02/16/2015
MTHFR, SHMT1, SHMT2
02/17/2015
1. Library comparison
2. OKB2/AC (>=0.1) combined with OKB2/untreated (>=1), >= 2-fold (2886 transcriptid), sent to GO
Biological Process (BP): GOBP
Molecular Function (MF): GOMF
Cellular Compartment (CC): GOCC
03/28/2015
Excel 製圖時
1. On “OKB2” Filter > 3, shade them
2. On “Fold OKB2” Filter >=1.5 shade with red; Filter <=0.67 shade with blue
3. On “S1F” Filter > 3, shade them
4. On “Fold S1F” Filter >=1.5 shade with red; Filter <=0.67 shade with blue
5. On “THP1” Filter > 3, shade them
6. On “Fold THP1” Filter >=1.5 shade with red; Filter <=0.67 shade with blue
04/02/2015
感謝大家的製圖與整理! 再來要做啥? 我有幾個想法
1. Nucleosome assembly的原料, i.e. histones, 全面下降. 表示細胞若正在S phase, 再來的nucleosome assembly將有巨大變化. 是符合hypomethylation嗎?
2. 我們map出來的histone gene多在ch6, 少數幾個在ch1. 這些cluster在一起的gene family, 使要被調降的?
3. 還有沒有其它類似例子?
4. 如果ROS是元凶 (許多AC造成的上升或下降, 可被GSH reverse 回來), 機制是什麼?
5. To confirm or extend more experiments, let’s try on OKF4-hTert, NP460hTert and CGHNK2.
04/14/2015
1. Focus on nucleosome downregulation: histone cluster 1 on ch6 and histone cluster 2 on ch1.
2. Large hypo-methylation blocks: 在正常細胞時多為LAD/LOCK domain, 經過”transformation stimuli”後, 成為hypo-methyl region.
LAD: lamina-associated domain
LOCK: laroge organized chromatin lysine-modification
3. 40T/N methylation array中, delta beta >= 0.35 定義為 hypermethylated genes, delta beta <= -0.3 定義為 hypomethylated genes.
4. 40T/N methylation array中:
N_MAX-N_MIN delta beta >= 0.3 計有6830 (表示 normal tissue中, methylation差異性的存在)
T_MAX-T_MIN delta beta >= 0.3 計有10808 (表示 tumor tissue中, methylation差異性的存在)
T AVB-N AVB delta beta >= 0.3 計有702 (Illumina廠商根據其它條件 例如是否有CpG island, 再縮減成37個up, 37個down.
T AVB-N AVB delta beta >= 0.3 計有702 (Illumina廠商根據其它條件 例如是否有CpG island, 再縮減成37個up, 37個down.
5/26/2015 New
On Molas portal:
step 1: OKB2 > 1, OKB2_AC > 0, Fold >= 2, 得 3220 transcripts.
step 2: OKB2 > 0, OKB2_AC >= 2, Fold >= 2 , 得1460 transcripts.
step 3: combine step 1 and step 2, 用 “IF” 公式 , 得 unique transcrpt n = 3563.
step 4: Sort for value, < 0.1者全部replace為0.1, 計算Fold.
step 5: Sort for GeneName / OKB2值大到小 / OKB2_AC 值大到小, 用”IF” 公式過濾unique gene name.
小計: unique gene name n= 2547 (down 1873, up 674).
unique transcript / no gene name n=155.
2547 + 155 = 2702 = 1987 down, 715 up.
Among the 2547 unique genes, we will focus on
(1) chromatin organization genes
(2) cell cycle related genes
(3) TP53 related genes (TP53值較高的transcript 因未達2倍差異, 反未被挑出 )
(2) anti-oxidant genes
(3) secreted proteins