CBCB Seminar Series

UPDATE (Aug. 3, 2022): For Fall 2022, the CBCB Seminar Series will be held weekly during the academic year, with the goal of bringing the CBCB community together to learn about the research being done at the Center, at UMD, and at other institutions. To this end, the seminar features invited speakers from academia and industry as well as talks by faculty and graduate students in the Center. Talks by invited speakers are 40-50 minutes followed by questions. Talks by graduate students are 20 minutes followed by questions. If you are interested in speaking, please contact Erin Molloy. The CBCB Seminar Series will be held on Thursdays from 2-3pm at the Iribe Center in room 4105. If you're unable to attend in person, please contact us for a Zoom link.

Other seminars you may be interested in attending can be found HERE


    CBCB Seminar Series Schedule for Fall Semester 2022



    Institution or Advisor/Lab

    Topic & Abstract

    Location or Zoom Only


    Michael Cummings

    CBCB, Director




    Ben Langmead

    Johns Hopkins University

    Pan-genomic advances for fighting reference bias

    Abstract: Sequencing data analysis often begins with aligning sequencing reads to a reference genome, where the reference takes the form of a linear string of bases. But linearity leads to reference bias, a tendency to miss or misreport alignments containing non-reference alleles, which can confound downstream statistical and biological results. This is a major concern in human genomics; we don't want to live in a world where diagnostics and therapeutics are differentially effective depending how closely our genome matches the reference.

    Fortunately, computer science and bioinformatics are meeting the moment. In particular, we can now index and align sequencing reads to references that include many population variants. Here I will describe this journey from the early days of efficient genome indexing -- especially the FM index approach behind Bowtie and BWA -- continuing through more modern methods for graph-shaped references and references that include many genomes. I will emphasize recent results that show how to optimize simple and complex pan-genome representations for effective avoidance of reference bias. Finally, I will outline some promising future areas, including a new class of compressed indexes that improves locality of reference.

    Iribe #4105


    Lichun Ma

    National Cancer Institute

    Understanding of tumor heterogeneity and tumor evolution in liver cancer

    Abstract: Liver cancer, the second most lethal malignancy in the world, consists of mainly hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). However, most of patients with HCC and iCCA have limited response to molecularly-targeted therapies. Tumor heterogeneity is key factor for therapeutic failures and lethal outcomes of solid malignancies. My lab applies cutting-edge single-cell and spatial approaches to profile primary tumors from liver cancer patients and develops computational methods to understand important biological questions in liver cancer including tumor heterogeneity and tumor evolution, with the goal of improving early detection and therapeutics for liver cancer.

    Iribe #4105


    Tobias Rubel

    CBCB/Molloy Lab

    Fast and accurate heuristics for the Maximum Triplet Support Species Tree problem

    Abstract: Reconstructing the evolutionary history, or phylogeny, of a collection of species is a core task of evolutionary biology, and is instrumental for analyses in nearly all areas of the biological sciences. The task is particularly difficult in light of biological processes which cause discordance with respect to the history of genes and that of species. A number of methods have been developed to infer species trees from (estimated) gene trees when gene tree discordance is due to deep coalescence. The most popular of these are heuristics for the NP-hard Maximum Quartet Support Species Tree (MQSST) problem; such methods attempt to maximize the number of shared four-taxon subtrees (quartets) between the input gene trees and the output species tree. Recently, there has been interest in a related problem for rooted trees, which is based on three-taxon subtrees (triplets). In this talk, I examine two heuristics for the Maximum Triplet Support Species Tree (MTSST) problem: one that leverages graph cuts to build the species tree in a divide-and-conquer algorithm fashion and the other that applies dynamic programming within a constrained version of the solution space. I show that the runtime of both of these heuristics can be reduced by a factor of n, so that our new algorithms run in O(n^2k) and O(nkx^2) time, respectively, where n is the number of species, k is the number of gene trees, and x is the size of the constrained solution space. This allows us to benchmark these methods on large heterogeneous data sets, shedding light on why such methods have so-far failed to achieve the accuracy of their more popular quartet-based cousins.



    François LeBreton

    Walter Reed

    Genomic tracking of superbugs in the Military Health System: from continents to hospital wards

    Abstract: Active surveillance is critical for detecting and preventing the spread of multidrug-resistant organisms (MDRO) globally and in the clinic. The Multidrug-Resistant Organism Repository and Surveillance Network (MRSN) is the primary surveillance organization for the DoD and collects MDROs from an extensive network of healthcare facilities in the continental US and overseas. Recently the MRSN developed an approach for the routine detection, in near real-time, of possible MDRO outbreaks. This service does not depend on human pattern detection but employs systematic genomic comparison of newly received MDROs to a private repository of >65,000 historical isolates. Upon detection of highly genetically related isolates, epidemiological analyses are initiated, and hospitals are immediately alerted. Among the successful outcomes, a multi-ward outbreak involving six patients caused by a carbapenem-resistant A. baumannii was detected early, tracked in the hospital environment, and successfully eradicated. Other outcomes included the detection of a carbapenem-resistant P. aeruginosa clone for which retrospective analysis, together with Bayesian phylogenetics and environmental sampling, revealed an outbreak with cases spanning decades from 20+ wards and all floors of a single hospital.

    Bio: Dr. Francois Lebreton obtained his PhD in microbiology from the University of Caen (France) and completed his postdoctoral training with Mike Gilmore and Ashlee Earl at Harvard Medical School and The Broad Institute. Since 2019, Dr. Lebreton is the head of bioinformatics within the Multidrug-resistant organism Repository & Surveillance Network (MRSN) at the Walter Reed Army Institute of Research. His team uses next generation sequencing and comparative genomics to provide routine, real-time surveillance and detection of MDR bacterial outbreaks in a large network of military treatment facilities, in continental USA and overseas. His research focuses on conducting retrospective and prospective outbreak analyses to identify the factors that influence the emergence of MDR populations, their distinguishing characteristics, and the evolutionary drivers that shape them.

    Iribe #4105


    Theresa Alexander

    CBCB/El-Sayed Lab




    Harihara Muralidharan

    CBCB/Pop Lab




    Ryan Blaustein

    UMD - Nutrition and Food Science


    Iribe #4105


    Matthew Pennel

    University of Southern California


    Iribe #4105


    Renee Ti Chou

    CBCB/Cummings Lab




    Megan Fritz

    UMD - Entomology Dept.


    Iribe #4105


    Thanksgiving Holiday


    Jamshed Khan

    CBCB/Patro Lab