Showing posts with label ELN關站. Show all posts
Showing posts with label ELN關站. Show all posts

Feb 5, 2023

Blogger」的匯出格式

 Blogger」的匯出格式

Feb 4, 2023

網站整合


Blogspots/Bloggers

1. Move "sflin3" to "nhrinicr" (ourthoughts.sflin.net)


關版Google Groups:

1. SflinLab2020. 庚子夏至0621開版(實驗室同仁及合作夥伴限定) (move 12 posts to here)

2.treasureCollection (move 9 posts to here)


關版 舊的協作平台Classic Google Sites , remove access from

sflinlab-internal 1 只有連結 "ExpLog_2020_04"


All set.  

Nov 10, 2015

LabLog-SFL

LogIDTitle Initial Date
AF001Codebook for GA341 (= YJC AE538 = YMU RNA-seq on OKB2/S1F/THP1)SFL10/21/15
AF002Experimental sheme for AE592 (YMU RNA-seq GA416 and GA417)SFL10/23/15
AF003Codebook for GA417SFL11/3/15
AF004Codebook for GA416SFL11/4/15
AF005Codebook for Po-TingSFL11/5/15
AF006Codebook for tidy 40T/N raw data (2015_1110_11SVA_40TN_raw = 40TNRaw)SFL11/10/15
AF007
SFL

Sep 17, 2015

LabLog-柏廷篇

LogIDTitle Initial Date
AE801Compare Serum folate concn in OPMD patient and summary MAF in SNPs of folate-mediated one carbon metabolism PTL2015/11/9
AE802Calculate and compare the MAF of rs1801133 and rs1801131 from samplePTL2015/11/13
AE803Calculate and compare the MAF of rs2274976, rs1805087, rs1801394 and rs1979277 from samplePTL2015/11/17
AE804PTL
AE805PTL

修改完畢

老師 我已經修改完畢了

AE801 result














•FTEST=0.039 < 0.05      Variance between control and OPMD
•P=0.067 > 0.05               No significant difference  




















Jun 9, 2015

IRB 問與答 2014年題庫

IRB 問與答 2014年題庫

 
Su-Fang Lin <sflin1@gmail.com> Jun 5 Reply to 張書銘, 陳雅雯, 江士昇, 夏興國 
Dear All,
我PP計畫的IRB審查回來了, 兩個reviewer都有一點問題. 總評皆為「修正後推薦」。想請問大家先前有沒有碰到類似的問題, 其正解為何? 先謝謝大家的幫忙!  之後如果大家不介意的話, 我們是不是把各自的問題集合在一起、弄成一本題庫? 具IRB同仁說 送出去的審委不一樣、同樣的申請件, 碰到的問題都不一樣。  Thanks again!  Su-Fang (ext 35107)   (餘刪, 見下6/8回覆)

Ya-Wen Chen Jun 5 Reply to me, 張書銘, 江士昇, 夏興國 
以下是我遇到的問題及回答, 素芳的問題與我的問題有相似, 重點是(簡易審查核對表)提及使用合法生物資料庫..
簡易審查核對表我更改成接續前階段研究之後續資料分析(湘如建議).
 

Jun 4, 2015

EBV and NGS

 
3552_2012_12_Whole genome sequencing of the Akata and Mutu Epstein-Barr virus (EBV) strains.pdf
3577_2011_11_Direct sequencing and characterization of a clinical isolate of Epstein-Barr virus from nasopharyngeal carcinoma tissue by using next-generation sequencing technology
3964_2014_02_Integrated mRNA and microRNA transcriptome sequencing characterizes sequence variants and mRNA–microRNA regulatory network in nasopharyngeal carcinoma model systems

Jun 3, 2015

2015/05 Methylation Marker Paper from JAMA Oncology

Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer JAMA Oncol. Published online May 14, 2015. doi:10.1001/jamaoncol.2015.1053
Andrew E. Teschendorff, PhD1,2; Zhen Yang, PhD2; Andrew Wong, PhD3; Christodoulos P. Pipinikas, PhD4; Yinming Jiao, MSc2; Allison Jones, BSc5; Shahzia Anjum, PhD5; Rebecca Hardy, PhD3; Helga B. Salvesen, MD6; Christina Thirlwell, PhD4; Samuel M. Janes, PhD7; Diana Kuh, PhD3; Martin Widschwendter, MD5

At a Glance
¶ The purpose of the research was to assess the suitability of buccal cells as an epithelial source of tissue to examine the effects of smoking on the epigenome, and to test whether these effects are also seen in smoke-related epithelial cancers.
¶ Smoking is associated with widespread changes in the DNA methylation landscape of buccal cells.
¶ Some smoking-associated DNA methylation changes are common to buccal and blood tissue, but buccal cells exhibit significantly more changes than blood cells.
¶ Smoking-associated DNA methylation changes in buccal cells correlate with DNA methylation changes in epithelial cancers and do so most strongly in smoke-related epithelial cancer.

