جستجوی استاد راهنمای دانشگاهی

Molly Megrawمشاهده پروفایل

دانشیار

• Transcription Factors
• Machine Learning
• Arabidopsis
• +۶ مورد دیگر

Molly Megraw is an Associate Professor at Oregon State University's College of Agricultural Sciences , affiliated with the Department of Botany and Plant Pathology , Molecular and Cellular Biology Program , and Center for Genome Research and Biocomputing . She also collaborates with the Department of Electrical Engineering and Computer Science. Education : Ph.D. in Bioinformatics (2007) from the University of Pennsylvania Her research integrates computational biology , machine learning , and plant genomics to study: Transcriptional regulation of Pol-II genes and miRNAs in plants Network motif analysis in TF-miRNA circuits Chromatin accessibility and promoter identification via nanoCAGE Synthetic biology applications in plant systems Recent publications focus on: Salinity stress responses in rice miRNA editing in human cancer Network motif discovery algorithms PlantSimLab modeling platform Scientific accomplishments include: NSF CAREER Award (2018) for machine learning models of gene regulation MiRGen database development for miRNA genomic organization She supervises graduate students in Biological and Physical Sciences (BPP) and teaches computational courses like BDS 470/570 Introduction to Computing in the Life Sciences . Her lab maintains active collaborations across plant biology and computational disciplines.

Linda Hoangمشاهده پروفایل

استاد بالینی

• Medical Microbiology
• Public Health Laboratory Science
• Genomic Epidemiology
• +۷ مورد دیگر

Linda Hoang is a medical microbiologist and medical director of the British Columbia Centre for Disease Control (BCCDC) Public Health Laboratory, and a clinical professor in the Department of Pathology & Laboratory Medicine at the University of British Columbia (UBC). Dr. Hoang completed both her medical school and medical microbiology residency training at UBC, becoming a Fellow of the Royal College of Physicians and Surgeons of Canada in 2006. She also obtained a diploma in tropical medicine and hygiene from the London School of Hygiene & Epidemiology in the UK. Dr. Hoang has been based at BCCDC since 2006, though she has been involved in BCCDC-led projects in Vietnam and BC since 1998. Dr. Hoang has pioneered advancements in public health laboratory science in British Columbia. Her research focuses on medical microbiology, genomic epidemiology, and infectious disease surveillance. She led the modernization of the Public Health Advanced Bacteriology and Mycology Program for over 17 years, implementing innovative diagnostic techniques such as 16S and ITS sequencing directly from clinical samples. Her work has significantly improved the province's capacity to detect and respond to infectious disease threats, including foodborne outbreaks and healthcare-associated infections. Analysis of Dr. Hoang's publications reveals a consistent focus on applying genomic technologies to public health challenges. Her research spans diagnostic test evaluation, pathogen surveillance, antimicrobial resistance monitoring, and pandemic response. She has made significant contributions to understanding the epidemiology of bacterial pathogens including Neisseria gonorrhoeae, Streptococcus pyogenes, and Streptococcus pneumoniae, as well as viral pathogens like SARS-CoV-2. Medical Director, BCCDC Public Health Laboratory Site Director, Medical Microbiology Residency Training Program Former Medical Co-Director for the Provincial Infection Control Network (PICNet) until 2023 Dr. Hoang has played a critical role in building British Columbia's public health laboratory infrastructure. She was instrumental in implementing Canada's first Laboratory Response Network program for the 2010 Olympics and has led the integration of genomic technologies into routine public health surveillance. Her leadership was particularly crucial during the COVID-19 pandemic, where she helped establish a high-throughput pathogen-agnostic genomics hub at the BCCDC Public Health Laboratory as a pandemic legacy.

Pengfei Liuمشاهده پروفایل

دانشیار

• Genomic Medicine
• Mendelian Disorders
• Clinical Transcriptomics
• +۵ مورد دیگر

Pengfei Liu, Ph.D. , is an Associate Professor in the Department of Molecular and Human Genetics at Baylor College of Medicine and Associate Clinical Director of NGS/Molecular at Baylor Genetics . He directs the ACGME/ABMGG Laboratory Genetics and Genomics Fellowship Program and leads the Medical Genetics and Multiomics Laboratory (MGML), a CLIA-certified diagnostic facility. Education: PhD from Baylor College of Medicine (2012), BS from Nankai University Certifications: American Board of Medical Genetics (Clinical Molecular Genetics #2015130, Laboratory Genetics and Genomics #2023143) Dr. Liu's research focuses on genomic medicine for rare diseases , with emphasis on clinical implementation of whole genome/exome/transcriptome sequencing . His lab pioneers patient-derived cell transdifferentiation for functional variant analysis and antisense oligonucleotide therapies for Mendelian disorders. Current projects include integrating low-cost sequencing technologies (Ultima Genomics) and characterizing genetic modifiers in recurrent deletions (e.g., 1q21.1 and 17q12 syndromes). His publications span genomic structural variation mechanisms , transcriptome diagnostics , and AI applications in Mendelian disease diagnosis (AI-MARRVEL system). The lab contributes to national initiatives including NIH Undiagnosed Diseases Network (UDN), GREGoR, RADIANT, and PrenatalSEQ consortia. Scientific Awards: C. W. Cotterman Award (ASHG 2012) NHGRI Top 10 Genomic Advances (2019) ACMG Michael S. Watson Innovation Award (2022) NHGRI Genomic Innovator Award (2021) The MGML, under Dr. Liu's leadership, operates the first clinical RNA-seq pipeline for the NIH Undiagnosed Diseases Network. The lab trains clinical geneticists through its ACGME-accredited fellowship program and collaborates with families affected by SPTAN1 mutations to develop individualized therapies.

