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ÁÖ °ü ¼­¿ïÀÇ´ë Á¤º¸ÀÇÇнÇ, ½Ã½ºÅÛ ¹ÙÀÌ¿À Á¤º¸ÀÇÇÐ ¿¬±¸¼¾ÅÍ (SBI-NCRC)
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Genome Data Analysis WorkshopÀ» °³ÃÖÇϸç

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   ¹ÙÀÌ¿À-À¯Àüü Á¤º¸ÀÇÇÐÀÇ Åµ¿Àº 1980³â´ë·Î »ý°¢µË´Ï´Ù. À¯Àüü Á¤º¸ ºÐ¼®ÀÇ ±âº»ÀûÀÎ °³³äµéÀÌ Çü¼ºµÈ ½Ã±â¿´½À´Ï´Ù. 2003³â ¿Ï¼ºµÈ Àΰ£À¯Àüü»ç¾÷Àº °¡Àå Áß¿äÇÑ ¼º°ú¿´´Ù°í ÇÒ ¼ö ÀÖ½À´Ï´Ù. 1995³â DNA ¸¶ÀÌÅ©·Î¾î·¹ÀÌ ±â¼úÀÇ °³¹ßÀº BT¿Í ITÀÇ À¶ÇÕÀÌ ÁøÁ¤À¸·Î ÀÇÇп¡ Àû¿ë°¡´ÉÇÒ ¼ö ÀÖ´Ù´Â °ÍÀ» ÀÔÁõÇϱ⠽ÃÀÛÇß½À´Ï´Ù. ¸¾¸¶ÇÁ¸°Æ®(MamaPrint)³ª Ƽ½´¾îºê¿À¸®Áø(Tissue of Origin)Àº °³Àκ° ¸ÂÃãÀÇÇÐÀ» °¡½ÃÈ­Çß°í, ¿ÍÆĸ° ¿¬±¸·Î ´ëÇ¥µÇ´Â ¾à¹°À¯Àüü ¸ÂÃãÀÇÇÐÀº ¸ðµç ¾à¹°Ä¡·áÀÇ Æз¯´ÙÀÓÀ» ±Ùº»ÀûÀ¸·Î º¯È­½ÃÅ°°í ÀÖ½À´Ï´Ù.

   ±× ±â°£ °¡Àå ³î¶ø°Ô ¹ß´ÞÇÑ ±â¼úÀº ¿ª½Ã Â÷¼¼´ë¿°±â¼­¿­°áÁ¤(NGS, Next Generation Sequencing)ÀÔ´Ï´Ù. ¿¬±¸ÀÚµéÀº ±×°£ÀÇ Á¾º° ºÐ¼®À̳ª Áý´Ü¿¬±¸ÀÇ ÇѰ踦 ¹þ¾î³ª ÁøÁ¤À¸·Î °³ÀÎÀÇ »ý¸íÁ¤º¸¸¦ ³¹³¹ÀÌ ¹àÈ÷°í À̸¦ ÅëÇØ ÀÇÇÐÀÇ »õ ÁöÆòÀ» ¿­ ¼ö ÀÖ´Ù´Â È®½ÅÀ» °®°Ô µÇ¾ú½À´Ï´Ù. ¾Ï À¯Àüü´Â ¸ðµç ¾ÏÀº ¼­·Î ´Ù¸£´Ù´Â °ÍÀ» Áõ¸íÇÏ¸ç ¸ÂÃãÄ¡·á¸¦ Ã˹߽ÃÄ×°í, Ç»Àü À¯ÀüÀÚ¿Í °°Àº ¾Ï ƯÀÌ ¹°ÁúÀÇ ¹ß°ßÀº °ÅÀÇ ºÎÀÛ¿ëÀÌ ¾ø´Â ¾Ï Ä¡·áÁ¦¸¦ °¡´ÉÇÏ°Ô ÇÏ¿© ÀÌÁ¦ ¾ÏÀº ¸¸¼ºÁúȯ¿¡ ºÒ°úÇÒ °ÍÀ̶ó°í ¿¹»óµË´Ï´Ù.

