29

推荐5个生物信息学领域的教授

排名不分先后:

推荐宾夕法尼亚州立大学的一个教授Istvan Albert

他写了一本书是: https://www.biostarhandbook.com/
他还可以授予网上课程学位:http://www.personal.psu.edu/iua1/certificate.html
他还推荐了一本R语言书籍:http://onepager.togaware.com/

关注一下华盛顿大学医学院的教授Obi L. Griffith

他的主页:http://www.obigriffith.org/

他的一个比较出名的的贡献是 www.rnaseq.wiki
他在 Biostars bioinformatics forum 非常活跃
他的课程包括Molecular Basis of Cancer (BIO5288) and Genetics and Genomics of Disease (BIO5487) at Washington University School of Medicine.
I was a TA for Genome Analysis (MEDG505) and the bioinformatics section of Advanced Human Molecular Genetics (MEDG520) and a guest instructor for Cell Biology For Biomedical Engineering Graduate Students (APSC552), Cell and Organismal Biology (BIOL111) and Cell Biology (BIOL200) at UBC.

关注一下华盛顿大学医学院的教授Malachi Griffith

他的个人主页是:http://www.malachigriffith.org/index.htm

他的github主页是:https://github.com/malachig

WashU TGI Faculty page: Profile
Linked In: Profile
Twitter: Feed
Google Scholar: Citations
Research Gate: Profile
Scopus: Profile
Open Research ID: Profile
Github: Profile
BioStar: Profile
SeqAnswers: Profile
Code Academy: Profile
Iterative Genomics Consulting: Company website
Flickr: Photostream
www.dgidb.org
www.alexaplatform.org

关注一下麦吉尔大学的Pablo Cingolani教授

他是snpeff的作者

他的github是:https://github.com/pcingola
现就职于McGill University

推荐弗吉尼亚大学的stephen教授

他是个人主页:http://stephenturner.us/

他所有公开的ppt : https://speakerdeck.com/stephenturner
stephen教授我要重点提一下,因为他的教育资源特别多。
09

affymetix的基因表达芯片数据差异基因分析

我主要是看了一个差异分析的教程,讲的非常详细,全面,我先简单列出这个教程,然后再贴出我的代码

GEO本来只有三种层级的数据,分别是Sample, Platform, and Series
现在共有14,927 platforms,包括主流的affymetrix,agilent,illumina等产商的芯片,以及它们在不同领域的应用(snp,snv,gwas等等),以及各种不同的生物体(人,小鼠,大鼠)
这个分析流程,仅仅针对于affymetrix公司的基因表达相关的芯片数据。
目录如下:
因为他也是转载,所以链接失效了,现在的链接如下:
其实根据目录名重新搜索肯定能得到内容的, 链接失效太正常了。
具体内容,我整理并且重新注释了以下,在有道云笔记里面。
基本上只需要用心看这个教程,都能上手芯片数据的差异分析,但这只是差异分析的一种方法而已,而且还是非常过时的方法。
现在比较流行DESeq,edgeR等高通量测序的差异分析包,即使是十几年前的芯片数据,也不需要下载cel那种数据,可以直接下载每个项目的表达量矩阵Series Matrix File(s)
然后在R里面用read.table,调整好参数就可以直接读取啦!
06

JQuery学习笔记

以后写这样的文章就直接用有道云笔记分享啦,这样可以节约这个免费的云服务器的空间。

jquery学习笔记第一弹:基础语法

http://note.youdao.com/share/?id=82021515144eb4820762e9fdbc686340&type=note

JQuery笔记第二弹:ppt效果操作

http://note.youdao.com/share/?id=08eb606b2084b9b0d8c9eb5ef72e3433&type=note

JQuery笔记第三弹:操作html元素

http://note.youdao.com/share/?id=fb8ff7deeb186adb82751838bf82cfbe&type=note

JQuery笔记第四弹:循环,遍历,判断等语句实现

http://note.youdao.com/share/?id=746ac6f1a801351f49d13cb3d7a335bf&type=note

JQuery笔记第五弹:Ajax实现

http://note.youdao.com/share/?id=0b2c6fb8c89e307ec79602e6d67e7c66&type=note

JQuery参考手册-函数大全

http://note.youdao.com/share/?id=2e926f98c9bd51b1192d309706f8c1ca&type=note

 

