T3 – Write your own R script to jointly analyse RNA-, ATAC-seq, DNA methylation, SNPs and find drug targets in signal transduction networks using TRANSFAC®
Date: September 9, 2018
Time: 13:30 – 17:00 (half day tutorial)
- Philip Stegmaier, geneXplain GmbH, Germany
- Olga Kel-Margoulis, geneXplain GmbH, Germany
- Alexander Kel, geneXplain GmbH, Germany; Institute of Systems Biology, Russia
This tutorial will first provide an introduction to the best selection of bioinformatics and systems biology tools for analysis gene regulatory mechanisms and to reveal drug targets. We will introduce tools such as: Match, MEME, ChIPMunk, PathFinder, and the databases: TRANSFAC, TRANSPATH, REACTOME, KEGG, HumanPSD as well as many other algorithms and tools that can be combined in task-oriented pipelines using web platforms such as BioUML (biouml.org; genexplain.com) and Galaxy (usegalaxy.org) as well as using R and Java API in your own applications. We will also explain the steps researchers should follow to get their tools running inside such platforms and how to build your specific pipelines that will use your own tools together with rich collection of other powerful tools in order to lead you quickly to the discovery. In the second part we will have a practical session where attendees will face examples of multi-omics data in different disease conditions and will be building analysis pipelines for drug target discovery. At the end we will run a contest “Find your own drug” where the best pipeline will be presented and discussed.
Transcription regulation is of central importance for nearly all processes in a living system, and erroneous transcription control is causative for numerous diseases. To enable a systems approach to transcription, we still have to struggle with the very first step that is to infer underlying wiring diagrams. Empirical information about the interaction of regulators (transcription factors) and the regulated target genes, obtained by either conventional or high-throughput methods, has been collected in the TRANSFAC database for over 30 years, and statistical models inferred from this information have been included as positional weight matrices (PWMs) and made available for the prediction of regulatory sites as well. New extensions include syntax (relevant combinations) and semantics (regulated processes) of regulatory sites. Extended annotation of gene-disease associations is available in the Human Proteome Survey Database (HumanPSD), connected with signaling pathways that control the activity of TFs (TRANSPATH database). All this carefully curated information can be used in full power to analyze disease related multi-omics data using the BioUML/geneXplain platform, which helps to decipher the molecular mechanisms of disease often on very early stages of its progression. First of all, differentially expressed genes revealed by microarray or RNA-seq analysis are combined with genomic (SNP) and epigenomic (ChIP-seq/ATAC-seq and DNA methylation) assays to find disease-related enhancers. Next, genetic algorithms reveal TFs synergistically acting in those enhancers. Finally, topology analysis of signal transduction networks upstream of transcription factors identifies master-regulators of the disease progression, which proposed as perspective therapeutic targets.
The goal is that users attending the tutorial acquire basic knowledge in:
- Concepts of molecular mechanisms of gene regulation. Regulatory code.
- How to write your own R script to analyze multi-omics data
- Practical experience with drug target discovery.
The course addresses researchers in biology, bioinformatics, biochemistry and medicine with interest in gene regulation and related topics.
- Laptop with installed and running Internet browser (Chrome, Firefox, Opera, Safari), is to be used during the tutorial
- Basic biochemistry
- Elementary computing skills
- Background knowledge in bioinformatics is not absolutely necessary, but may be instrumental
|time||subject – tutor|
|13:30||Welcome – Alexander Kel|
Principles of gene regulatory code. Experimental approaches. Microarrays, RNA-seq,
ATAC-seq, ChIP-seq, ENCODE, FANTOM – Alexander Kel
Introduction to BioUML/GeneXplain, TRANSFAC, Reactome, TRANSPATH, HumanPSD
– Olga Kel-Margoulis
Writing your code using R and Java API
Ananlysis of TF binding sites, composite elements; promoters, enhancers;
signal transduction networks, finding key nodes and master regulators.
– Philip Stegmaier
Hands-on to construct the workflow to find “Your own drug”– Philip Stegmaier,
Olga Kel-Margoulis, Alexander Kel