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Á¦5Â÷ Genome Data Analysis WorkshopÀ» °³ÃÖÇϸç

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Genome Data Analysis ¿÷¼¥ÀÌ ±× ´Ù¼¸ ¹ø °¿¡ Á¢¾îµì´Ï´Ù. ¿ì¸®µµ ´Ü¼øÇÑ °­Á ½Ã¸®Áî º¸´Ù ½ÇÁ¦ÀûÀ¸·Î ½ÇÇà°¡´ÉÇÑ Äڵ带 Áß½ÉÀ¸·Î ½Ç½À, finger exercise¸¦ ÇÒ ¼ö ÀÖ´Â ÇÁ·Î±×·¥À» ¸¸µé¾îº¸ÀÚ´Â ¹Ù·¥À¸·Î ½ÃÀÛÇÑÁöµµ 3³â¿¡ µÇ¾î°©´Ï´Ù. ±×°£ º¸³»ÁֽŠ¼º¿ø¿¡ ±íÀÌ °¨»çµå¸³´Ï´Ù.

Genome Data´Â ³ª³¯ÀÌ ´Ã¾î°©´Ï´Ù. 1000 Genome Project°¡ ±× Çϳª°í, TCGA (The Cancer Genome Atlas)°¡ ´Ù¸¥ÇϳªÀÔ´Ï´Ù. ±× ¿Ü¿¡µµ ´Ù¾çÇÑ »ç¾÷À» ÅëÇØ Genome Data°¡ ³Î¸® °ø°³µÇ¾î, ÀÌÁ¦´Â "À¯Àüü ¿¬±¸ÀÇ ¹ÎÁÖÈ­ ½Ã´ë" °¡ ¸¸°³Çß½À´Ï´Ù. Àú´Â ÇÐȸ µî¿¡¼­ °ø°ø¿¬È÷, ±×¸®°í ¿ë°¨ÇÏ°Ô "¾ÕÀ¸·Î 10³â Èĸé, ¾ÏÀÌ ¸¸¼ºÁúȯȭ µÉ °ÍÀÔ´Ï´Ù"¶ó°í À̾߱âÇÏ°ï ÇÕ´Ï´Ù. ÇãȲµÈ À̾߱Ⱑ ¾Æ´Ï¶ó°í »ý°¢ÇÕ´Ï´Ù. ¾ÏÀº ±Ùº»ÀûÀ¸·Î DNAÀÇ ÁúȯÀ̶ó°í ¹Þ¾Æµé¿©Áý´Ï´Ù. ´« ºÎ½Å ±â¼ú¹ßÀüÀ¸·Î DNAÀÇ ºñ¹ÐÀÌ µå·¯³ª°í ÀÖ½À´Ï´Ù. ´Ù¾çÇÑ Áø´Ü-Ä¡·á Àü·«ÀÌ ¹ßÀüÇÏ°í ÀÖ°í, ¸ÓÁö ¾Ê¾Æ »ó´ç ºÎºÐÀÇ ¾ÏÁ¾Àº, Áö±Ýó·³ ±Þ¼º ÁúȯÀÌ ¾Æ´Ñ ¸¸¼ºÁúȯÀÇ Çϳª·Î ÀÚ¸®¸Å±èÇÒ °ÍÀÓÀ» ¹Ï¾î ÀǽÉÄ¡ ¾Ê½À´Ï´Ù.

