2-5 June 2025
To foster international participation, this course will be held online
This course will introduce participants with the inference and analysis of biological networks from RNA-seq data using R and Bioconductor packages. The course will cover data structures for
quantitative data and graphs, statistical methods for network inference, functional analyses of biological networks, and network comparison. At the end of the course, participants will be able to
infer and analyze gene coexpression networks (GCNs) and gene regulatory networks (GRNs), compare networks, and integrate GCNs with genetic markers to prioritize candidate genes associated with
traits.
The course is targeted to researchers and students that would like to learn how to use R and Bioconductor to infer and analyze networks for systems biology projects. Familiarity with RNA-seq and core Bioconductor data structures (e.g., SummarizedExperiment and GRanges) is helpful, but not essential, as they will be covered during the course. Participants need to have a working knowledge of R (R syntax, commonly used functions, basic data structures such as data frames, vectors, matrices and their manipulation).
Monday - 2-7 pm Berlin time zone
Inference and Analysis of Gene Co-expression Networks (GCNs)
Theory:
● Types of networks
● Topological properties of biological networks
● Representing data as graphs
● Bias correction in co-expression network inference
Practice:
● Quantitative data and the SummarizedExperiment class
● Preprocessing, normalization, and transformation of expression data
● Inference of co-expression networks
● Functional analyses of network modules
● Network visualization
Tuesday- 2-7 pm Berlin time zone
Network comparison
Theory:
● Parametric and non-parametric network comparison
● Cross-species network comparison
Practice:
● Identifying consensus modules
● Identification preserved modules between networks
● Functional analyses of differences and similarities between networks
Wednesday - 2-7 pm Berlin time zone
Network-based data integration for gene discovery
Theory:
● Introduction to genome-wide association studies
● Bottlenecks in SNP-to-gene mapping
Practice:
● The GRanges class
● SNP-to-gene mapping
● Prioritizing candidate genes associated with a phenotype
Thursday - 2-7 pm Berlin time zone
Inference and Analysis of Gene Regulatory Networks (GRNs)
Theory:
● An overview of GRN inference algorithms
● GRN inference through an ensemble of methods
● GRN analyses and applications
Practice:
● Inference of GRNs
● Functional analyses of GRNs
● GRN visualization
480 €
800 €
Should you have any further questions, please send an email to info@physalia-courses.org
Cancellation Policy:
> 30 days before the start date = 30% cancellation fee
< 30 days before the start date= No Refund.
Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.