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Á¦5Â÷ Genome Data Analysis
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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 ¸¸¼ºÁúÈ¯È 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 Æ¯ÈµÈ 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.
2013³â 6¿ù, ¼¿ï´ë ½Ã½ºÅÛ ¹ÙÀÌ¿À Á¤º¸ÀÇÇÐ ¿¬±¸¼¾ÅÍÀå ±è ÁÖ ÇÑ
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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®
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½Ç½À¼ "À¯Àüü µ¥ÀÌÅÍ ºÐ¼®" Ãâ°£
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DAY 1: Advanced Microarray Data Analysis
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8¿ù 26ÀÏ(¿ù)
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½Ã°£
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ÁÖ Á¦
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° »ç
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8:30 ~ 9:30
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µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡ |
9:30 ~ 9:50
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Advanced Microarray Data Analysis |
±èÁÖÇÑ ±³¼ö
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9:50 ~ 10:40
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Gene Expression Analysis
- Normalization
- Differential Expression Analysis
- Classification Analysis
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±èÁÖÇÑ ±³¼ö
(¼¿ïÀÇ´ë)
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10:50 ~ 12:10
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½Ç ½À I: Bioconductor
t-test, SAM, ANOVA, FDR
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À̼ö¿¬s, ¹é¼ö¿¬
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12:10 ~ 13:10
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Áß ½Ä
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13:10 ~ 14:00
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Classification and Clustering
- Classification Analysis
- Clustering Analysis
- Evaluation and Validation
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¼Õ°æ¾Æ ±³¼ö
(¾ÆÁÖ´ë)
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14:10 ~ 15:30
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½Ç ½À II: KNN, SOM, HC, PCA
LDA, DTs, SVMs
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À̼ö¿¬, ¼Èñ¿ø
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15:40 ~ 16:30
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Gene-set Approaches & Prognostic Subgroup Prediction
- Gene Ontology & Pathway Analysis
- Gene Set Enrichment Analysis
- Prognostic Subgroup Prediction
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Á¶¼º¹ü ¹Ú»ç
(±¹¸³º¸°Ç¿¬±¸¿ø)
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16:40 ~ 18:00
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½Ç ½À III: Gene Set Enrichment Analysis
Cox-PH, Log Rank Test
David, ArrayXPath
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±è±âÅÂ, ¹é¼ö¿¬
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DAY 2: Next Generation Sequencing & Personal Genome Data Analysis
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8¿ù 27ÀÏ(È)
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½Ã°£
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ÁÖ Á¦
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° »ç
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8:30 ~ 9:30
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µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡ |
9:30 ~ 9:50
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Next Generation Sequencing & Personal Genome Data Analysis
|
±èÁÖÇÑ ±³¼ö
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9:50 ~ 10:40
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NGS Platforms and Applications
- Current NGS Platforms
- NGS Data Formats
- NGS Data Analysis Technologies
- NGS Applications
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±èÁöÈÆ ¹Ú»ç
(¸¶Å©·ÎÁ¨)
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10:50 ~ 12:10
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½Ç ½À I: NGS Data Processing
NGS Data Format Converting
NGS Visualization Tools
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¼Èñ¿ø, ÀÓÀçÇö
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12:10 ~ 13:10
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Áß ½Ä
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13:10 ~ 14:00
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NGS Data Analysis
- Sequence Alignment Algorithms
- Whole Genome and Exome Data Analysis
- Variation Detection and Reference Genome
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À̼ö¿¬ ¹Ú»ç
(¼¿ïÀÇ´ë)
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14:10 ~ 15:30
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½Ç ½À II: Exome Sequencing Alignment
SNP and Indel Identification
Variation Filtering
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¹ÚÂùÈñ, ¼Èñ¿ø
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15:40 ~ 16:30
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Personal Genome Interpretation
- Phenotype Annotation
- Genetic Risk Prediction
- Healthcare Application
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±èÁÖÇÑ ±³¼ö
(¼¿ïÀÇ´ë)
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16:40 ~ 18:00
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½Ç ½À III: SNP Prioritization
Genetic Risk Prediction methods
Resources for Personal Genome Interpretation
(dbGAP, PheGeni, SNPedia, PhenoDB)
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À̼ö¿¬s, ¹é¼ö¿¬
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DAY 3: RNA-seq Data Analysis
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8¿ù 28ÀÏ(¼ö)
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½Ã°£
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ÁÖ Á¦
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° »ç
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8:30 ~ 9:30
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µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡ |
9:30 ~ 9:50
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RNA-seq Data Analysis
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±èÁÖÇÑ ±³¼ö
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9:50 ~ 10:40
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RNA-Seq Expression Profile Analysis
- Read Alignment Methods
- Expression Quantification Strategy
- Differentially Expressed Genes Identification
- Expression Profile Analysis
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Á¤Á¦±Õ ¹Ú»ç
(»ï¼ºÀ¯Àüü¿¬±¸¼Ò)
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10:50 ~ 12:10