¸¹Àº ºÐµéÀÌ ¡°¸ÂÃãÀÇÇС±À» À̾߱â ÇÕ´Ï´Ù. ¸ÂÃãÀÇÇп¡ ´ëÇÑ Á¦ Á¤ÀÇ´Â °³°³ÀÎ º°·Î ´Ù¸¥ ´Ù¾çÇÑ µ¥ÀÌÅÍ¿¡ ±â¹ÝÇؼ­ °³Àκ°·Î ƯȭµÈ ÀÇÇÐÀû Àü·«À» Àû¿ëÇÏ´Â ºÐ¾ßÀÔ´Ï´Ù. ¹Ù ·Î ÀÓ»ó µ¥ÀÌÅÍ¿Í À¯Àüü µ¥ÀÌÅÍ¿¡ ±â¹ÝÇÑ µ¥ÀÌÅÍ ÀÇÇÐÀÌÁö¿ä. ±× ¾î´À ¶§º¸´Ùµµ Genomic Data¿¡ ´ëÇÑ ¿Ã¹Ù¸¥ ÀÌÇØ°¡ Áß¿äÇÑ ½Ã´ë°¡ µÇ¾ú½À´Ï´Ù. ¿¬±¸ÀÚµéÀÇ ½ÇÁúÀû ¹®Á¦ÇØ°á¿¡ µµ¿òÀÌ µÇ±â À§Çؼ­, ¼­¿ïÀÇ´ë Á¤º¸ÀÇÇнǰú ¼­¿ï´ë ½Ã½ºÅÛ ¹ÙÀÌ¿À Á¤º¸ÀÇÇÐ ±¹°¡Çٽɿ¬±¸¼¾ÅÍ¿¡¼­´Â 2013³âµµ ÇÏ°è ¹æÇÐÀ» ¸Â¾Æ Ãʺ¸ÀÚµµ Á¢±ÙÇÒ ¼ö ÀÖ´Â ½Ç¿ëÀûÀÎ À¯Àüü µ¥ÀÌÅÍ ºÐ¼®ÀÇ Àü¹ÝÀûÀÎ ±âÃÊÁö½ÄÀ» ¿¬½ÀÇÏ°í, ¿¬±¸ »Ó ¾Æ´Ï¶ó ¸ÂÃãÀÇ·á ¹× »ê¾÷¿¡ ÀÀ¿ë°¡´ÉÇÑ ³»¿ëÀ¸·Î GDA (Genome Data Analysis) ¿÷¼¥À» °³¼³Çß½À´Ï´Ù. º» ¿÷¼¥À» ÅëÇØ ½Ç¿ëÀûÀÎ À¯Àüü Á¤º¸ ºÐ¼®ÀÇ ¿ª·®À» °­È­ÇÏ´Â ±âȸ°¡ µÇ½Ã±â¸¦ ±â´ëÇÏ¸ç ¸¹Àº °ü½É°ú Âü¿©¸¦ ºÎŹµå¸³´Ï´Ù.

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  Á¦1Â÷ GDA Workshop: 2011³â 8¿ù 22ÀÏ~26ÀÏ, ¼­¿ïÀÇ´ë

 Á¦2Â÷ GDA Workshop: 2012³â 2¿ù 20ÀÏ~24ÀÏ, ¼­¿ïÀÇ´ë
  Á¦2Â÷ ¿÷¼¥¿¡¼­´Â ´ÙÀ½°ú °°Àº »õ·Î¿î ½Ç½À¸ðµâ 3°³°¡ Ãß°¡ µÇ¾ú´Ù.
  
(1) micro-RNA µ¥ÀÌÅÍ ºÐ¼®
   (2) °³ÀÎÀ¯Àüü Çؼ®: Personal Genome Interpretation
   (3) ¾ÏÀ¯Àüü/Èñ±ÍÁúȯÀ¯Àüü µ¥ÀÌÅÍ ºÐ¼®

  Á¦3Â÷ GDA Workshop: 2012³â 8¿ù 20ÀÏ~24ÀÏ, ¼­¿ïÀÇ´ë
   Á¦ 3Â÷ ¿÷¼¥¿¡¼­´Â ´ÙÀ½°ú °°Àº 2°³ÀÇ ½Ç½À¸ðµâÀÌ Ãß°¡µÇ¾ú´Ù.
 
 (1) Family-based ¿¢¼Ø½ÃÄö½Ì ºÐ¼®
   (2) TCGA (The Cancer Genome Atlas) µ¥ÀÌÅÍ ºÐ¼®

  Á¦4Â÷ GDA Workshop: 2013³â 2¿ù 18ÀÏ~22ÀÏ, ¼­¿ïÀÇ´ë
    4Â÷ ¿÷¼¥¿¡¼­´Â ´ÙÀ½°ú °°Àº 2°³ÀÇ ½Ç½À¸ðµâÀÌ Ãß°¡µÇ¾ú´Ù.
 
