ChIP-seq data analysis and visualization using Chipster

Workshop on next generation sequencing data analysis

31.5 - 4.6.2010

Espoo

Massimiliano Gentile

CSC – IT Center for Science

Chipster What is it?

User-friendly analysis software and workflow tool

• Intuitive GUI, interactive visualizations• Analysis steps taken can be saved as an automatic workflow, which can be shared

Generic platform

• currently used mainly for microarray data and proteomics data • building support for ChIP-seq, RNA-seq and miRNA-seq

Client-server system: centralized maintenance and updates

• Also Web services (SOAP) are connected to the system

Open source, server installation packages available

• http://chipster.sourceforge.net/

http://chipster.csc.fi

Enable researchers without programming skills orextensive bioinformatics knowledge to:

• access to an extensive selection of up-to-date tools for high-throughput data analysis

• work with the data through a graphical and intuitive user interface

• combine tools into automatic workflows that can be shared

• integrate different types of data and analysis workflows

• interpret results in meaningful and efficient visualizations

Chipster Goals

Chipster How does it look?

Chipster Architecture

• Loosely coupled, independent components• Message oriented communications• Flexible, scalable, robust

Clients

Authentication service

Management service

Computing services

Brokers

Message broker

File broker

Currently building support for: ChIP-seq

RNA-seq,

miRNA-seq

MeDIP-seq, BS-seq

Tools Preprocessing (merging, sorting, filtering, …)

Alignment (Maq, Bowtie, TopHat, …)

Peak detection (MACS, PeakSeq, …)

Motif and TFBS detection

Finding neighbouring genes

Pathway analysis

RNA-seq: quantitation and detection of novel splice variants

Integration with target gene expression

Visualization

Genome Browser

Chipster NGS data analysis

Genome BrowserFeatures

• Open source, java-based• Interactive zooming from full chromosome down to nucleotide level• Ensembl annotations for transcripts and genes including miRNA• Easily extendable with new tracks, views and file formats• Standalone as well as Integrated with Chipster analysis environment

Challenges

• Handle very large data sets

•View both the big picture and the details

• Smooth zooming and browsing

Solution

• Optimize global viewing by data sampling: details not read when looking at the big picture

•Optimize local viewing: the whole data not read when looking at a detail

• Both optimizations need random access to data, at the moment local files

Genome Browser Tree-based summarization

Genome Browser Fully zoomed out, ChIP-seq example

Genome Browser Zoomed to transcript level

Genome Browser Zoomed to ChIP-seq peak level

Genome Browser Zoomed to nucleotide level

Genome Browser RNA-seq example

Chipster development team

Jarno Tuimala

Eija Korpelainen

Aleksi Kallio

Taavi Hupponen

Petri Klemelä

Mikko Koski

Janne Käki

Collaborators

Ilari Scheinin

Laura Elo

Dario Greco

Funding agents

Tekes (SYSBIO research programme)

European Commission (FP6 NoE EMBRACE)

Acknowledgements