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½Ç ½À I: Read alignment with TopHat,
Expression Quantification with Cufflinks
RNA-Seq Gene Expression Analysis
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ÀÓÀçÇö, ¼Èñ¿ø
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12:10 ~ 13:10
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Áß ½Ä
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13:10 ~ 14:00
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Sequence-level Transcriptome Analysis
- Novel Transcript Discovery
- Alternative Splicing Identification
- RNA-editing Analysis
- New/Fusion Gene Identification
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14:10 ~ 15:30
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½Ç ½À II: Alternative Splicing Identification
RNA Editing Site Annotation
Fusion Gene Identification
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À̼ö¿¬s, ÀÓÀçÇö
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15:40 ~ 16:30
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Non-coding RNAs in RNA-Seq Data
- miRNA Expression Profiling
- miRNA Target Gene Prediction
- Non-coding RNA Characterization
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³²Áø¿ì ±³¼ö
(ÇѾç´ë)
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16:40 ~ 18:00
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½Ç ½À III: miRNA Sequencing Data Process
miRNA Expression Profiling
non-coding RNA Resources
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¼Èñ¿ø, ÀÓ¿µ±Õ
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DAY 4: Exome Sequencing and Cancer Genome Bioinformatics
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8¿ù 29ÀÏ(¸ñ)
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½Ã°£
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ÁÖ Á¦
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° »ç
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8:30 ~ 9:30
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µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡ |
9:30 ~ 9:50
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Exome Sequencing and Cancer Genome Bioinformatics
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±èÁÖÇÑ ±³¼ö
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9:50 ~ 10:40
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Exome Sequencing and Rare Disease
- Exome Sequencing Data
- Exome Sequencing of Rare Disease
- Variant Analysis and Annotation
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±è³²½Å ¹Ú»ç
(»ý¸í°øÇבּ¸¿øKOBIC)
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10:50 ~ 12:10
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½Ç ½À I: Trio-Exome-Sequencing Data Analysis
Known Variant Filtering
Detection of Disease-causing Variations
(SIFT, PolyPhen2, VAAST)
Disease Gene Prioritization
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¼Èñ¿ø, ±è±âÅÂ
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12:10 ~ 13:10
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Áß ½Ä
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13:10 ~ 14:00
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Cancer Genome Bioinformatics
- Cancer Genome Analysis
- Identifying Genomic Rearrangement
- Gene Fusion Analysis
- Survival Analysis
|
¼Û¿µ¼ö ±³¼ö
(ÇѾçÀÇ´ë)
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14:10 ~ 15:30
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½Ç ½À II: TCGA Data Analysis
Cancer Genome Analysis (Multiple Plotting,
Network Analysis, Visualization, Mutation,
Methylation, Survival Analysis)
Cancer Panel, OncoMap
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ÀÓÀçÇö, °íÀμ®
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15:40 ~ 16:30
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Copy Number and Genomic Rearrangement
- CNA Identification in Cancer Genome
- Copy Number Data Processing
- Genomic Rearrangement
|
±èºÀÁ¶ ¹Ú»ç
(±¹¸³º¸°Ç¿¬±¸¿ø)
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16:40 ~ 18:00
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½Ç ½À III: Cancer Genomic Rearrangement
Identification of CNV Regions
CNV Database
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ÀÓÀçÇö, ÀÓ¿µ±Õ
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DAY 5: GWAS and Post-GWAS, eQTL, PheWAS and EWAS Data Analysis
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8¿ù 30ÀÏ(±Ý)
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½Ã°£
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ÁÖ Á¦
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8:30 ~ 9:30
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µî·Ï ¹× »çÀü ÇÁ·Î±×·¥ ¼³Ä¡ |
9:30 ~ 9:50
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GWAS and Post-GWAS, eQTL, PheWAS and EWAS Data Analysis
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±èÁÖÇÑ ±³¼ö
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9:50 ~ 10:40
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SNPs Data Analysis and Genome Wide Association Study
- Linkage Disequilibrium Analysis
- Genotype & Haplotype
- Rare Variant Analysis
- Regression-based Testing
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ÀÌ俵 ±³¼ö
(¼þ½Ç´ë)
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10:50 ~ 12:10
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½Ç ½À I: Haplotype Estimation, LD Blocking
GWAS Catalog
GWAS test with PLINK software
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ÀÓ¿µ±Õ, ¹ÚÂùÈñ
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12:10 ~ 13:10
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Áß ½Ä
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13:10 ~ 14:00
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Post-GWAS and eQTL Data Analysis
- Post-GWAS: Connection to GWAS
- Runs of Homozygosity (ROH)
- Cis- and trans-expression QTL
- eQTL Hotspots
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¹ÚÀÌ¿µ ¹Ú»ç
(¼¿ïÀÇ´ë)
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14:10 ~ 15:30
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½Ç ½À II: Idenfity eQTL hotspots
eQTL Resources
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ÀÓÀçÇö, ±è±âÅÂ
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15:40 ~ 16:30
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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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±èÁÖÇÑ ±³¼ö
(¼¿ïÀÇ´ë)
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16:40 ~ 18:00
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½Ç ½À III: EMR Data Structure and Extraction
SNPs and Disease Associations from EMR
EMR Based Phe-WAS
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