The Computational Genomics Office at the IGB identifies and provides resources to support computational aspects of research at the Institute. Contact us at email@example.com and see below for more.
Genomics data analysis short course
The IGB Computational Genomics, in collaboration with HPCBio, will be piloting a four-session short course in genomics data analysis. The goal is to provide a conceptual framework for how to learn bioinformatics. This framework will be illustrated with bulk and single-cell RNA-seq analyses using the R programming language. The course will assume knowledge at the level of the HPCBio workshops. Email firstname.lastname@example.org to attend.
The course will meet on the following Mondays from 12:00 - 1:00 pm CT. Preliminary syllabus:
Topics: Concepts and framework of bioinformatics, formulating data analyses
Exercises: Identify literature examples of genomic analyses, apply framework to examples
Topics: Discussion of literature examples, canonical data analysis strategies
Exercises: Individually formulate analysis questions of interest, obtain and preprocess relevant data
Topics: R and R workflow, identify canonical data analysis strategies, bulk RNA-seq tutorial
Exercises: Another bulk RNA-seq tutorial, single-cell RNA-seq tutorial
Topics: Guided analyses of individually formulated questions
Exercises: Publish paper
Matching students with computational projects
We are piloting a program to help themes at the IGB identify students of all levels to work on computational projects. Themes can generate text for job ads, which we will use to recruit and screen candidates from across campus. The ad generator and further instructions can be found here.
Students who are interested in learning more about genomics can check out IGB's MOOC called Genomics: Decoding the Universal Language of Life.
Spatial Omics Initiative
The IGB recently established this initiative to advance research in spatial omics at the University of Illinois. See here for more information.
Illinois researchers are pushing the frontiers of computational omics and bioinformatics in many different directions. Find below this amazing cast of computational scientists with highly varied interests and expertise.
Medical Decision Making; Computational Biology
Md Tauqeer Alam
Veterinary Clinical Medicine
Infectious Diseases Genomics; Bacterial Genomics; Metagenomics
Integrative omics data analysis
Scientific Visualization; Visual Analytics; User Experience
Interaction of plants and microbes; Evolution of biological networks; Function and structure in proteins and ncRNA; Evolution of macromolecular structure
Evolution, Ecology, and Behavior
Genome evolution; Structural variation; Population Genomics
Biophysics; Systems Biology; Noise Biology; Drug Screening; Virology
Mathematical biology, Biophysical modeling, Neuronal networks
tumor phylogenies; copy-number aberrations and their evolution; migration history of metastatic tumors.
High Performance Computing in Biology group (HPCBio), Roy J Carver Biotechnology Center
Applied bioinformatics; bioinformatics training; genomics analysis workflows and pipelines; Bioinformatics infrastructure; microbiome analysis
Biological text mining
Plant and animal genomics; population genomics; genetic basis of crop traits; interactions of plants with pathogens and pests
Electrical and Computer Engineering
Personalized healthcare analytics, and hardware and software systems for healthcare.
Infectious diseases; Accelerating microbiology; design new therapies
Biomedical informatics; natural language processing; literature-based knowledge discovery
Computational genomics; sequence bioinformatics; high performance computing for biology;
Bioinformatics; Biomolecular modeling; Cancer biology; Cancer genomics
Compression, gene regulatory networks, Gene prioritization
Naveen Naidu Narisetty
high dimensional data analysis; model selection; Bayesian computation; large-scale computational models; functional data; quantile modeling.
Functional and Structural Genomics; Cancer Genomics; Pharmacogenomics; Neurodegenerative Diseases
Genetics, Genomics, and Bioinformatics; Immunophysiology and Behavior; Meat Science and Muscle Biology; Nutrition; Production and Environment Management; Reproductive Biology
Electrical and Computer Engineering
Algorithms for genome assembly, Metagenomics, Information Theory, Data Science and Machine Learning
Chemical and Biomolecular Engineering
Molecular Engineering; Molecular Modeling and Simulations; Biophysics
Regulatory Genomics; Systems Biology; Neurogenomics; Cancer genomics.
integrative genomics; transcription factors; chromatin structure; non-coding RNAs; gene regulation in development and diseases; cancer genomics; physics-inspired machine learning
Data and Information Systems; Bioinformatics and Computational Biology; Artificial Intelligence
microbiome data analysis, high dimensional statistics, statistical machine learning
phylogenomics (genome-scale phylogeny estimation); multiple sequence alignment; metagenomics; Statistical estimation, applied probability, machine learning, graph algorithms
Health informatics; Biological text mining
Sihai Dave Zhao
Statistical genomics; High-dimensional statistics; Survival analysis
Personalized Medicine; Survival Analysis