate that scAllele achieves better performance in identifying nucleotide variants than other commonly used tools. The read-specific variant calls by scAllele enables allele-specific splicing analysis. Applied to a lung cancer scRNA-seq data set, scAllele identified variants with strong allelic linkage to alternative splicing, some of which being cancer-specific. scAllele represents a versatile tool to uncover multi-layer information and novel biological insights from scRNA-seq data.

https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Giovanni-Valdez.mp4

QCBio Research Seminar: Ulzee An (Sankararaman), Grad student in Computer Science
2021-10-27 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “AutoComplete: Deep Learning-based Phenotype Imputation”
ABSTRACT: Health data has become increasingly available, vast in scale, and highly missing. For many downstream applications, the ability to accurately impute missing features in health records may tap into additional analytical power which would be unrealized otherwise. While existing imputation methods are applicable, many fall short in one or more aspects of being reliable or scalable in the domain of massive, highly incomplete, and heterogenous population-scale data. We propose AutoComplete, a deep learning-based imputation method that extends with ease to incomplete datasets with millions of entries and handles heterogeneous data of continuous and categorical format. In imputing phenotypes for a collection of half-million individuals from the UK Biobank, AutoComplete significantly improved imputation accuracy for several phenotypes in comparison to best-performing low-rank factorization and deep learning methods.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Ulzee-An.mp4

Bioinformatics/Human Genetics Seminar Series: Anya Prince, Associate Professor of Law, Genetic Cluster, University of Iowa College of Law
2021-11-01 11:00 - 12:00 | ZOOM, CA, United States
“Regulating Insurer Use of Genetic Information”
Hosted by Christina Palmer

QCBio Research Seminar: Alexander Markowitz (Boutros), Postdoc in Human Genetics
2021-11-03 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “A pan-cancer multi-omic analysis of tumor proliferation.”
ABSTRACT: The underlying mechanisms of dysregulated cellular proliferation in cancer remain unclear; however, it is hypothesized that specific mutations, mutational signatures, evolutionary trajectories and other global (epi)genomic features may be linked to differing rates of proliferation. In this presentation, I will showcase a landscape analysis of cellular proliferation across primary cancers and cell lines using genome, transcriptome and proteome data from 11,597 primary tumors and 1,804 cell lines across six major consortia. The approach is to develop computational pipelines that will take in next-generation sequenced, multi-omic datasets from an expanding source of consortia, apply rigorous statistical methods to test multiple hypotheses on the global, functional and pharmacogenomics of tumor proliferation and output detailed integration analyses on the underlying genomics of tumor proliferation. By evaluating proliferation at these molecular levels, key insights will be yielded including expansive new resources on the variation of proliferation rate across and within cancer types.
For lunch at 12:30pm with the speaker, please RSVP at qcboffice@lifesci.ucla.edu

QCBio Research Seminar: Ricky Wolff (Garud), Graduate Student in Ecology & Evolutionary Biology
2021-11-03 13:00 - 13:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome.”
ABSTRACT: The human gut microbiome is a complex community that harbors substantial ecological diversity at the species level, as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome community are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of co-colonizing lineages. In this study, we sought to characterize the typical strain-level dynamics of the healthy human gut microbiome on timescales ranging from days to years. We show that genetic diversity within almost all species is stationary, tending towards a long-term typical value within hosts over time scales of several years, despite fluctuations on shorter timescales. Moreover, the abundance fluctuations of strains can be sufficiently described by a stochastic logistic model (SLM) – a model used to describe abundance fluctuations among species around a fixed carrying capacity – in the vast majority of cases, suggesting that strains are dynamically stable. Lastly, we find that strain abundances follow the same macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Ricky-Wolff.mp4

Bioinformatics/Human Genetics Seminar Series: Eimear Kenny, Founding Director, Institute for Genomic Health, Mount Sinai School of Medicine
2021-11-08 11:00 - 12:00 | ZOOM, CA, United States
“Population genetics in an era of genomic health”
Hosted by Christa Caggiano