• Bioinformatics
• Computational Biology
• RNA Biology
• +۵ مورد دیگر

Chao Zeng is an Associate Professor at Waseda University’s Research Institute for Science and Engineering, affiliated with the School of Advanced Science and Engineering. He holds a PhD in Science from the University of Tokyo (2016) and a Master’s in Information Science from Kyoto University (2013). His research integrates computational biology and RNA genomics, focusing on non-coding RNAs, RNA localization mechanisms, and repetitive genetic elements. Dr. Zeng’s work spans bioinformatics algorithm development , RNA structure-function relationships , and transcriptomic regulation in disease . Key interests include lncRNA interactions with ribosomes, phase separation of RNA, and RNA-binding protein networks. His lab employs high-throughput sequencing, machine learning, and comparative genomics to decode RNA regulatory systems across species. His publications emphasize RNA biology in cancer and fibrosis , with recent work exploring mitotic RNA editing, inflammatory recovery pathways, and RNA quality control. Article analyses reveal consistent themes: lncRNA mechanisms (35%), RNA-protein interactions (25%), computational methods (20%), splicing/localization (15%), and translational applications (5%). Awards & Grants: Waseda University Teaching Award (2022) JSPS Grants: PI for ‘Repetitive Elements in Genome Regulation’ (2022-2025) and ‘lncRNAs in Genetic Compensation’ (2020-2022) Co-investigator for Phase Separation Research (2023-2026) and Cancer RNA Mechanisms (2021-2023) He leads the RNA Bioinformatics group at Waseda, collaborating with the Hamada Lab and international teams. Future work targets RNA-based therapeutics and multi-omics integration for disease modeling.

Antonio Ribeiroمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Computational Biology
• DNA Sequence Analysis
• +۳ مورد دیگر

Dr. Antonio Ribeiro serves as a Bioinformatician at the Centre for Genome-Enabled Biology and Medicine (CGEBM) within the School of Medicine, Medical Sciences and Nutrition at the University of Aberdeen. He joined the institution in August 2015, bringing extensive expertise in DNA sequence alignment, SNP calling, and next-generation sequencing data analysis. His technical proficiency spans Bash scripting, Java, and Perl for developing and optimizing bioinformatics pipelines. Dr. Ribeiro holds an MSc in Computational and Systems Biology from FIOCRUZ-RJ in Brazil, along with specializations in Computer Networks (PUC-Rio) and Information Systems Development (NCE-UFRJ). Prior to his academic career, he accumulated over 20 years of industry experience as a Customer Service Engineer with companies including Nokia-Siemens Networks and Xerox. His research focuses on advanced bioinformatics applications in microbial genomics, molecular epidemiology, and NGS data analysis. Dr. Ribeiro's work bridges computational methods with medical and biological applications, particularly in bacterial pathogen analysis, antimicrobial resistance studies, and genomic variation detection. His technical background enables sophisticated analysis of complex genomic datasets across diverse biological contexts. Analysis of his publication history reveals a strong trajectory in computational genomics with increasing focus on medical applications. His recent work (2023-2025) centers on molecular epidemiology of bacterial bloodstream infections, antimicrobial resistance mechanisms, and clinical applications of genomic analysis. Earlier publications (2012-2016) demonstrate foundational work in NGS data processing, SNP validation, and bioinformatics tool development. Dr. Ribeiro's technical expertise in processing and analyzing NGS data using both third-party open-source tools and custom scripts positions him at the intersection of computational science and biological research. His work supports critical medical research through advanced genomic analysis capabilities, contributing to understanding of bacterial pathogens, antimicrobial resistance, and genomic variation.

