| 2014 2 24()- 28()
| Ǵ âȸ 3 õȦ

| Ǵ н, ý ̿ (SBI-NCRC)
| Ƿȸ, ѱýۻȸ

6 Genome Data Analysis Workshop ϸ

  1 GDA Workshop: 2011 8 22~26, Ǵ

  2 GDA Workshop: 2012 2 20~24, Ǵ
  2 ο ǽ 3 ߰ Ǿ.
  
(1) micro-RNA м
   (2) ü ؼ: Personal Genome Interpretation
   (3) ü/ȯü м

  3 GDA Workshop: 2012 8 20~24, Ǵ
    3 2 ǽ ߰Ǿ.
 
 (1) Family-based ؽ м
   (2) TCGA (The Cancer Genome Atlas) м

  4 GDA Workshop: 2013 2 18~22, Ǵ
    4 2 ǽ ߰Ǿ.
 
 (1) eQTL м
   (2)
PheWAS & EWAS м

  5 GDA Workshop: 2013 8 26~30, Ǵ
  5 ο ǽ 3 ߰ Ǿ.
  
(1) ü м: Isoforms, Alternative Splicing, RNA-editing, and Fusion Gene
   (2) ü ؼ /ͱ ڿ Ұ м
   (3) Post-GWAS: EMR Ϳ м

  6 2 ǽ ߰ ̴.
 
 (1) Human Genome Data Analysis using ENCODE
   (2) Cancer Genome Data Analysis using TCGA

ü  м
ǽ "ü м" Ⱓ

  

         ֽϴ.

DAY 1: Advanced Microarray Data Analysis

           2 24()

ð

    

8:30 ~ 9:30

α׷ ġ

9:30 ~ 9:50

Advanced Microarray Data Analysis

9:50 ~ 10:40

Gene Expression Analysis
- Normalization
- Differential Expression Analysis
- Classification Analysis


(Ǵ)

10:50 ~ 12:10

I: Bioconductor
          t-test, SAM, ANOVA, FDR
          LDA, DTs, SVM

̼,

12:10 ~ 13:10

    

13:10 ~ 14:00

Clustering and eQTL Analysis of Gene Expression Data
- Clustering Analysis
- Cis- and trans-expression eQTL
- eQTL hotspots
- Connection to GWAS

հ
(ִ)

14:10 ~ 15:30

II: KNN, SOM, HC, PCA
           Identify eQTL hotspot
           eQTL resource

, ȼ

15:40 ~ 16:30

Gene-set Approaches & Prognostic Subgroup Prediction
- Gene Ontology & Pathway Analysis
- Gene Set Enrichment Analysis
- Prognostic Subgroup Prediction

ڻ
(ǿ)

16:40 ~ 18:00

III: Gene Set Enrichment Analysis
           Cox-PH, Log Rank Test
           David, ArrayXPath

,

 

DAY 2: Next Generation Sequencing & Personal Genome Data Analysis

          2 25(ȭ)

ð

    

8:30 ~ 9:30

α׷ ġ

9:30 ~ 9:50

Next Generation Sequencing & Personal Genome Data Analysis

 

9:50 ~ 10:40

NGS Platforms and Applications
- Current NGS Platforms
- NGS Data Formats
- NGS Data Analysis Technologies
- NGS Applications

ڻ
(ͽ)

10:50 ~ 12:10

I: NGS Data Processing
         NGS Data Format Converting
         NGS Visualization Tools

,

12:10 ~ 13:10

    

13:10 ~ 14:00

NGS Data Analysis
- Sequence Alignment Algorithms
- Whole Genome and Exome Data Analysis
- Variation Detection and Reference Genome

ֹ
(Ǵ)

14:10 ~ 15:30

II: Exome Sequencing Alignment
          SNP and Indel Identification
          Variation Filtering

,

15:40 ~ 16:30

Personal Genome Interpretation
- Phenotype Annotation
- Genetic Risk Prediction
- Healthcare Application


(Ǵ)

16:40 ~ 18:00

III: SNP Prioritization
            Genetic Risk Prediction methods
            Resources for Personal Genome Interpretation
            (dbGAP, PheGeni, SNPedia, PhenoDB)

̼,

 

DAY 3: RNA-seq Data Analysis

          2 26()

ð

    