 

29

研究癌症领域必看文献

最近需要了解一些癌症相关知识,看到了这个文献列表,觉得非常棒,所以推荐给大家。

抽时间慢慢看,一个月应该可以把这些文献看完的。

癌症种类大全 http://www.cancer.gov/types
癌症药物大全 http://www.cancer.gov/about-cancer/treatment/drugs
癌症所有的信息几乎都能在这个网站上面找到 http://www.cancer.gov/
包括癌症的科普、treatment、diagnosis,prognosis,classification,drugs、prediction等等

different_kinds_of_cancer_in_CCLE

Cancer Precision Medicine: Improving Evidence in Practice - August 24, 2015

NCI-MATCH Trial Opens,External Web Site Icon AACR blog post, August 2015

NCI-MATCH launch highlights new trial design in precision-medicine eraExternal Web Site Icon
McNeal C , JNCI, August 2015

The Cancer Genomics Resource List, 2014External Web Site Icon
Zutter MM et al. CAP Lab Improvement Program,Archives of Pathology, August 2015

Personalized medicine and economic evaluation in oncology: all theory and no practice?External Web Site Icon
Garattini L et al. Expert Rev Pharmacoecon Outcomes Res 2015 Aug 9. 1-6

Precision medicine trials bring targeted treatments to more patients,External Web Site Icon C. Helwick, ASCO Post, Jul 25

Next-generation sequencing to guide cancer therapy External Web Site Icon
Gagan J et al, Genome Medicine, July 29, 2015

Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials.External Web Site Icon
Meric-Bernstam F et al. J. Clin. Oncol. 2015 May 26.

Brave-ish new world-what's needed to make precision oncology a practical reality.External Web Site Icon
MacConaill LE et al. JAMA Oncol 2015 Jul 16.

Genomic profiling: Building a continuum from knowledge to careExternal Web Site Icon
Helen C et al. JAMA Oncology, July 2015

Are we there yet?External Web Site Icon
When it comes to curing cancer, targeted therapies and genomic sequencing are helping, but we still have far to go. Genome Magazine, June 29, 2015

Artificial intelligence, big data, and cancerExternal Web Site Icon
Kantarjian H et al, JAMA Oncology, June 2015

Multigene panel testing in oncology practice - how should we respond?External Web Site Icon
Kurian AW et al. JAMA Oncology, June 2015

Use of whole genome sequencing for diagnosis and discovery in the cancer genetics clinic.External Web Site Icon
Foley SB et al. EBioMedicine 2015 Jan 2(1) 74-81

The future of molecular medicine: biomarkers, BATTLEs, and big data External Web Site Icon
ES Kim, ASCO University, June 2015

NCI-MATCH trial will link targeted cancer drugs to gene abnormalitiesExternal Web Site Icon

Targeted agent and profiling utilization registry study,External Web Site Icon from the American Society for Clinical Oncology

ASCO study aims to learn from patient access to targeted cancer drugs used off-label,External Web Site Icon American Society for Clinical Oncology

Improving evidence developed from population-level experience with targeted agents Adobe PDF file [PDF 462.93 KB]External Web Site Icon
McLellan M et al Issue Brief. Conference on Clinical Cancer Research November 2014

Implementing personalized cancer care.External Web Site Icon
Schilsky RL et al. Nat Rev Clin Oncol 2014 Jul (7) 432-8

Accelerating the delivery of patient-centered, high-quality cancer care.External Web Site Icon
Abrahams E et al. Clin. Cancer Res. 2015 May 15. (10) 2263-7

Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity.External Web Site Icon
Catenacci DV et al. Mol Oncol 2015 May (5) 967-996

Cancer Precision Medicine: Improving Evidence in Practice - May 29, 2015

Diagnosis and treatment of cancer using genomicsExternal Web Site Icon
Vockley JG et al. BMJ, May 28, 2015

Targeted agent and profiling utilization registry study,External Web Site Icon from the American Society for Clinical Oncology

ASCO study aims to learn from patient access to targeted cancer drugs used off-label,External Web Site Icon American Society for Clinical Oncology