   °ú°Å ÇϳªÇϳªÀÇ Á¤º¸¸¦ ¿¬±¸ÀÚÀÇ ¼Õ¶¡À¸·Î ¾ò´ø ½Ã´ë¿¡´Â ÀÚ·áȹµæÀÌ °¡Àå ¾î·Æ°í °¡Ä¡ÀÖ´Â ÀÛ¾÷À̾úÁö¸¸ ¿À´Ã³¯ÀÇ ´ë·® »ý¸íÁ¤º¸ ȹµæ±â¼úÀÇ ¹ß´ÞÀº ¿¬±¸ÀÇ º´¸ñÀ» ÀÚ·áȹµæ¿¡¼­ ÀÚ·áó¸® ¹× Çؼ®À¸·Î ¿Å°Ü ³õ¾Ò½À´Ï´Ù. ÃÖ±Ù ºÐ¼®¿¡ ÀÇÇÏ¸é ´ëÇü À¯ÀüüÁ¤º¸ ¼¾ÅÍ¿¡¼­ °³¹ßµÇ¾î¿Â ºÐ¼®¹æ¹ý·Ð º¸´Ù´Â ½ÇÁ¦ ÇöÀå Àû¿ë¿¡ ÇÊ¿äÇÑ °³º° ºÐ¼®¹æ¹ý·ÐÀÇ Çʿ伺ÀÌ ½Ã±ÞÈ÷ ´ëµÎµÇ°í ÀÖ´Ù°í ÇÕ´Ï´Ù. ÀÌ°ÍÀº ±×°£ÀÇ ¿¬±¸¼º°úµéÀÌ ½ÇÁ¦ ¸ÂÃãÀÇÇÐÀÇ ÇöÀå¿¡¼­ ³¹³¹ÀÌ ½ÇÇöµÇ´Â ¸ð½ÀÀ» ÀǹÌÇÕ´Ï´Ù. 

   ´ëºÎºÐÀÇ »ý¸í°úÇÐÀÚ³ª ÀÇÇÐÀÚ¿¡°Ô ´ë±Ô¸ð ¹ÙÀÌ¿À À¯Àüü Á¤º¸µéÀº ´Ù·ç±âµµ Èûµé°í, Á¤º¸Ã³¸®³ª ºÐ¼®, ³ª¾Æ°¡ ½ÇÁúÀû Çؼ®Àº ³Ñ±â Èûµç À庮ÀÌ µÇ¾ú½À´Ï´Ù. ´õ±¸³ª ¼­¿­Á¤º¸, ¹ßÇöÁ¤º¸, ¿¡ÇÇÁö³ð Á¤º¸, Á¤º¸Ç¥ÁØ ¹× ºÐÀÚ»ý¹°ÇÐ µ¥ÀÌÅͺ£À̽º¿Í Æнº¿þÀÌ, ¿ÂÅç·ÎÁö µî ÇÊ¿äÇÑ ÁÖÁ¦ÀÇ ¸ñ·ÏÀº °è¼Ó ±æ¾îÁ® °¡°í ÀÖ½À´Ï´Ù. ¼­¿ïÀÇ´ë Á¤º¸ÀÇÇнǰú ¼­¿ï´ë ½Ã½ºÅÛ ¹ÙÀÌ¿À Á¤º¸ÀÇÇÐ ±¹°¡Çٽɿ¬±¸¼¾ÅÍ¿¡¼­´Â 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.