May 27, 2015

2016/105年待辦事項

2016/105年待辦事項
由 sufang 在 三, 05/27/2015 - 08:45 發表 Pre-published Gene Fusion HOK Lin Lab Oral Cancer
Paper Submission
宛樺: DDR1 overexpression in OSCC 
嬿如: EBV Rta mediated DNA methylation in the host genome
 
FGFR Gene Fusion related (玉蓮、奕宏)
1. FGFR2 gene fusion in cholangiocarcinoma (Post 連結)
2. FGFR3 gene fusion in oral cancer (Post 連結)
 
Carcinogen induced epigenetic transformation in human oral keratinocytes (嬿如、英潔)
1. Molas analysis of RNA-Seq data (Post 連結)
2. MTHFR SNPs and epigenetic transformation
3. Develop methylation markers for head and neck patients
 
Mutational landscape of cholangiocarcinoma (Post 連結)
 
Mutational landscape of OSPCC and Oral Cancer (Post 連結)
 
Grant Proposal and Report
1. 104-CAPP05
    月報 x 12 (in progress)
    期中報告 (done by 佩瑩)
    期末報告
2. 105-CAPP  執行與考核點設定
3. 106-CAPP  計畫書撰寫
4. MOHW 衛福部OPMD計畫
    實驗進行 (記得送OSCC DNA給Jeffrey Lab. 一併測試MTHFR)
5. 105-MOST 科技部計畫 (11月中 call for proposal)
6. 105-MOST PPG (LOI submitted)

Apr 1, 2015

RBBS

RBBS
由 sufang 在 三, 04/01/2015 - 09:00 發表 Pre-published RBBS

須細讀, 台大擬用此法screen HNSCC patients.
04/01/2015

Mar 30, 2015

OSCC Cell Line STR information 20150312 更新版

OSCC Cell Line STR information 20150312 更新版
由 sufang 在 一, 03/30/2015 - 08:45 發表 Oral Cancer OSCC Cell Line STR analysis

Dear all,

更新oral cancer相關細胞株之STR資訊
共有16個細胞株, 其中DOK, FaDu, HOK, HSC-3, KB, OEC-M1, SCC-15, SCC-9, Tu-183(共9株)有送件STR的資訊(詳如附件)
須請大家注意的資訊如下:
1. HSC-3, 於Dr.郭靜娟's lab的新HSC-3為真, 請非此來源者把舊的細胞扔了吧! (p.s Dr. 李岳倫於2014年7月及9月分別定的STR結果同新HSC-3, 推測亦為可用細胞株)
2. KB, 於Dr.郭靜娟's lab定STR結果為HeLa細胞, ATCC/JCRB資料庫也證明KB為HeLa. 請勿使用此細胞株!
3. SCC-15, Dr.李岳倫's lab 於2014年9月及10月分別定的STR結果皆與SAS細胞株相符(90.9% match), 使用此細胞株請小心!
4. Tu-183, 於Dr.江士昇's lab於2014年12月所定STR結果未比對到現今資料庫中任何細胞株, 推測可能為可用細胞株. 但Dr. 江提供資訊:expression pattern與預期不同, 大家使用此細胞株請小心!

以上,
如有問題或錯誤的地方, 敬請不吝指正! 謝謝!

敬祝 研究順利

嬿如 敬上

原檔按我下載 (2015_0320_cell_lines_str_information20150312ding_gao.xlsx)

Mar 13, 2015

2015/01/30 國長會議

由 sufang 在 五, 03/13/2015 - 08:29 發表 Pre-published 國長會議 長庚
國衛院長庚雙邊會議 2015/01/30 (五) at 林口長庚
王陸海: miR491-3p is responsible for oral cancer metastasis
葉大森: bile duct cancer (miRNA 200下降), liver cancer (miRNA 200 上升,which is correlated with EMT)
陳鈴津教授介紹 閻紫宸教授另外一個專長是 radio pharmatheutical research (新藥開發 GMP等等)
-----------------------
趙盈凱 副教授【胸腔外科】可吸收性生物醫材在食道癌之應用
東方人食道癌 是由ABC引起的、西方的食道癌則多由胃食道逆流
證實CCRT+Surgert is better therapy than just surgery
-Big Database (2400 病人 由該醫師和助理慢慢build up)
-健保加值資料庫
-cisplatin membrane
biodegradable polymers in thoracic disease
-----------------
陳立宗 特聘研究員兼所長 【國衛院癌症研究所】 Pancreatobiliary Cancer Clinical Trial in Taiwan
–TCOG很多東西需要長庚醫院幫忙 e.g. 陳敏夫教授 adjuvant interferon alpha 2b TCPG T1297 (this is a negative study, indicating that Taiwanese surgery was very successful)
–原來文獻建議 肝癌患者 開完刀後若能 加上抗病毒藥物治療 可有效降低recurrence rate.
但是由TCOG的結果看來, not necessary.
-----------------


