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|Course Info||CSE 590C is a weekly seminar on Readings and Research in Computational Biology, open to all
graduate students in computational, biological, and mathematical sciences.|
|Theme||Traditionally, we reserve Spring quarter for "homegrown" research --- highlights of work by
researchers in the Seattle area. Our tentative Spring schedule is:|
|Papers, etc.|| |
Note on Electronic Access to JournalsThe UW Library is generally a paid subscriber to non-open-access journals we cite. You can freely access these articles from on-campus computers. For off-campus access, follow the "[offcampus]" links below or look at the library "proxy server" instructions. You will be prompted for your UW net ID and password.
03/29: -- ---- Organizational Meeting ----
04/05: Linking T cell receptor sequence to transcriptional profiles with clonotype neighbor graph analysis (CoNGA) -- Dr. Phil Bradley -- FHCRC
Authors: Stefan A. Schattgen, Kate Guion, Jeremy Chase Crawford, Aisha Souquette, Alvaro Martinez Barrio, Michael J.T. Stubbington, Paul G. Thomas, Philip Bradley
Abstract: Multi-modal single-cell technologies capable of simultaneously assaying gene expression and surface phenotype across large numbers of immune cells have described extensive heterogeneity within these complex populations, in healthy and diseased states. In the case of T cells, these technologies have made it possible to profile clonotype, defined by T cell receptor (TCR) sequence, and phenotype, as reflected in gene expression (GEX) profile, surface protein expression, and peptide:MHC (pMHC) binding, across large and diverse cell populations. These rich, high-dimensional datasets have the potential to reveal new relationships between TCR sequence and T cell phenotype that go beyond identification of features shared by clonally related cells. In order to uncover these connections in an unbiased way, we developed a graph-theoretic approach---clonotype neighbor-graph analysis or âCoNGAâ---that identifies correlations between GEX profile and TCR sequence through statistical analysis of a pair of T cell similarity graphs, one in which cells are linked based on gene expression similarity and another in which cells are linked by similarity of TCR sequence. Applying CoNGA across diverse human and mouse T cell datasets uncovered known and novel associations between TCR sequence features and cellular phenotype including the classical invariant T cell subsets; a novel defined population of human blood CD8+ T cells expressing the transcription factors HOBIT and HELIOS, NK-associated receptors, and a biased TCR repertoire, representing a potential previously undescribed lineage of ânatural lymphocytesâ; a striking association between usage of a specific V-beta gene segment and expression of the EPHB6 gene that is conserved between mouse and human; and TCR sequence determinants of differentiation in developing thymocytes. As the size and scale of single-cell datasets continue to grow, we expect that CoNGA will prove to be a useful tool for deconvolving complex relationships between TCR sequence and cellular state in single-cell applications.
04/12: Joint identification of neuron types and type-specific activity-regulated genes with coupled autoencoders -- Dr. Yeganeh Marghi -- Allen Institute
Abstract: Recent advances in single-cell transcriptomics revealed an enormous diversity among neuronal cells. While the previous studies confirmed the existence of broad neuron classes, they also pointed towards a complicated landscape, where neuron types often appear to overlap or form gradients in gene expression. Therefore, a crucial step toward elucidating the neuronal identity is to jointly identify the discrete and the continuous factors of variability. Taking advantage of deep learning approaches, we study this problem in a variational framework by utilizing multiple interacting autoencoders, designed to disentangle the discrete and continuous aspects of neuronal diversity. We demonstrate the application of our method to a stand-alone single cell RNA sequencing dataset, which defined over 100 transcriptomic neuron types in the mouse cortex. Our results suggest that the proposed method can refine the existing classifications of neurons by joint identification of discrete types and type-specific, activity-regulated genes.
04/19: Hybrid Molecular-Electronic Computing // Interpreting Neural Networks for Bio
Sequences -- Alyssa & Jason // Alyssa
Two short talks this week:
04/26: Mapping and navigating the human regulatory genome -- Dr. Wouter Meuleman -- Altius Institute
Abstract: This year marks the 20 year anniversary of the sequencing of the human genome in 2001. Since then, many large-scale data generation and analysis efforts have built upon this work by producing genome-wide maps and annotations. I will highlight several of our own efforts in this area, in particular of maps that describe the human genome in terms of functional and contextual regulatory annotations. Despite the increasing comprehensiveness, quality and utility of genomic maps over the last two decades, systems to efficiently navigate them at scale have remained lacking. This is at least in part due to the enormous growth of available genomic data, resulting in a vast underutilization of data by a confined focus on the data directly at hand. I will outline ideas for how to address this challenge in a way that combines human expertise with machine-assisted intelligence.
Abstract: Genomics and the physical chemistry of biomolecules are two pillars of molecular biology. One of the key branches of the latter is chemical thermodynamics, which, curiously, is quite marginalized in the current research on computational molecular biology (CMB). I shall revisit this subject, but no starting from physics nor chemistry, but rather through a result from probability, beyond the law of large numbers and central limit theorem, call large deviations theory. I shall show how our theory can be applied to Fisher's FTNS as well as to analyzing data from single cells.
05/10: May the Methanotrophs be with you -- Erin -- CSE
05/17: Correcting Measurement Error in Microbiome Data -- David Clausen -- Biostatistics
05/24: Similarity Search with DNA // TBA -- Lee // TBA
Two short talks this week:
05/31: -- Holiday
|Other Seminars||Past quarters of CSE 590C|
COMBI & Genome Sciences Seminars
|Resources||Molecular Biology for Computer Scientists, a primer by Lawrence Hunter (46 pages)|
A comprehensive FAQ at bioinformatics.org, including annotated links to online tutorials and lectures.
CSE 527: Computational Biology
CSEP 527: Computational Biology (Professional Masters Program)
Genome 540/541: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis
Computer Science & Engineering|
University of Washington
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX