SNP Chip Data Analysis for Whole Genome Association Studies I - PowerPoint PPT Presentation

SNP Chip Data Analysis for Whole Genome Association Studies I nforSense Limited Sha Liao , Life Science Application Scientist July 11, 2007 www.inforsense.com

What is GenSense? -- Field: Statistical Genetics -- Focus on: Genome-Wide Association studies -- Subject: SNP Chip Data ~~ Rapid identification of phenotype/genotype associations and predictive biomarkers using GenSense

What can GenSense do? Portal – End user environment Portal – End user environment Data Quality Analysis Results Data Quality Analysis Results Handling Control Methods Interpretation Handling Control Methods Interpretation -- Organise data in projects -- Assess quality of SNPs and samples -- Analyse data using statistical tests -- Annotation services like ‘SNP to gene’ mapping

Example: Affymetrix SNP Chip Data

Duplicate Check

Analysis: SNP, MAF, HWE

Interpret: Map SNPs to Genes

Ingenuity Interpret: Genes to Pathways GeneGO

Why use GenSense? � -- Automatic organization of imported data � -- Effective utilization of analysis approaches � -- Wide integration of related tools � -- Deployable reuse of completed workflows � -- Intuitive visualization of performed results

Automatic organization of imported data Portal – End user environment Portal – End user environment Data Quality Analysis Results Data Quality Analysis Results Control Methods Interpretation Handling Control Methods Interpretation Handling � Affymetrix, Illumina, Linkage, PLINK, HapMap & PrettyBase formats � Generic � SNP major � Sample major � SNP, Sample triple � Data reduction � Flexible import styles

Effective utilization of analysis approaches Portal – End user environment Portal – End user environment Analysis Data Quality Results Analysis Data Quality Results Handling Control Interpretation Methods Handling Control Interpretation Methods � SNP and genotype analysers � Haplotype analysers � Genotype/Phenotype correlation and simulation � Linkage Disequilibrium (LD)

Wide integration of 3rd part tools Portal – End user environment Portal – End user environment Analysis Results Data Quality Analysis Results Data Quality Handling Control Methods Interpretation Handling Control Methods Interpretation � Genome browser (UCSC) � Haploview � PLINK � PHASING

Deployable reuse of completed workflows Portal – End user environment Portal – End user environment

Intuitive visualization of performed results --Spotfire --Haploview --HeatMap …

How to use GenSense? � -- Portal: Deployable service � -- Client: Build application workflows

Portal – Homepage Portal – Homepage

Portal – Create a new project Portal – Create a new project

Portal – Service Links Portal – Service Links

Portal – Duplicate Check Results Portal – Duplicate Check Results

Portal – HeatMap Visualization Portal – HeatMap Visualization

Client: Build workflows

Interaction With Other Plug-in BioSense GeneGO, Ingenuity

ClinicalSense

Thank you! � For more information, please visit: � http://www.inforsense.cn � http://www.inforsense.com � For questions please contact � Sha Liao liaosha@inforsense.com � Ronghua Zeng rhzeng@inforsense.com

SNP Chip Data Analysis for Whole Genome Association Studies I - PowerPoint PPT Presentation

SNP Chip Data Analysis for Whole Genome Association Studies I nforSense Limited Sha Liao , Life Science Application Scientist July 11, 2007 www.inforsense.com What is GenSense? -- Field: Statistical Genetics -- Focus on: Genome-Wide

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Workshop exercise Data integration and analysis In this exercise, we would like to work out

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Analysis of structural genome varia3on in whole genome and exome sequencing data Victor Guryev

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Whole Genome Analysis and Annotation Adam Siepel Biological Statistics & Computational

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SNP Chip Data Analysis for Whole Genome Association Studies I - PowerPoint PPT Presentation

SNP Chip Data Analysis for Whole Genome Association Studies I nforSense Limited Sha Liao , Life Science Application Scientist July 11, 2007 www.inforsense.com What is GenSense? -- Field: Statistical Genetics -- Focus on: Genome-Wide

Toward a More Accurate Genome: Algorithms for the Analysis of High- Throughput Sequencing Data

ChIP-seq data analysis 04-05-12 Outlook Friday 04-05-12: Next-generation sequencing

Introduction to Chromatin IP sequencing (ChIP-seq) data analysis Workshop on ChIP-seq data

The Epigenome Tools 2: ChIP-Seq and Data Analysis Chongzhi Zang zang@virginia.edu

Workshop exercise Data integration and analysis In this exercise, we would like to work out

Genome-Wide Association Studies Caitlin Collins , Thibaut Jombart MRC Centre for Outbreak Analysis

Analysis of structural genome varia3on in whole genome and exome sequencing data Victor Guryev

Analysis of structural genome variation in whole genome and exome sequencing data Victor Guryev

Whole Genome Analysis and Annotation Adam Siepel Biological Statistics &amp; Computational

Scaling normalisation for ChIP-seq with exogenous chromatin Workshop on ChIP-seq data analysis

Re-analysis of a CD4 ChIP-Seq data set with csaw Ryan C. Thompson Salomon Lab The Scripps

Current Topics in Genome Analysis Fall 2006 Week 4: Mining Genomic Sequence Data Tyra G.

ANALYSIS OF MULTIPLE RELATED PHENOTYPES IN GENOME-WIDE ASSOCIATION STUDIES Taesung Park 1 Sohee

Genetic variation: SNPs ATTGCAATCCGTGG...ATCGAGCCATACG ATTGCACGCCG Basics What is

Bag of Nave Bayes: biomarker selection and classification from Genome-Wide SNP data Francesco

Genetic analysis of complex traits in the age of the genome-wide association study David Duffy

Methods for Analyzing ChIP-Seq data Introduction to the ChIP-Seq server at SIB Lausanne Public

Genome-wide supervised ChIP-seq peak detection Toby Dylan Hocking toby.hocking@mail.mcgill.ca

ChIP-seq data analysis Introduction to practicals I: data processing ENCODE Dataset chr 1 &amp;

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Is Reuse Distance Applicable to Data Locality Analysis on Chip Multiprocessors? Yunlian Jiang

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