Improving evidence developed from population-level experience with targeted agents Adobe PDF file [PDF 462.93 KB]External Web Site Icon
McLellan M et al Issue Brief. Conference on Clinical Cancer Research November 2014

Implementing personalized cancer care.External Web Site Icon
Schilsky RL et al. Nat Rev Clin Oncol 2014 Jul (7) 432-8

Accelerating the delivery of patient-centered, high-quality cancer care.External Web Site Icon
Abrahams E et al. Clin. Cancer Res. 2015 May 15. (10) 2263-7

Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity.External Web Site Icon
Catenacci DV et al. Mol Oncol 2015 May (5) 967-996

Precision Medicine: Cancer and Genomics - May 12, 2015

Promise, peril seen in personalized cancer therapy,External Web Site Iconby Marie McCullough, Philadelphia Inquirer, May 10

A decision support framework for genomically informed investigational cancer therapy.External Web Site Icon
Meric-Bernstam F et al. J. Natl. Cancer Inst. 2015 Jul (7)

Divide and conquer: The molecular diagnosis of cancer,External Web Site Icon by Louis M. Staudt, National Cancer Insitute, Apr 13

Health: Make precision medicine work for cancer careExternal Web Site Icon
To get targeted treatments to more cancer patients pair genomic data with clinical data, and make the information widely accessible, Mark A. Rubin. Nature News, Apr 15

Using somatic mutations to guide treatment decisionsExternal Web Site Icon
Horlings H et al. JAMA Oncology, March 12, 2015

The landscape of precision cancer medicine clinical trials in the United StatesExternal Web Site Icon
Roper N et al. Cancer Treatment Reviews 2015

What is “precision medicine?External Web Site Icon Information from the National Cancer Institute

Impact of cancer genomics on precision medicine for the treatment of cancer,External Web Site Icon from the Cancer Genome Atlas, NCI

US precision-medicine proposal sparks questions,External Web Site Icon by Sara Reardon, Nature News, Jan 22

Obama's 'precision medicine' means gene mapping,External Web Site IconNBC News, Jan 21

What is President Obama's 'precision medicine' plan, and how might it help you?External Web Site Icon By Lenny Bernstein, Jan 21

Recent reviews

Companion diagnostics: the key to personalized medicine.External Web Site Icon
Jørgensen JT. Expert Rev Mol Diagn. 2015 Feb;15(2):153-6

Promoting precision cancer medicine through a community-driven knowledgebase.External Web Site Icon
Geifman N, et al. J Pers Med. 2014 Dec 15;4(4):475-88.

Toward a prostate cancer precision medicine.External Web Site Icon
Rubin MA. Urol Oncol. 2014 Nov 20.

Prioritizing targets for precision cancer medicine.External Web Site Icon
Andre F, et al. Ann Oncol. 2014 Dec;25(12):2295-303

Toward precision medicine with next-generation EGFR inhibitors in non-small-cell lung cancer.External Web Site Icon
Yap TA, Popat S. Pharmgenomics Pers Med. 2014 Sep 19;7:285-95.

Genomically driven precision medicine to improve outcomes in anaplastic thyroid cancer.External Web Site Icon
Pinto N, et al.  J Oncol. 2014;936285

Translating genomics for precision cancer medicine.External Web Site Icon
Roychowdhury S, Chinnaiyan AM. Annu Rev Genomics Hum Genet. 2014;15:395-415

The Cancer Genome Atlas: Accomplishments and Future - April 3, 2015

The Cancer Genome Atlas (TCGA): an immeasurable source of knowledgeExternal Web Site Icon
Tomczak K, et al. Contemp Oncol (Pozn). 2015; 19(1A): A68-A77.

The Cancer Genome Atlas' 4th Annual Scientific SymposiumExternal Web Site Icon
May 11-12 ~ Bethesda, MD

The Cancer Genome Atlas (TCGA) Data Portal External Web Site Icon
Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA

Cancer Genomics Hub: A resource of the National Cancer Institute,External Web Site Icon from the USC Genome Browser

Molecular classification of gastric adenocarcinoma: translating new insights from The Cancer Genome Atlas Research Network.External Web Site Icon
Sunakawa Y et al. Curr Treat Options Oncol 2015 Apr (4) 331

TCGA data and patient-derived orthotopic xenografts highlight pancreatic cancer-associated angiogenesis.External Web Site Icon
Gore J et al. Oncotarget 2015 Feb 25.

Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group.External Web Site Icon
Shinagare AB et al. Abdom Imaging 2015 Mar 10.

Proteomics of colorectal cancer in a genomic context: First large-scale mass spectrometry-based analysis from the Cancer Genome Atlas.External Web Site Icon
Jimenez CR et al. Clin. Chem. 2015 Feb 26.

End of cancer-genome project prompts rethinkExternal Web Site Icon
Geneticists debate whether focus should shift from sequencing genomes to analysing function. Heidi Ledford, Nature News and Comments, January 2015

Cancer Genomics: Insights into Driver Mutations - March 10, 2015

Seek and destroy: Relating cancer drivers to therapiesExternal Web Site Icon
E. Martinez-Ledesma et al. Cell, March 9, 2015

In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunitiesExternal Web Site Icon
C Rubio-Perez et al. Cancer Cell, March 9, 2015

MADGiC: a model-based approach for identifying driver genes in cancer. Adobe PDF file [PDF 373.56 KB]External Web Site Icon
Keegan D. Korthauer et al. Bioinformatics, January 2015

Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine.External Web Site Icon
Benjamin J Raphael et al. Genome Medicine 2014

Novel recurrently mutated genes in African American colon cancers.External Web Site Icon
Guda K et al. Proc Natl Acad Sci U S A. 2015 Jan 12

Sparse expression bases in cancer reveal tumor drivers.External Web Site Icon
Logsdon BA, et al. Nucleic Acids Res. 2015 Jan 12

Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.External Web Site Icon
Bertrand D, et al. Nucleic Acids Res. 2015 Jan 8

Identification of constrained cancer driver genes based on mutation timing.External Web Site Icon
Sakoparnig T, et al. PLoS Comput Biol. 2015 Jan 8;11(1):e1004027

CaMoDi: a new method for cancer module discovery.External Web Site Icon
Manolakos A, et al. BMC Genomics. 2014 Dec 12;15 Suppl 10:S8.

VHL, the story of a tumour suppressor gene.External Web Site Icon
Gossage L, et al. Nat Rev Cancer. 2014 Dec 23;15(1):55-64

Targeting the MET pathway for potential treatment of NSCLC.External Web Site Icon
Li A, et al. Expert Opin Ther Targets. 2014 Dec 23:1-12

Deciphering oncogenic drivers: from single genes to integrated pathways.External Web Site Icon
Chen J, et al. Brief Bioinform. 2014 Nov 5.

Driver and passenger mutations in cancer.External Web Site Icon
Pon JR, et al. Annu Rev Pathol. 2014 Oct 17

Hereditary Cancer Genetic Testing: Where are We? - December 18, 2014

NCI paper:Prevalence and correlates of receiving and sharing high-penetrance cancer genetic test results: Findings from the Health Information National Trends SurveyExternal Web Site Icon
Taber J.M. et al Public Health Genomics, January 2015

Clinical decisions: Screening an asymptomatic person for genetic risk--polling resultsExternal Web Site Icon
Schulte J, et al. N Engl J Med 2014 Nov;371(20):e30

Testing for hereditary breast cancer: Panel or targeted testing? Experience from a clinical cancer genetics practice.External Web Site Icon
Doherty J, J Genet Couns. 2014 Dec 5

Hereditary colorectal cancer syndromes: American Society of Clinical Oncology clinical practice guideline endorsement of the familial risk-colorectal cancer: European Society for Medical Oncology clinical practice guidelines.External Web Site Icon
Stoffel EM, et al. J Clin Oncol. 2014 Dec 1

Population testing for cancer predisposing BRCA1/BRCA2 mutations in the Ashkenazi-Jewish community: A randomized controlled trial.External Web Site Icon
Manchanda R, et al. J Natl Cancer Inst. 2014 Nov 30;107(1)

Cost-effectiveness of population screening for BRCA mutations in Ashkenazi Jewish women compared with family history-based testing.External Web Site Icon
Manchanda R et al. J Natl Cancer Inst. 2014 Nov 30;107(1). pii: dju380. doi: 10.1093/jnci/dju380. Print 2015 Jan.

