Annual BICB Research Symposium
Register now for the 15th Annual Bioinformatics and Computational Biology (BICB) Research Symposium on January 12, 2023 at the University of Minnesota, Rochester Campus.
This year’s program will include distinguished faculty speakers, presentations given by BICB graduate students, updates on the BICB program and a poster session.
**Oral and poster abstract submissions by the University of Minnesota and BICB-affiliated graduate students are due December 29, 2022.
**Transportation will be provided to and from the Twin Cities.
Presentation Titles and Abstracts
Nuri Ince, PhD, University of Houston
Investigation of Functional Utility of High Frequency Oscillations: Applications in Neuromodulation and Functional NeurosurgeryAbstract.
Abstract: Despite the recent advances in neural engineering to process oscillatory brain activity in different scenarios such as brain machine interfaces, limited progress has been done towards the interpretation of oscillatory neural activity (such as LFPs, iEEG or ECoG) with computational intelligence for clinical decision making. In this talk, I will summarize our efforts towards mapping of subcortical regions during awake brain surgeries using machine learning and neural signal processing for the optimization of DBS in PD. Moreover, I provide additional perspectives regarding the use of machine intelligence for the detection of localized high frequency oscillations (HFOs) in large scale iEEG datasets for identification of seizure onset zone in epilepsy.
Ju Sun, University of Minnesota
Three Pillars of Health Data Science: Transfer Learning, Federated Learning, and Imbalanced Learning.
Data poverty and data inequality are major roadblocks to advancing health data science. The former is mostly due to high annotation costs, data-sharing regulations, and intrinsic rarity. The latter takes the form of non-uniform representation and non-uniform label distribution. In this talk, I’ll describe three machine learning frameworks: transfer learning, federated leaning, and imbalance learning, that are crucial for addressing data poverty and inequality. I’ll highlight our efforts in revamping and optimizing these frameworks in tackling real-world healthcare problems.
Jaeyun Sung, Assistant Professor of Surgery, Department of Surgery, Mayo Clinic
Building Algorithm-based Indicators of Health Using the Gut Microbiome.
To date, human gut microbiome research has given us various convincing associations and potential mechanisms in complex, chronic diseases. An emerging direction in the years ahead is applying high-throughput microbial metagenomic data to build computational tools for public health innovations. In this talk, I will discuss recently published and ongoing works from our research group that demonstrate how the state of the gut microbiome reflects one’s present and future health. First, I will introduce the Gut Microbiome Health Index (GMHI), a stool-based indicator for determining the likelihood of having a disease independent of the clinical diagnosis. Next, I will show how a deep-learning neural network model, which was trained with gut microbiome data, can accurately predict clinical improvement for patients with rheumatoid arthritis. In all, our work aims to identify key insights into how the gut microbiome can be optimized to enhance therapeutic efficacy, prevent autoimmune inflammatory disorders, or promote healthy longevity.
Christopher Tignanelli - Associate Professor Division of Critical Care/Acute Care Surgery, Department of Surgery
Shizhen (Jane) Zhu, Mayo Clinic
Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma.
Neuroblastoma (NB) is the most common extracranial solid tumor in children. Although only a few recurrent somatic mutations have been identified, chromosomal abnormalities are often seen in the high-risk cases. The biological basis and evolutionary forces that drive such genetic abnormalities remain enigmatic. Here, we conceptualize the Gene Utility Model (GUM) that seeks to identify genes driving biological signaling via their collective gene utilities and apply it to understand the impact of those differentially utilized genes on constraining the evolution of NB karyotypes. By employing a computational process-guided flow algorithm to model gene utility in protein-protein networks that built based on transcriptomic data, we conducted several pairwise comparative analyses to uncover genes with differential utilities in stage 4 NBs with distinct classification. We then constructed a utility karyotype by mapping these differentially utilized genes to their respective chromosomal loci. Intriguingly, hotspots of the utility karyotype, to certain extent, can consistently recapitulate the major chromosomal abnormalities of NBs and also provides clues to yet identified predisposition sites. Hence, our study not only provides a new look, from a gene utility perspective, into the known chromosomal abnormalities detected by integrative genomic sequencing efforts, but also offers new insights into the etiology of NB and provides a framework to facilitate the identification of novel therapeutic targets for this devastating childhood cancer.
Nancy Scott (Advisor: Anna Selmecki)
ERG3 mutations mediate antifungal multidrug resistance in Candida lusitaniae.
Candida species are opportunistic pathogens and an important cause of hospital-acquired fungal infections. Only three major drug classes (azoles, echinocandins and polyenes) are available to treat fungal infections. Multidrug resistance in Candida species is on the rise, but the underlying mechanisms are poorly understood. Here we perform comparative genomic and phenotypic analysis of Candida lusitaniae to provide evidence that ERG3 point mutations drive multidrug resistance. Echinocandin exposure can select for ERG3 mutations within days during experimental evolution assays and, importantly, during human infection. C. lusitaniae ERG3 mutants have increased in vitro drug resistance to both micafungin (an echinocandin) and fluconazole (an azole). Echinocandins and azoles act on different target proteins, and our findings highlight the risk of acquired multidrug resistance due to mutations within a single gene.
Josh Fry (Advisors: Rendong Yang)
Surveying the exitron splicing landscape across cancer.
More than 95% of multi-exon human genes undergo alternative splicing of pre-mRNA, leading to multiple gene isoforms and protein products with potentially different functions in distinct cellular processes. Perturbations in alternative splicing are common across all cancers, where subtype-specific mRNA splicing can have prognostic value and contribute to the hallmarks of cancer progression. Earlier work in our lab has identified a novel species of alternative splicing, called exitron splicing (a portmanteau of 'exon' and 'intron'), which occurs when a region of a single exon is spliced out resulting in a split exon. Utilizing ~18,000 total tumor and normal samples from TCGA and GTEx libraries, we show that exitron splicing can disrupt protein domains within tumor suppressor genes, promote tumor progression and be a potential source of neoantigens. In order to survey the role of exitron splicing in cancer further, we are developing several bioinformatic tools for the detection, quantification and analysis of exitron splicing in emerging sequencing technologies, such as long-read and single-cell RNA sequencing. Long-read RNA sequencing enables unprecedented views of the full complexity of the transcriptome, but produces error-prone reads that must be carefully managed. We show that our long-read exitron detection tools are able to call exitrons in sequencing libraries with high error-rate and enable exitron specific transcript discovery. While our previous work focused on exitron splicing in protein coding regions, we show that exitron splicing is enriched in the 3'UTR, which is unique among alternative splicing types. We present data that shows how 3'UTR exitron splicing affects important post-transcriptional processing and modulates mRNA stability across cancer. In addition, we show that 3'UTR exitron splicing can be detected in 3' biased single-cell RNA libraries, enabling cell-type specific insights into the exitron splicing landscape.