2011³â 6¿ù, ¼­¿ïÀÇ´ë Á¤º¸ÀÇÇнÇÀå  ±è ÁÖ ÇÑ
 

 

°­Á ÀÏÁ¤   

DAY 1: Advanced Microarray Data Analysis

          °­ÁÂ I (8¿ù 22/29ÀÏ):

½Ã°£

  ÁÖ  Á¦

°­ »ç

8:30 ~ 9:20

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:20 ~ 9:40

Advanced Microarray Data Analysis

 ±èÁÖÇÑ ±³¼ö

9:40 ~ 10:30

Gene Expression Analysis
- Differential Expression Analysis
- Clustering Analysis
- Classification Analysis

±èÁÖÇÑ ±³¼ö
(¼­¿ïÀÇ´ë)

10:40 ~ 12:00

½Ç ½À I: t-test, SAM, ANOVA, FDR
           KNN, SOM, Graph-based Algorithms
           LDA, DTs, SVMs

³ª¿µÁö, À̼ö¿¬

12:00 ~ 13:00

  Áß  ½Ä

13:00 ~ 13:50

Gene Ontology & Pathway Analysis
- GO Enrichment Analysis
- Pathway Enrichment Analysis
- Biological Interpretation

ÃßÀμ± ¹Ú»ç
(»ý¸í°øÇבּ¸¿ø
KOBIC)

14:00 ~ 15:20

½Ç ½À II: Biological interpretation of gene
            expression data with BioLattice, David

Á¤ÈñÁØ, ¹ÚÀ¯¶û

15:30 ~ 16:20

Gene-set Approaches & Prognostic Subgroup Prediction
- Gene Set Database
- Gene Set Enrichment Analysis
- Prognostic Subgroup Prediction

Á¶¼º¹ü ¹Ú»ç
(±¹¸³º¸°Ç¿¬±¸¿ø)

16:30 ~ 17:50

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

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DAY 2: Next Generation Sequencing, Useful Applications

          °­ÁÂ II (8¿ù 23/30ÀÏ):

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  ÁÖ  Á¦

°­ »ç

8:30 ~ 9:20

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:20 ~ 9:40

Next Generation Sequencing, Useful Applications

 ±èÁÖÇÑ ±³¼ö

9:40 ~ 10:30

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

ÀÌȯ¼® ¹Ú»ç
(¸¶Å©·ÎÁ¨)

10:40 ~ 12:00

½Ç ½À I: NGS Data Processing
           NGS Sequence Alignment
           NGS Visualization Tools

³ª¿µÁö, À±¼±¹Î

12:00 ~ 13:00

  Áß  ½Ä

13:00 ~ 13:50

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

ÀÌÁ¾Àº ¹Ú»ç
(DNA Link)

14:00 ~ 15:20

½Ç ½À II: SNP and Indel Identification
            Variant Analysis and Annotation

¹ÚÂùÈñ, À±ÁØÈñ

15:30 ~ 16:20

RNA-Seq Data Analysis
- Novel Transcript Discovery
- Alternative Splicing Identification
- Noncoding RNAs & NATs Identification
- Differentially Expressed Genes Identification

Á¤Á¦±Õ ¹Ú»ç
(¼­¿ï´ë ÀÇ°ú´ëÇÐ)

16:30 ~ 17:50

½Ç ½À III: RNA-Seq Gene Expression Analysis

À̼ö¿¬, Á¤Á¦±Õ

 

DAY 3: Network Biology, Sequence, Pathway & Ontology Informatics

          °­ÁÂ III (8¿ù 24ÀÏ / 9¿ù 5ÀÏ)

½Ã°£

  ÁÖ  Á¦

°­ »ç

8:30 ~ 9:20

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:20 ~ 9:40

Network Biology, Sequence, Pathway & Ontology Informatics

 ±èÁÖÇÑ ±³¼ö

9:40 ~ 10:30

Motif and Regulatory Sequence Analysis
- Motif Analysis
- miRNA Sequence Analysis
- Genome Sequence Analysis, Genome Browser

ÃÖ¼±½É ±³¼ö
(°­¿ø´ë)

10:40 ~ 12:00

½Ç ½À I: TF and miRNA Target Prediction
            Phylogenetic Analysis (ClustalW &
            TreeView)
            UCSC Genome Browser

Á¤ÈñÁØ, Á¶¿ë·¡

12:00 ~ 13:00

  Áß  ½Ä

13:00 ~ 13:50

Molecular Pathway & Gene Ontology
- Pathway Database and Tools
- Gene Ontology Database and Tools
- Biological Literature and Text Mining