QCBio Research Seminar: Mariana Harris Heredia (Garud), Grad student in Biomathematics
2021-11-10 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Differences in the Signatures of Selection Between the Autosomes and the X Chromosome in Drosophila melanogaster.”
ABSTRACT: Sex chromosomes in Drosophila are found in a hemizygous state in males. Thus, deleterious mutations may be purged more rapidly on the X chromosome than on the autosomes. This purging can result in less available standing variation that can seed adaptation on the X chromosome. Here, we investigate how differences in the recessivity of alleles on the X chromosome versus autosomes impact the mode and tempo of adaptation. Specifically, we test the hypothesis that hard sweeps, in which a single adaptive mutation rises to high frequency, are more common on the X chromosome, while soft sweeps, in which multiple haplotypes rise to high frequency simultaneously, are more common on the autosomes. We analyze D. melanogaster genomic data from two populations, North Carolina, and Zambia, and find evidence that suggests that indeed, hard sweeps are more prevalent on the X chromosome and soft sweeps are more common on the autosomes.
For lunch at 12:30pm with the speaker, please RSVP at qcboffice@lifesci.ucla.edu

QCBio Research Seminar: Guo Xiaohui (Pinter-Wollman), Postdoc in Ecology & Evolutionary Biology
2021-11-10 13:00 - 13:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Decoding alarm signal propagation of seed-harvester ants, Pogonomyrmex californicus.”
ABSTRACT: Alarm signal propagation through social-insect colonies provides an empirically tractable context for analyzing information flow through a natural system, with useful insights for network dynamics in other social groups, including human social networks. Here, I develop a methodological approach to track alarm spread within the group of harvester ants, Pogonomyrmex californicus. I alarmed initial 3 individuals and tracked subsequent signal transmission through the group. Because there was no actual threat, the alarm was false, allowing us to assess amplification and adaptive damping of collective alarm response. I trained a Random-Forest machine learning regression model to quantify alarm behavior of individual workers from multiple movement features associated with alarm behavior in this species. This approach provides reliable continuous assessments of an individual’s behavioral state at much finer temporal scales and more consistently than can be achieved visually. I combined the ML alarm state assessments with proximity data from tracking software (ABCTracker) to construct a propagation network of alarm spread. Using this system, alarm propagation can be manipulated and assessed to ask and answer a wide range of questions on information and misinformation flow in social networks.
For lunch at 12:30pm with the speaker, please RSVP at qcboffice@lifesci.ucla.edu

Bioinformatics/Human Genetics Seminar Series: Erick Matsen, Professor, Public Health Sciences Division, Associate Program Head of Herbold Computational Biology Program, Fred Hutchinson Cancer Center
2021-11-15 11:00 - 12:00 | ZOOM, CA, United States
“Towards Bayesian phylogenetics via systematic search and gradient ascent”
Hosted by Sriram Sankararaman

QCBio Research Seminar: Chloe Yap (Gandal), Visiting Graduate Student, University of Queensland
2021-11-17 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Restricted diet drives autism gut-microbiome associations, and other tales from the Australian Autism Biobank”
ABSTRACT: The Australian Autism Biobank (AAB) is an initiative of the Autism CRC – the first national, cooperative research effort focused on autism across the lifespan. The AAB recruited a total of ~2,500 autistic children, family members, and unrelated undiagnosed children, and couples deep phenotypic information with multi-omic datasets (SNP genotyping, stool metagenomics, DNA methylation, metabolomics, WGS). One particularly controversial area in the field has been the potential causal contribution of the gut microbiome to autism. I will present results from one of the largest metagenomics study of the autism stool microbiome to date (n=246, including 99 children diagnosed with autism). We propose a model whereby microbiome changes in autistic children may reflect consequences of behaviour and dietary preferences, and we caution against undue emphasis on the microbiome having an upstream role in autism.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Chloe-Yap.mp4

QCBio Research Seminar: Paheli Desai-Chowdhry (Savage), Grad student in Biomathematics
2021-11-17 13:00 - 13:30 | 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.