Mar 6, 2015

ds-cDNA prep for gene fusion detection

3/6/2015 DR 提醒
1: Daw-Yang seems to use Clontech ds-cDNA prep for single cell Seq + Tagmentation
2: There is no way to add specific primer in this way...

According to Daw-Yang:  I will use Nexera XT for DNA prep.
Figure 1 AMP for targeted RNA and DNA sequencing (pdf 4231). 
Double-stranded cDNA (dscDNA) synthesis starts with total nucleic acid or RNA from fresh or FFPE material without ribosomal RNA or genomic DNA(gDNA) depletion. Solid-phase reversible immobilization (SPRI)-cleaned double-stranded cDNA or fragmented genomic DNA is processed with end repair and dA tailing, directly followed by ligation with a half-functional adapter. SPRI-cleaned, ligated fragments are amplified with 10–14 cycles of multiplex PCR1 using gene-specific primers (GSP1 pool) containing a PCR multiplexing tag (brown) and a primer complementary to a portion of the universal ligated adapter (short green). An unknown fusion partner or target sequence is indicated by a question mark. SPRI-cleaned PCR1 amplicons are amplified with a second round of 10-cycle multiplex PCR2 using a combination of GSP2 pool nested gene-specific primers (3' downstream of GSP1), which are tagged with the second adapter sequence specific for Ion Torrent (black-blue) or Illumina (black-red, subsequently tagged with an indexing primer (red-orange-purple)), and a second nested primer against the ligated universal adapter (long green). After a final SPRI cleanup, the target amplicon library is ready for quantitation, downstream clonal amplification and sequencing. 
 
 

Fluidigm 4-Primer Amplicon Tagging
The Access Array 4-primer amplicon tagging scheme is based on a step-out four primer PCR strategy where tagged target-specific (TS) primer pairs are combined with sample-specific primer pairs that contain a barcoding sequence and the adaptor sequences used by Illumina sequencing systems. By incorporating sample-specific barcodes, all 2,304 PCR products generated from a 48.48 Access Array Integrated Fluidic Circuit (IFC) are unique and can be pooled together to run in a single sequencing experiment.
 

Nextera_xt_sample_prep

Feb 27, 2015

TCGA HNSCC Marker Paper

由 sufang 在 五, 02/27/2015 - 10:08 發表 Oral Cancer HNSCC TCGA

Comprehensive genomic characterization of head and neck squamous cell carcinomas (pdf 4292; PubMed Link)
The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.
Five Figures in the Main Text:
Figure 1 | DNA copy number alterations. a, Copy number alterations by anatomic site and HPV status for squamous cancers. Lung squamous cell carcinoma (LUSC, n = 358) and cervical squamous cell carcinoma (CESC, n = 114). b, Unsupervised analysis of copy number alteration of HNSCC (n = 279) with associated characteristics. The rectangle indicates chromosome 7 amplifications in the purple cluster. NA, not available. 
Figure 2 | Significantly mutated genes in HNSCC. Genes (rows) with significantly mutated genes (identified using the MutSigCV algorithm; q , 0.1) ordered by q value; additional genes with trends towards significance are also shown. Samples (columns, n = 279) are arranged to emphasize mutual exclusivity among mutations. Left, mutation percentage in TCGA. Right, mutation percentage in COSMIC (‘upper aerodigestive tract’ tissue). Top, overall number of mutations per megabase. Colour coding indicates mutation type.
Figure 3 | Candidate therapeutic targets and driver oncogenic events. Alteration events for key genes are displayed by sample (n = 279). TSG, tumour suppressor gene.
Figure 4 | Integrated analysis of genomic alterations. a,b,Samples (n=279) ared isplayed in columns and grouped by gene expression (a) or methylation (b) subtype (sub.). Unadjusted two-sided Fisher’s exact test P values assess the association of each genomic alteration. Methylation probe location of CpG islands, shores and shelves are shown on the left of b. Annotation shows HPV status and subtype (16, 33 and 35). CN, copy number.
Figure 5 | Deregulation of signalling pathways and transcription factors. Key affected pathways, components and inferred functions, are summarized in the main text and Supplementary Information section 7 for n = 279 samples. The frequency (%) of genetic alterations for HPV(–) and HPV(+) tumours are shown separately within sub-panels and highlighted. Also see Supplementary Fig. 7.15. Pathway alterations include homozygous deletions, focal amplifications and somatic mutations. Activated and inactivated pathways/genes, and activating or inhibitory symbols are based on predicted effects of genome alterations and/or pathway functions. 