 (1) eQTL µ¥ÀÌÅÍ ºÐ¼®
   (2)
PheWAS & EWAS µ¥ÀÌÅÍ ºÐ¼®

  º»5Â÷ ¿÷¼¥¿¡¼­´Â ´ÙÀ½°ú °°Àº 3°³ÀÇ ½Ç½À¸ðµâÀÌ Ãß°¡µÉ ¿¹Á¤ÀÌ´Ù.
 
 (1) ½ÃÄö½º ·¹º§ Àü»çü ºÐ¼®: Isoforms, Alternative Splicing, RNA-editing, and Fusion Gene
   (2)
°³ÀÎÀ¯Àüü Çؼ®À» À§ÇÑ Áö½Ä/µ¥ÀÌÅͱâ¹Ý ÀÚ¿ø ¼Ò°³¿Í À¯ÀüÀû À§Çè ¿¹Ãø ºÐ¼®
   (3)
Post-GWAS: EMR µ¥ÀÌÅÍ¿Í Áúº´ ¿¬°ü ºÐ¼®

À¯Àüü µ¥ÀÌÅÍ ºÐ¼®
½Ç½À¼­ "À¯Àüü µ¥ÀÌÅÍ ºÐ¼®" Ãâ°£

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        °­ÁÂÀÏÁ¤Àº ÁÖÃÖÃøÀÇ »çÁ¤¿¡ µû¶ó º¯°æµÉ ¼ö ÀÖ½À´Ï´Ù.

DAY 1: Advanced Microarray Data Analysis

           8¿ù 26ÀÏ(¿ù)

½Ã°£

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8:30 ~ 9:30

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:30 ~ 9:50

Advanced Microarray Data Analysis

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9:50 ~ 10:40

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

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(¼­¿ïÀÇ´ë)

10:50 ~ 12:10

½Ç ½À I: Bioconductor
          t-test, SAM, ANOVA, FDR

À̼ö¿¬s, ¹é¼ö¿¬

12:10 ~ 13:10

  Áß  ½Ä

13:10 ~ 14:00

Classification and Clustering
- Classification Analysis
- Clustering Analysis
- Evaluation and Validation

¼Õ°æ¾Æ ±³¼ö
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14:10 ~ 15:30

½Ç ½À II: KNN, SOM, HC, PCA
           LDA, DTs, SVMs

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15:40 ~ 16:30

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

Á¶¼º¹ü ¹Ú»ç
(±¹¸³º¸°Ç¿¬±¸¿ø)

16:40 ~ 18:00

½Ç ½À III: Gene Set Enrichment Analysis
           Cox-PH, Log Rank Test
           David, ArrayXPath

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DAY 2: Next Generation Sequencing & Personal Genome Data Analysis

          8¿ù 27ÀÏ(È­)

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8:30 ~ 9:30

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:30 ~ 9:50

Next Generation Sequencing & Personal Genome Data Analysis

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9:50 ~ 10:40

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

±èÁöÈÆ ¹Ú»ç
(¸¶Å©·ÎÁ¨)

10:50 ~ 12:10

½Ç ½À I: NGS Data Processing
         NGS Data Format Converting
         NGS Visualization Tools

¼­Èñ¿ø, ÀÓÀçÇö

12:10 ~ 13:10

  Áß  ½Ä

13:10 ~ 14:00

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

À̼ö¿¬ ¹Ú»ç
(¼­¿ïÀÇ´ë)

14:10 ~ 15:30

½Ç ½À II: Exome Sequencing Alignment
          SNP and Indel Identification
          Variation Filtering

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15:40 ~ 16:30

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

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16:40 ~ 18:00

½Ç ½À III: SNP Prioritization
            Genetic Risk Prediction methods
            Resources for Personal Genome Interpretation
            (dbGAP, PheGeni, SNPedia, PhenoDB)

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DAY 3: RNA-seq Data Analysis

          8¿ù 28ÀÏ(¼ö)