• Bioinformatics
• Genomics
• Algorithm Design
• +۵ مورد دیگر

Jean-Stéphane VARRÉ is a Professor in the Computer Science Department at the University of Lille and a core member of the BONSAI bioinformatics research team. His administrative leadership includes: Vice-head of the Computer Science Department (since 2018) In charge of the B.Sc. in Computer Science (since 2015) Former head of the MOCAD master's degree in Computer Science (2010-2015) His research centers on algorithmic solutions for genomic challenges, specializing in genomic rearrangements with duplicated markers, transcription prediction (splicing/orthology), third-generation sequencing assembly, and GPU-accelerated bioinformatics. Key contributions include ProCARs for ancestral genome reconstruction and the TFM suite for sequence comparison using position weight matrices. Recent publications (2019) demonstrate dual expertise in microbial genomics (bacterial assembly graph analysis) and clinical virology (coronavirus OC43 sequencing protocols), reflecting his team's capacity to bridge theoretical algorithm design with high-impact biomedical applications through scalable computational frameworks. Professor VARRÉ has co-supervised six doctoral candidates across 17 years: Pierre Marijon: Assembly graph analysis (current) Antoine Thomas: Genomic rearrangements with duplicates Tuan Tu Tran (2009-2012): Manycore architecture data structures Aude Darracq (2006-2010): Plant mitochondrial genome rearrangements Aude Liefooghe (2004-2008): Transcription factor binding site algorithms Martin Figeac (2002-2005): Constrained genomic rearrangement scenarios The BONSAI team maintains an active software repository for genomic analysis tools, including GPU-optimized string matching implementations and breakpoint region analyzers, supporting both academic research and clinical applications through open-source computational pipelines.

Lukasz Krychمشاهده پروفایل

دانشیار

• Food Microbiology
• Gut Health
• Fermentation
• +۳ مورد دیگر

Lukasz Krych is an Associate Professor in the Department of Food Science at the Faculty of Science, University of Copenhagen. His research focuses on the intersection of food microbiology, gut health, and fermentation technologies, with particular expertise in high-throughput community sequencing and bioinformatics. Dr. Krych's educational background includes: MSc and engineer degree in biotechnology (major: Molecular Diagnostics) from the University of Life Sciences, Poznan, Poland (2008) PhD from the University of Copenhagen, Denmark (2014) Postdoctoral research at the Department of Food Science, Faculty of Science, University of Copenhagen (2014-2018) Dr. Krych's research investigates the association between gut microbiome and health and disease in human and animal models using molecular genetic methods and classical microbiology techniques. He specializes in developing protocols for Illumina and Oxford Nanopore sequencing, high-throughput qPCR, and NGS data analysis including amplicon sequencing and metagenome assembly. His computational expertise spans Qiime, C++, R, Matlab, and bash, with additional skills in hardware assembly and software management for custom-built analysis workstations. Analysis of Dr. Krych's recent publications reveals a strong interdisciplinary approach spanning microbiology, genomics, and food science. His work applies advanced sequencing technologies to diverse ecosystems including human gut, animal models, and food systems, with particular emphasis on long-read sequencing for improved phylogenetic analysis, microbiome interactions with health conditions, and historical microbial cultures. Dr. Krych teaches multiple courses including Microbiology of Fermented Food and Beverages, Dairy Microbiology, Basic Cell and Molecular Biology, and Yeast Physiology and Applications at bachelor's and master's levels, plus the Microbiome Analysis PhD course. His teaching directly reflects his research expertise in food microbiology and molecular analysis techniques. Dr. Krych's laboratory collaborates with GenXone, one of Europe's first certified Oxford Nanopore Technologies laboratories, developing molecular diagnostics for genetic disorders and pathogen detection. His research team investigates microbial communities in food processing environments, gut health models, and historical culture preservation, demonstrating strong industry-academic partnerships and methodological innovation.

• Non-protein coding RNAs (ncRNAs)
• RNA structure biology
• Next-generation sequencing
• +۵ مورد دیگر

Ernst Stefan Seemann serves as an Associate Professor in the Department of Veterinary and Animal Sciences within the Faculty of Health and Medical Sciences at the University of Copenhagen. His research focuses on non-protein coding RNAs (ncRNAs) and RNA structure functionality, utilizing advanced methodologies including RNA structure modeling, prediction of RNA-RNA and protein-RNA binding, and analysis of next-generation sequencing data. His primary research interests encompass: Non-protein coding RNAs (ncRNAs) and RNA structure biology Next-generation sequencing and single-cell RNA-seq analysis Comparative genomics and prediction/annotation of ncRNAs Host-pathogen RNA interactions Stem cell models for neurodegenerative diseases Seemann is actively involved in multiple high-impact research initiatives including investigations of novel ncRNAs in bacteria (NextProd project), animal ncRNAs (FAANG consortium), host-virus RNA-RNA interactions with Assoc Prof Troels KH Scheel, single-cell RNA sequencing in neurogenesis studies with Assoc Prof Vanessa J Hall, and transcriptomics in stem cell models for neurodegenerative diseases (NeuroStem project). His publication record demonstrates strong interdisciplinary collaboration across computational biology, veterinary science, and medical research. His scientific contributions span environmental microbiology, gene therapy, neuroscience, and computational biology, with recent work focusing on CRISPR applications, CAR T-cell engineering, microglial metabolism in dementia, and metabolic engineering of bacterial systems. His research has been cited across multiple platforms including academic journals, Mendeley readership, Wikipedia, and news outlets.