8:30 ~ 9:30

α׷ ġ

9:30 ~ 9:50

RNA-seq Data Analysis

 

9:50 ~ 10:40

RNA-Seq Expression Profile Analysis
- Read Alignment Methods
- Expression Quantification Strategy
- Differentially Expressed Genes Identification
- Expression Profile Analysis

ڻ
(Zü)

10:50 ~ 12:10

I: Read alignment with TopHat,
          Expression Quantification with Cufflinks
          RNA-Seq Gene Expression Analysis

,

12:10 ~ 13:10

    

13:10 ~ 14:00

Sequence-level Transcriptome Analysis
- Novel Transcript Discovery
- Alternative Splicing Identification
- RNA-editing Analysis
- New/Fusion Gene Identification


(Ǵ)

14:10 ~ 15:30

II: Alternative Splicing Identification
           RNA-DNA Difference (RDD) Analysis
           RNA Editing Site Annotation

̼,

15:40 ~ 16:30

Non-coding RNAs in RNA-Seq Data
- miRNA Expression Profiling
- miRNA Target Gene Prediction
- Non-coding RNA Characterization


(Ѿ)

16:40 ~ 18:00

III: miRNA Sequencing Data Process
           miRNA Expression Profiling
           non-coding RNA Resources

, ӿ

 

DAY 4: Exome Sequencing and Cancer Genome Bioinformatics

          2 27()

ð

    

8:30 ~ 9:30

α׷ ġ

9:30 ~ 9:50

Exome Sequencing and Cancer Genome Bioinformatics

 

9:50 ~ 10:40

Exome Sequencing and Rare Disease
- Exome Sequencing Data
- Exome Sequencing of Rare Disease
- Variant Analysis and Annotation

賲 ڻ
(пKOBIC)

10:50 ~ 12:10

I: Trio-Exome-Sequencing Data Analysis
          Known Variant Filtering
          Detection of Disease-causing Variations
          Disease Gene Prioritization

,

12:10 ~ 13:10

    

13:10 ~ 14:00

Cancer Genome Bioinformatics
- Cancer Genome Analysis
- Identifying Genomic Rearrangement
- Gene Fusion Analysis
- Survival Analysis

ۿ
(ѾǴ)

14:10 ~ 15:30

II: Fusion Gene Analysis from RNA-seq
          Network and Survival Analysis
          Resources for Cancer Research:
          cBioPortal, COSMIC, CCLE, OncoMap

̼,

15:40 ~ 16:30

Copy Number and Genomic Rearrangement
- CNA Identification in Cancer Genome
- Copy Number Data Processing
- Genomic Rearrangement

ڻ
(ǿ)

16:40 ~ 18:00

III: Cancer Genomic Rearrangement            Identification of CNV Regions
           CNV Database

, ӿ

 

DAY 5: Translational Bioinformatics: Thousands of Public Data Analysis

          2 28()

ð

    

8:30 ~ 9:30

α׷ ġ

9:30 ~ 9:50

Translational Bioinformatics: Thousands of Public Data Analysis

 

9:50 ~ 10:40

The Cancer Genome Atlas (TCGA) Project and Cancer Genome Research
- TCGA Introduction
- TCGA Data and Scientific Findings
- Impact of TCGA and Future

¹
(ī縯Ǵ)

10:50 ~ 12:10

I: TCGA Somatic Mutation Landscape
          Find Significantly Mutated Genes
          Identify Driver Groups of Mutations

,

12:10 ~ 13:10

    

13:10 ~ 14:00

The Encyclopedia of DNA Elements (ENCODE) and Human Genome Research
- ENCODE Overview
- New Insights into the Human Genome
- Gencode project and UCSC Genome Browser

ȫ ڻ
()

14:10 ~ 15:30

II: Explore ENCODE Data at UCSC Genome Browser
          Identify Transcription Factor Binding Loci from ChIP-seq Data
          Detection of Regulatory SNPs

, ӿ

15:40 ~ 16:30

Genome-Phenome-EMR Integrative Analysis
- EMR and beyond GWAS
- Phenome-Wide Association Study (PheWAS)
- Environment-Wide Association Study (EWAS)


(Ǵ)

16:40 ~ 18:00

III: Phenotype Extraction from eMERGE Network Data
           Integrating Genetics: EMR-based Phe-WAS
           PheWAS View for Visualization
 

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