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8:30 ~ 9:30

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:30 ~ 9:50

RNA-seq Data Analysis

 ±èÁÖÇÑ ±³¼ö

9:50 ~ 10:40

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

Á¤Á¦±Õ ¹Ú»ç
(»ï¼ºÀ¯Àüü¿¬±¸¼Ò)

10:50 ~ 12:10

½Ç ½À I: Read alignment with TopHat,
          Expression Quantification with Cufflinks
          RNA-Seq Gene Expression Analysis

ÀÓÀçÇö, ¼­Èñ¿ø

12:10 ~ 13:10

  Áß  ½Ä

13:10 ~ 14:00

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


14:10 ~ 15:30

½Ç ½À II: Alternative Splicing Identification
           RNA Editing Site Annotation
           Fusion Gene Identification

À̼ö¿¬s, ÀÓÀçÇö

15:40 ~ 16:30

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

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(ÇѾç´ë)

16:40 ~ 18:00

½Ç ½À III: miRNA Sequencing Data Process
           miRNA Expression Profiling
           non-coding RNA Resources

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DAY 4: Exome Sequencing and Cancer Genome Bioinformatics

          8¿ù 29ÀÏ(¸ñ)

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8:30 ~ 9:30

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:30 ~ 9:50

Exome Sequencing and Cancer Genome Bioinformatics

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9:50 ~ 10:40

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

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(»ý¸í°øÇבּ¸¿øKOBIC)

10:50 ~ 12:10

½Ç ½À I: Trio-Exome-Sequencing Data Analysis
          Known Variant Filtering
          Detection of Disease-causing Variations
          (SIFT, PolyPhen2, VAAST)
          Disease Gene Prioritization

¼­Èñ¿ø, ±è±âÅÂ

12:10 ~ 13:10

  Áß  ½Ä

13:10 ~ 14:00

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

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(ÇѾçÀÇ´ë)

14:10 ~ 15:30

½Ç ½À II: TCGA Data Analysis
          Cancer Genome Analysis (Multiple Plotting,
          Network Analysis, Visualization, Mutation,
          Methylation, Survival Analysis)
          Cancer Panel, OncoMap

ÀÓÀçÇö, °íÀμ®

15:40 ~ 16:30

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

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(±¹¸³º¸°Ç¿¬±¸¿ø)

16:40 ~ 18:00

½Ç ½À III: Cancer Genomic Rearrangement            Identification of CNV Regions
           CNV Database

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DAY 5: GWAS and Post-GWAS, eQTL, PheWAS and EWAS Data Analysis

          8¿ù 30ÀÏ(±Ý)

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8:30 ~ 9:30

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9:30 ~ 9:50

GWAS and Post-GWAS, eQTL, PheWAS and EWAS Data Analysis

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9:50 ~ 10:40

SNPs Data Analysis and Genome Wide Association Study
- Linkage Disequilibrium Analysis
- Genotype & Haplotype
- Rare Variant Analysis
- Regression-based Testing

ÀÌ俵 ±³¼ö
(¼þ½Ç´ë)

10:50 ~ 12:10

½Ç ½À I: Haplotype Estimation, LD Blocking
          GWAS Catalog
          GWAS test with PLINK software

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12:10 ~ 13:10

  Áß  ½Ä

13:10 ~ 14:00

Post-GWAS and eQTL Data Analysis
- Post-GWAS: Connection to GWAS
- Runs of Homozygosity (ROH)
- Cis- and trans-expression QTL
- eQTL Hotspots

¹ÚÀÌ¿µ ¹Ú»ç
(¼­¿ïÀÇ´ë)

14:10 ~ 15:30

½Ç ½À II: Idenfity eQTL hotspots
           eQTL Resources

ÀÓÀçÇö, ±è±âÅÂ

15:40 ~ 16:30

PheWAS, EWAS and Electronic Medical Records Data Analysis
- EMR and beyond GWAS
- Phenome-Wide Association Study (PheWAS)
- Environment-Wide Association Study (EWAS)

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16:40 ~ 18:00

½Ç ½À III: EMR Data Structure and Extraction
           SNPs and Disease Associations from EMR
           EMR Based Phe-WAS
 

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