Check out our Cancer Genetic Testing  Update Page for additional information and links

Cancer Genomic Tests (October 30, 2014)

Cancer Precision Medicine: Where Are We? - September 18, 2014

NIH announces the launch of 3 integrated precision medicine trials; ALCHEMIST is for patients with certain types of early-stage lung cancer,External Web Site Icon August 2014

National Cancer Institute's Precision Medicine Initiatives for the New National Clinical Trials Network.External Web Site Icon Jeffrey Abrams et al. ASCO Annual Meeting 2014

Personalized medicine: Special treatment.External Web Site Icon
Michael Eisenstein. Nature, September 11, 2014

Why the controversy? Start sequencing tumor genes at diagnosis. Tumor sequencing at the time of diagnosis can give significant insight for successful cancer treatment,External Web Site Icon by Shelly Gunn, Genetic Engineering & Biotechnology News, Sep 10

National Cancer Institute information: Precision medicine and targeted therapyExternal Web Site Icon

Genomics and precision oncology: What's a targeted therapy for cancer?External Web Site Icon An updated list of approved drugs from the National Cancer Institute (2014)

Therapy: This time it's personalExternal Web Site Icon
Gravitz L Nature 509, S52-S54 2014 May 29

Multi-marker solid tumor panels using next-generation sequencing to direct molecularly targeted therapiesExternal Web Site Icon
Michael Marrone, et al. PLoS Currents Evidence on Genomic Tests 2014 May 27

Impact of cancer genomics on precision medicine for the treatment of cancer,External Web Site Icon from the National Cancer Institute

Cancer genomics and precision medicine in the 21st century Adobe PDF file [PDF 2.20 MB]External Web Site Icon, power point presentation from the National Human Genome Research Institute

 

28

TCGA年度研讨会资料分享

TCGA想必搞生信都或有耳闻,尤其是癌症研究方向的,共4个年度研讨会,主要是pdf格式的ppt分享,有需要的可以具体点击到页面一个个下载自己慢慢研究,也可以用我下面链接直接下载。

本来是有youtube分享演讲视频的,但是国内被墙了,大家就看看ppt吧

http://www.genome.gov/17516564

The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing.

TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services.

Meetings

pdf链接地址如下

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Laird.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Durbin.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Ley.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Sartor.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Ciriello.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Imielinski.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Gao.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Carter.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Ng.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Parvin.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Raphael.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Lawrence.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Kreisberg.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Marra.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Helman.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Stuart.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Cooper.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Levine.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Natsoulis.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Haussler.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Erkkila.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Gehlenborg.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Qiao.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Sivachenko.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Sumazin.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Gutman.pdf

http://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Mardis.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/01_Shaw.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/02_Chanock.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/03_Staudt.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/05_Creighton.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/06_Stojanov.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/07_Karchin.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/08_Mungall.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/09_Hakimi.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/10_Gao.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/11_Hayes.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/12_Troester.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/13_Knobluach.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/14_Raphael.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/15_Akbani.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/16_Giordano.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/17_Weinstein.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/18_Zheng.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/19_Getz.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/20_VanDneBroek.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/21_Liao.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/22_Khazanov.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/23_Levine.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/24_Miller.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/25_Ewing.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/26_Cirello.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/27_Verhaak.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/28_Hofree.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/29_Meyerson.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/30_Yang.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/31_Wheeler.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/32_Parfenov.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/33_Bernard-Rovira.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/34_Hast.pdf

http://www.genome.gov/Multimedia/Slides/TCGA2/36_Sellars.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/04_Brat.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/05_Mungall.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/06_Boutros.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/07_Zmuda.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/08_Benz.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/09_Zheng.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/11_Creighton.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/12_Aksoy.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/13_Dinh.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/14_Stuart.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/15_Amin.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/16_Gross.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/15_Akbani.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/18_Giordano.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/19_Amin-Mansour.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/20_Oesper.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/21_Gatza.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/22_Bernard.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/23_Sinha.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/24_Akbani.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/25_Watson.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/26_Martignetti.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/27_Bandlamudi.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/28_Fu.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/29_Akdemir.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/30_Bass.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/31_Hakimi.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/32_Wheeler.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/33_Lehmann.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/34_Gordenin.pdf