±èÁöÈÆ ¹Ú»ç
(¼­¿ïÀÇ´ë)

14:00 ~ 15:20

½Ç ½À II: Pathway, Gene Ontology Analysis
            Biological Text Mining

¹ÚÀ¯¶û, ±èÁöÈÆ

15:30 ~ 16:20

Biological Network Analysis
- Characteristics of Biological Network
- Protein-protein Interaction Network Analysis
- Regulatory Network Analysis

À̱⿵ ±³¼ö
(¾ÆÁÖ´ë)

16:30 ~ 17:50

½Ç ½À III: Gene Regulatory Networks
             Biological Network Properties
             Network Visualization

ÇÑÇö¿í, À±¼±¹Î

 

DAY 4: SNPs, GWAS & CNVs: Informatics for Variations

          °­ÁÂ IV (8¿ù 25/31ÀÏ):

½Ã°£

  ÁÖ  Á¦

°­ »ç

8:30 ~ 9:20

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:20 ~ 9:40

SNPs, GWAS & CNVs: Informatics for Variations

 ±èÁÖÇÑ ±³¼ö

9:40 ~ 10:30

SNP Data Analysis
- Linkage Disequilibrium Analysis
- Haplotype Estimation
- LD Blocking, Tagging SNPs Selection

¹ÚÁö¿Ï ±³¼ö
(ÇѸ²´ë)

10:40 ~ 12:00

½Ç ½À I: dbSNP Database
           SNP Detection, PharmGKB

±èµµ±Õ, À±ÁØÈñ

12:00 ~ 13:00

  Áß  ½Ä

13:00 ~ 13:50

GWAS Data Analysis
- Genotype & Haplotype
- Rare Variant Analysis
- Runs of Homozygosity (ROH)
- Regression-based Testing

±è»ó¼ö ±³¼ö
(¼þ½Ç´ë)

14:00 ~ 15:20

½Ç ½À II: GWAS Data Processing
GWAS test with PLINK software

¹ÚÂùÈñ, Á¶¿ë·¡

15:30 ~ 16:20

CNV Data Analysis
- CNV in Diseases
- CNV Database / Data Processing
- Copy Number Detection

±èºÀÁ¶ ¹Ú»ç
(±¹¸³º¸°Ç¿¬±¸¿ø)

16:30 ~ 17:50

½Ç ½À III: Identification of CNV Regions
             CNV Association Testing

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DAY 5: Metagenome & Epigenome, Basic Data Analysis

          °­ÁÂ V (8¿ù 26ÀÏ)

½Ã°£

  ÁÖ  Á¦

°­ »ç

8:30 ~ 9:20

µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡

9:20 ~ 9:40

Metagenome & Epigenome, Basic Data Analysis

 ±èÁÖÇÑ ±³¼ö

9:40 ~ 10:30

Metagenome Data Analysis
- Whole Metagenome Sequencing
- Comprehensive Analyses of Metagenomics
   Data
- Diversity Assessment of Biological    Communities
- Functional Analysis of Metagenome

õÁ¾½Ä ±³¼ö
(¼­¿ï´ë)

10:40 ~ 12:00

½Ç ½À I: Data Management and Analysis
           for Metagenomes

ÇÑÇö¿í, À̼ö¿¬

12:00 ~ 13:00

  Áß  ½Ä

13:00 ~ 13:50

Epigenome
- Epigenetic Mechanisms
- Epigenetics in Diseases
- Technologies for Epigenetic Research
- Applications in Epigenome

À±È«´ö ±³¼ö
(¼­¿ïÀÇ´ë)

14:00 ~ 15:20

½Ç ½À II: Epigenome Databases
            Epigenome Tools

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15:30 ~ 16:20

Epigenome Data Analysis
- Computational Epigenetics
- DNA methylation Analysis
- Histone Modification Analysis
- Discovery of Epigenetic Biomarkers

³ëÅ¿µ ±³¼ö
(Æ÷Ç×°ø´ë)

16:30 ~ 17:50

½Ç ½À III: Visualization of DNA Methylation Data
             Identification of Methylated Genes

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