Announcements

seminar/talk
Dr. Uygar Sümbül on “Multimodal characterization of neuronal cell types”

Dr. Uygar Sümbül will give a seminar on “Multimodal characterization of neuronal cell types” on December 4 at 10 am. The abstract of the talk and a short bio is shared below.


Bio:
Uygar Sümbül is an Assistant Investigator at Allen Institute, Seattle, where he focuses on the intersection of neuroscience and machine learning. He obtained a Ph.D. in Electrical Engineering and a Ph.D. minor in Mathematics from Stanford University in 2009. Prior to joining the Allen Institute, Uygar held postdoctoral researcher positions at the Dept. of Brain and Cognitive Sciences at MIT, and the Dept. of Statistics & Grossman Center for the Statistics of Mind at Columbia University. He completed a BS degree in Electrical Engineering at Bilkent University, Turkey, in 2003.

Abstract:
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. While paired multimodal measurements to simultaneously characterize single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. We present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction and identifies cell types that are consistent across modalities.

01/12/2020
seminar/talk
Dr. İnci M. Baytaş on “Data-Driven Techniques in Biomedical Informatics”

Dr. İnci M. Baytaş will give a seminar on “Data-Driven Techniques in Biomedical Informatics” on November 27 at 4 pm. The abstract of the talk and a short bio is shared below.


Bio:
İnci M. Baytaş received her bachelor's and master's degrees from Electronics and Communication Engineering Department at Istanbul Technical University in 2012 and 2014, respectively. She joined the Ph.D. program of Computer Science and Engineering at Michigan State University in 2014. She has been working as an Assistant Professor in the Computer Engineering Department at Bogazici University since 2019. Her research interests are deep learning, adversarial machine learning, multi-task learning, temporal analysis, and their applications on biomedical informatics.

Abstract:
With innovations in digital data acquisition devices and increased memory capacity, virtually all commercial and scientific domains have been witnessing exponential growth in the amount of data they can collect. For instance, healthcare is experiencing tremendous growth in digital patient information due to the high adaptation rate of electronic health record systems in hospitals. The abundance of data offers many opportunities to develop robust and versatile algorithms. Biomedical informatics is an interdisciplinary domain, where machine learning techniques are adopted to analyze electronic health records (EHRs). EHR comprises digital patient data with various modalities and depicts an instance of big data. For this reason, analysis of digital patient data is quite challenging although it provides a rich source for clinical research. In this seminar, common tasks and challenges in biomedical informatics will be introduced. Then, state-of-the-art machine learning and deep learning solutions to some of the prevalent biomedical informatics tasks will be presented.

26/11/2020
seminar/talk
Dr. Sanjarbek Hudaiberdiev on “Using Deep Learning to infer causality of GWAS SNPs”

Dr. Sanjarbek Hudaiberdiev will give a seminar on “Using Deep Learning to infer causality of GWAS SNPs” on November 20 at 4 pm. The abstract of the talk and a short bio is shared below.


Bio:
Sanjar Hudaiberdiev is a research fellow at National Center for Biotechnology and Information (NCBI) of National Institutes of Health (NIH). His current research is focused on the development and implementation of data-centric computational approaches to understand the mechanisms of non-coding regulatory elements in human genome. He received his BSc degree in Computer Science at Erciyes University in Kayseri, and Ph.D. degree at International Center for Genetic Engineering and Biotechnology (ICGEB) in Trieste, Italy.

Abstract:
Decomposition of GWAS signals into the causal SNPs and the noise brought by linkage disequilibrium (LD) remains a key question to understand the mechanisms and the genetic underpinnings of complex human diseases. The overwhelming majority of SNPs linked to the diseases fall into non-coding regions of human genome. We developed a two-step deep learning (DL) framework that identifies active regions within regulatory elements (REs) and quantifies the influence of any arbitrary point mutation on the activity of the host RE. Our framework is tissue-specific, and we show that the mutation-susceptible regions in REs largely correspond to the binding sites of active transcription factors (TFs) and that the predicted mutational impact of these regions matches the binding specificity of the corresponding TFs. We further show that our scores of mutational impacts strongly correlate with the experimental data from a set of arrays, including those for quantitative trait loci for chromatin accessibility (caQTLs), massively parallel reporter assays (MPRAs), and reporter assay QTLs (raQTLs). Application of our method for resolving the ambiguity in LD of Type 2 Diabetes (T2D) genome-wide association studies (GWASs) consisting of 403 strong genetic T2D associations resulted in 706 (5.7%) genetic associations with quantifiable impact on pancreatic islet enhancer activity. We confirmed the directionality and magnitude of disrupted enhancer activity for a panel of experimentally validated T2D single nucleotide polymorphisms (SNPs), and we predicted 46 novel T2D causative enhancer mutations.

