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Dong X. et. al. "Modeling gene expression using chromatin features in various cellular contexts"

Itoshi NIKAIDO
September 28, 2012

Dong X. et. al. "Modeling gene expression using chromatin features in various cellular contexts"

2012/09/29 ENCODE 勉強会

Itoshi NIKAIDO

September 28, 2012
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  1. Dong X et. al. Modeling gene expression using chromatin features

    in various cellular contexts 2012/09/29 ENCODE ษڧձ ೋ֊ಊѪ / @dritoshi #encodejp-28 ཧԽֶݚڀॴ ൃੜɾ࠶ੜՊֶ૯߹ݚڀηϯλʔ
  2. Chromatin features ͔ΒҨ఻ࢠൃݱ͕༧ଌͰ͖Δ͔? 4ͭͷ໰͍ 1. Can we reproduce the quantitative

    relationship between gene expression levels and histone modifications? 2. Does the relationship hold across different human cell lines and between different groups of genes? 3. Do the most predictive chromatin features differ depending on the expression quantification technique used? 4. How well can the chromatin features predict expression levels of RNA from different cell compartments and/or RNA extracted by different
  3. Chromatin features ͕సࣸΛ੍ޚ͢Δ Ҩ఻ࢠपลͷώετϯम০, ΫϩϚνϯߏ଄ʹΑΔస੍ࣸޚ 7 histone modifications 1 histone

    variant DNase I hypersensitivity in 7 cell Cause = Chromatin feature RNA-seq RNA-PET deepCAGE Effect = transcription Cause = Chromatin feature Effect = transcription
  4. ·ͱΊ1 chromatin features ͔Βసࣸྔ͕ྑ͘༧ଌͰ͖Δ 0. chromatin features ͷసࣸ΁ͷӨڹΛఆྔతʹධՁͰ͖ͨ ൃݱON/OFF: H3K9ac

    > H3K4me3 > DNase I > H3K4me2 ... సࣸྔ: H3K79me2 > H3K36me3 > DNase I > H3K9ac ... 1. 2ஈ֊ͷ༧ଌํ๏ΛఏҊ ON/OFFͷ෼ྨͱճؼϞσϧ
  5. ·ͱΊ2 ΄͔ʹٞ࿦͞Ε͍ͯΔ͜ͱ 1. Nucleus, Cytosol, Whole Cell ༝དྷͷRNAྔΛ༧ଌͰ͖Δ͔? Ͱ͖ΔɻCytosol >

    Whole Cell >> Nucleus 2. RNA-seq, CAGE, RNA-PETͷͲΕ͕chromatin featuresͱͷ૬ ͕ؔߴ͍͔? CAGE > RNA-PET = RNA-seq 3. ΄͔ͷࡉ๔ͷసࣸྔΛઆ໌Ͱ͖Δ͔? R = 0.8 ఔ౓Ͱ 4. CpGͱͷؔ࿈͸? High CpG ͷ΄͏͕༧ଌ͕Α͍
  6. ͜͏ߟ͑Δ Α͔ͬͨ͜ͱͱ࢒͞Εͨ՝୊ Α͔ͬͨ͜ͱ 1. ༧ଌͰ͖ͨͷ͸Α͔ͬͨͶ 2. chromatin features ͷͦΕͧΕͷॏཁੑ͕Θ͔ͬͨͷ͸Α͔ ͬͨ

    ՝୊ 1. bestbin Λ૬ؔͰऔ͍ͬͯΔͷ͸͍͍ͷ͔ͳ? 2. ౷ܭϞσϧͰ͍͍ͷ͔ͳ? Ϟϊͷಈ͖͕Θ͔Βͳ͍ 3. CAGE, RNA-seq, RNA-PET͕ൺֱͰ͖Δ΄ͲϑΣΞ? 4. Ҩ఻ࢠʹண໨ͨٞ͠࿦΋ཉ͍͠ΑͶ 5. ༧ଌ݁Ռ͔ΒసࣸΛσβΠϯͰ͖Δͷ͔?
  7. Software ෼ྨ΍ճؼͳͲ Calculation of the mean density of chromatin features

    bigWigSummary: BigWig and BigBed: enabling browsing of large distributed datasets Variable importance relaimpo: Relative importance of regressors in linear models Regression/classification randomForest: Breiman and Cutler's random forests for classification and regression Regression earth: Multivariate Adaptive Regression Spline Models