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Massimiliano Gentile CSC – IT Center for Science

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

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Massimiliano Gentile CSC – IT Center for Science

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  1. ChIP-seq data analysis and visualization using ChipsterWorkshop on next generation sequencing data analysis31.5 - 4.6.2010Espoo Massimiliano Gentile CSC – IT Center for Science

  2. 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

  3. Chipster Goals Enable researchers without programming skills or extensive 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

  4. Chipster How does it look?

  5. Chipster Architecture • Loosely coupled, independent components • Message oriented communications • Flexible, scalable, robust

  6. 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

  7. Features • 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 Genome Browser • 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

  8. Genome Browser Tree-based summarization

  9. Genome Browser Fully zoomed out, ChIP-seq example

  10. Genome Browser Zoomed to transcript level

  11. Genome Browser Zoomed to ChIP-seq peak level

  12. Genome Browser Zoomed to nucleotide level

  13. Genome Browser RNA-seq example

  14. 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)

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