Transcript Chipster What is it?

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
Chipster
Goals
Enable researchers without programming skills or
extensive bioinformatics knowledge to:
• access to an extensive selection of up-to-date tools for highthroughput 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
How does it look?
Chipster
Architecture
Authentication
service
Management
service
Message broker
File broker
Clients
Brokers
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Loosely coupled, independent components
Message oriented communications
Flexible, scalable, robust
Computing
services
Chipster
NGS data analysis
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
Genome Browser
Features
• Open source, java-based
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Interactive zooming from full chromosome down to nucleotide level
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Ensembl annotations for transcripts and genes including miRNA
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Easily extendable with new tracks, views and file formats
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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
Acknowledgements
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)