http://www.genome.gov/Multimedia/Slides/TCGA3/35_Wyczalkowski.pdf

 

http://www.genome.gov/Multimedia/Slides/TCGA4/02_Zenklusen.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/03_Hutter.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/04_Brat.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/05_Mungall.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/06_Linehan.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/07_Brooks.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/08_Wu.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/09_Giger.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/10_Wilkerson.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/11_Orsulic.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/12_Zhong.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/13_Knijnenburg.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/14_Akbani.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/15_Wang.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/16_Poisson.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/17_Alaeimahabadi.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/18_Noushmehr.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/19_Pantazi.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/20_Shih.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/21_Stransky.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/22_Giordano.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/23_Davidsen.pdf

http://www.genome.gov/Multimedia/Slides/TCGA4/24_Gross.pdf

 

28

生信教程推荐-MSU的一个生信课程

http://angus.readthedocs.org/en/2014/index.html

Next-Gen Sequence Analysis Workshop (2014)

This is the schedule for the 2014 MSU NGS course.

This workshop has a Workshop Code of Conduct.

Download all of these materials or visit the GitHub repository.

Day Schedule
Monday 8/4
Tuesday 8/5
Wed 8/6
Thursday 8/7
Friday 8/8
Saturday 8/9
Monday 8/11
Tuesday 8/12
Wed 8/13
Thursday 8/14
Friday 8/15

 

17

转-windows快捷键,让你的办公效率提升一个档次

  1. gpedit.msc-----组策略

2. sndrec32-------录音机

3. Nslookup-------IP地址侦测器

4. explorer-------打开资源管理器

5. logoff---------注销命令

6. tsshutdn-------60秒倒计时关机命令

7. lusrmgr.msc----本机用户和组

8. services.msc---本地服务设置

9. oobe/msoobe /a----检查XP是否激活

10. notepad--------打开记事本

11. cleanmgr-------垃圾整理

12. net start messenger----开始信使服务

13. compmgmt.msc---计算机管理

15. conf-----------启动netmeeting

16. dvdplay--------DVD播放器

17. charmap--------启动字符映射表

18. diskmgmt.msc---磁盘管理实用程序

 19. calc-----------启动计算器

20. dfrg.msc-------磁盘碎片整理程序

21. chkdsk.exe-----Chkdsk磁盘检查

22. devmgmt.msc--- 设备管理器

23. regsvr32 /u *.dll----停止dll文件运行

24. drwtsn32------ 系统医生

25. rononce -p ----15秒关机

26. dxdiag---------检查DirectX信息

27. regedt32-------注册表编辑器

28. Msconfig.exe---系统配置实用程序

29. rsop.msc-------组策略结果集

30. mem.exe--------显示内存使用情况

31. regedit.exe----注册表

32. winchat--------XP自带局域网聊天

33. progman--------程序管理器

34. winmsd---------系统信息

  43. write----------写字板

44. winmsd---------系统信息

46. winchat--------XP自带局域网聊天

48. Msconfig.exe---系统配置实用程序

49. mplayer2-------简易widnows media player

50. mspaint--------画图板

51. mstsc----------远程桌面连接

52. mplayer2-------媒体播放机

53. magnify--------放大镜实用程序

 54. mmc------------打开控制台

55. mobsync--------同步命令

56. dxdiag---------检查DirectX信息

57. drwtsn32------ 系统医生

58. devmgmt.msc--- 设备管理器

59. dfrg.msc-------磁盘碎片整理程序

60. diskmgmt.msc---磁盘管理实用程序

61. dcomcnfg-------打开系统组件服务

62. ddeshare-------打开DDE共享设置

65. net start messenger----开始信使服务

67. nslookup-------网络管理的工具向导

68. ntbackup-------系统备份和还原

69. narrator-------屏幕“讲述人”

70. ntmsmgr.msc----移动存储管理器

71. ntmsoprq.msc---移动存储管理员操作请求

72. netstat -an----(TC)命令检查接口

73. syncapp--------创建一个公文包

  74. sysedit--------系统配置编辑器

75. sigverif-------文件签名验证程序

76. sndrec32-------录音机

77. shrpubw--------创建共享文件夹

78. secpol.msc-----本地安全策略

 80. services.msc---本地服务设置

81. Sndvol32-------音量控制程序

82. sfc.exe--------系统文件检查器

83. sfc /scannow---windows文件保护

84. tsshutdn-------60秒倒计时关机命令

85. tourstart------xp简介(安装完成后出现的漫游xp程序)