Ten Parts of Supplementary Information: 
S1: Biospecimen collection and clinical data
S1.1. Biospecimen collection and clinical data  
S1.2. HPV detection methods  
S1.3. Survival analysis  **
Figure S1.1. HPV status as a function of clinical and molecular characteristics  
Figure  S1.2.  Receiver  operating  characteristic  (ROC)  curves  in  HPV-associated  miRNAs  in  oropharyngeal HNSCC  
Figure S1.3. DNA methylation signatures of HPV
Figure S1.4. Survival analysis for select clinical and genomic variables
Figure S1.5. Survival analysis for platform-specific subtypes 
Table S1.1. Summary of clinical data 
Data file S1.1. Data freeze clinical dataset (This file contains clinical and demographic information for all patients)
Data file S1.2. Summary of HPV detection results  (This file contains the results of molecular analyses used to determine the HPV status of all patients. These include data from in situ hybridization, p16 staining, RNA and DNA sequencing, and the MassArray assay.)
Data file S1.3 Mutation signatures by HPV status (This file contains counts of base changes seen in APOBEC and smoking mutation signatures for HPV(+) and HPV(-) subjects. Fisher’s exact test p-values are also shown.)
S2: Copy number analysis  
S2.1. SSNP array-based copy number analysis
S2.2. Structural alterations  
Figure S2.1. GISTIC 2.0  analysis  of  significantly  reoccurring  focal  alteration  in  279  HNSCC tumors  
Figure S2.2. GISTIC amplification and deletion peaks in lung squamous cell and cervical squamous cell  carcinoma  
Figure S2.3. Comparison of GISTIC 2.0 analyses of 243 HPV(–) and 36 HPV(+) head and neck tumors  
Figure S2.4. Number of copy number segments in HPV(+) and HPV(–) samples  
Data file S2.1. GISTIC amplification and deletion peak annotation in head and neck squamous cell (all  samples, by HPV
status, and by site), lung squamous cell carcinoma, cervical squamous cell carcinoma  (results from GISTIC analyses for head and neck (HNSC), lung, and cervical squamous cell carcinoma. Additional analyses for HNSC were conducted by HPV status and tumor site.)
Data  file  S2.2. Fisher’s  exact  test  p-values  for  frequency  comparisons  of  significantly  reoccurring  alterations by HPV
status and site (results of analyses of frequency comparisons of significantly reoccurring DNA copy number alterations by HPV status and tumor site. Fisher’s exact test p-values are also shown.)
S3: RNA sequencing  
S3.1. RNA sequencing and expression quantification  
S3.2. RNA-Seq for confirmation of somatic alterations reported in whole exome sequencing  
S3.3. Gene fusion detection  
S3.4. RNA-Seq for gene splicing and viral integration  
Figure S3.1. RNA-Seq for confirmation of somatic alterations reported in whole exome sequencing  
Figure S3.2.  FGFR3-TACC3 fusion event  
Figure S3.3. EGFR vIII mutant sample  
Figure S3.4. Exon 14 skipping in MET+ 
Figure S3.5. Alterations of CDKN2A gene structure, copy number, and expression of its protein coding  transcripts p16INK4A and p14ARF  
Figure S3.6. Integration of DNA mutation type, copy number, and gene expression for CDKN2A+ 
Figure S3.7. Alterations of FAT1 gene structure, copy number, and expression  
Figure S3.8. Integration of DNA mutation type, copy number, and gene expression for FAT1+ 
Figure S3.9. Integration of DNA mutation type, copy number, and gene expression for predicted driver  genes relevant to HNSCC  
Figure S3.10. Distribution of HPV integration breakpoints across the host genome
Figure S3.11. The KLK12 gene documents recurrent alternate transcription in HNSCC  Figure S3.12. Heterogeneous TP63 isoform usage in HNSCC+ 
Data file S3.1. RNA-Seq predicted fusions ( contains the results of fusion detection analyses performed with MapSplice. )
Data file S3.2. Viral integration sites (information about HPV viral integration sites based on the analysis of RNA and DNA sequencing 
data.)
Data file S3.3. SigFuge clustering results for alternatively spliced genes (the results of SigFuge analyses to detect differential expression of multiple gene isoforms. Uncorrected p-values are shown.)

Feb 17, 2015

Molas Analysis on Arecoline-Treated HOK Cells

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.














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
(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