17/11/2020
seminar/talk
Dr. Burçak Otlu on “Evaluating topography of mutational signatures with SigProfilerTopography”

Dr. Burçak Otlu will give a seminar on “Evaluating topography of mutational signatures with SigProfilerTopography” on November 13 at 4 pm. The abstract of the talk and a short bio of Dr. Otlu are provided below.


Bio:
Burçak Otlu is a postdoctoral scholar in UC San Diego, Cellular and Molecular Medicine Department since March 2018. She is working with Asst. Prof. Ludmil Alexandrov on mutational signatures and topography of somatic mutations across the genome. Meanwhile, she is one of the scientists in CRUK Grand Challenge, Mutographs of Cancer Project. Formerly, she was a visiting scholar in UC San Diego, Computer Science, and Engineering Department under the supervision of Prof. Vineet Bafna between August 2017 and February 2018. She is the author of papers published in Bioinformatics, BMC Bioinformatics, Circulation, Nature Communications, and Cell. She received her Ph.D. from Middle East Technical University, Computer Engineering Department where she focused on developing tools and techniques for assessing the functional relevance of genomic loci. Prior to coming to UC San Diego, she has worked as a teaching assistant in the same department for several years. She has also worked in the private sector as a software engineer.

Abstract:
Mutations are found on the genomes of all cancerous and normal somatic cells. These mutations were generated by the activities of endogenous and exogenous mutational processes which were operative throughout the cell lineage. Mutational processes imprint characteristic patterns of somatic mutations, termed, mutational signatures. To exemplify, mutational signature associated with tobacco smoking causes C>A mutations prominently on the transcribed strand, whereas mutational signature due to exposure to ultraviolet light results in C>T mutations on the untranscribed strand during DNA transcription. Previous analyses have demonstrated that somatic mutations in cancer are not uniformly distributed across the landscape of the genome. Importantly, mutational signatures imprinted by different mutational processes exhibit distinct topographical properties including to be located on (i) early or late replicating regions, (ii) genic or intergenic regions, (iii) transcribed strand or untranscribed strand of DNA with respect to the transcription process, (iv) leading or lagging strand of DNA in regard to DNA replication. Remarkably, mutational signatures may accumulate somatic mutations preferably at nucleosome occupied loci, chromatin accessible regions, transcription factor binding sites, and histone modification sites. Here I present SigProfilerTopography, the most advanced tool for evaluating the topography of mutational signatures. SigProfilerTopography allows examining all types of mutational signatures and reveals topographical dependencies related to chromatin accessibility, nucleosome occupancy, histone modifications, transcription factor binding sites, replication timing, transcription strand bias, replication strand bias, and processivity. The tool also allows performing user-defined custom analysis based on custom assays. Having augmented with realistic simulated mutations, SigProfilerTopography assesses the significance of its findings which finally characterizes the mutational signatures and gives insight about their underlying biological mechanisms.

10/11/2020
seminar/talk
Dr. Melike Dönertaş on "In silico studies to understand and intervene in aging"

Dr. Melike Dönertaş will give a seminar on “In silico studies to understand and intervene in aging” on November 6 at 4 pm. The abstract of her talk and her short bio is shared below.


Bio:
Melike Dönertaş completed her BSc in Molecular Biology and Genetics in 2014 and her MSc in Biological Sciences in 2016, both at METU. She worked as a research and teaching assistant during her MSc. Then she did her Ph.D. at the University of Cambridge as an EMBL fellow. She is currently a postdoctoral fellow at the EMBL-EBI and Institute of Healthy Ageing at UCL. She has worked on a variety of topics ranging from cheminformatics to evolutionary genomics and ancient DNA studies, but her main research focus has been on aging, disease, and anti-aging interventions.

Abstract:
Aging is the major risk factor for a variety of non-communicable diseases. With the increase in life expectancy, aging poses significant challenges to individuals, societies, and healthcare systems. Aging is a complex phenotype with many interconnected cellular and organismal phenotypes. Thus, understanding aging and finding potential interventions require system-level approaches. In this seminar, Melike Dönertaş will summarise her recent research to understand the link between aging and age-related diseases, and different drug repurposing strategies to find potential anti-aging interventions for humans. She will first describe the common genetics of age-related diseases determined by the analysis of medical and genomic data for almost half a million participants in the UK. She will also present her ongoing research to find drugs that can promote healthy aging in humans using transcriptome, structural data, and electronic health records.

05/11/2020