86. taskmgr--------任务管理器

 87. eventvwr-------事件查看器

88. eudcedit-------造字程序

 92. progman--------程序管理器

94. rsop.msc-------组策略结果集

95. regedt32-------注册表编辑器

96. rononce -p ----15秒关机

99. cmd.exe--------CMD命令提示符

100. chkdsk.exe-----Chkdsk磁盘检查

101. certmgr.msc----证书管理实用程序

 102. calc-----------启动计算器

103. charmap--------启动字符映射表

104. cliconfg-------SQL SERVER 客户端网络实用程序

105. Clipbrd--------剪贴板查看器

106. conf-----------启动netmeeting

107. compmgmt.msc---计算机管理

108. cleanmgr-------垃圾整理

109. ciadv.msc------索引服务程序

110. osk------------打开屏幕键盘

 113. lusrmgr.msc----本机用户和组

114. logoff---------注销命令

115. fsmgmt.msc-----共享文件夹管理器

116. utilman--------辅助工具管理器

117. iexpress-------木马捆绑工具

 打开服务管理器的是services.msc

如果要用cmd直接启用已知服务名的服务如下:

net start [服务名] 启动一个服务

net stop [服务名] 停用一个服务

 

01

Samtools无法同时得到mpileup格式的数据和bcftools格式的数据

 来自于: https://www.biostars.org/p/63429/

I'm using samtools mpileup and would like to generate both a pileup file and a vcf file as output. I can see how to generate one or the other, but not both (unless I run mpileup twice). I suspect I am missing something simple.

Specifically, calling mpileup with the -g or -u flag causes it to compute genotype likelihoods and output a bcf. Leaving these flags off just gives a pileup. Is there any way to get both, without redoing the work of producing the pileup file? Can I get samtools to generate the bcf _from_ the pileup file in some way? Generating the bcf from the bam file, when I already have the pileup, seems wasteful.

Thanks for any help!

我写了脚本来运行,才发现我居然需要两个重复的步骤来得到mpileup格式的数据和bcftools格式的数据,而这很明显的重复并且浪费时间的工作

for i in *sam

do

echo $i

samtools view -bS $i >${i%.*}.bam

samtools sort ${i%.*}.bam ${i%.*}.sorted

samtools index ${i%.*}.sorted.bam

samtools mpileup -f /home/jmzeng/ref-database/hg19.fa  ${i%.*}.sorted.bam  >${i%.*}.mpileup

samtools mpileup -guSDf  /home/jmzeng/ref-database/hg19.fa  ${i%.*}.sorted.bam  | bcftools view -cvNg - > ${i%.*}.vcf

Done

我想得到mpileup格式,是因为后续的varscan等软件需要这个文件来call snp

而得到bcftools格式可以直接用bcftools进行snp-calling

samtools mpileup 命令只有用了-g或者-u那么就只会输出bcf文件

如果想得到mpileup格式的数据,就只能用-f参数。

  • bcftools doesn't work on pileup format data. It works on bcf/vcf files.
  • samtools provides a script called sam2vcf.pl, which works on the output of "samtools pileup". However, this command is deserted in newer versions. The output of "samtools mpileup" does not satisfy the requirement of sam2vcf.pl. You can check the required pileup format on lines 95-99, which is different from output of "samtools mpileup".

 

05

国外最出名的R语言大会-useR

这是2014年的会议报告以及ppt,但是好像很多ppt都是需要翻墙才能下载

http://user2014.stat.ucla.edu/#tutorials

Morning Tutorials Monday, 9:15

Room Presenter Title
Palisades Salon A+B Max Kuhn Applied Predictive Modeling in R
Palisades Salon C+F Winston Chang Interactive graphics with ggvis
Palisades Salon D+E Yihui Xie Dynamic Documents with R and knitr [Slides] [Examples]
Hermosa Romain Francois C++ and Rcpp11 for beginners [slides]
Venice Bob Muenchen Managing Data with R
Sproul-Landing building, 3rd floor Matt Dowle Introduction to data.table [Tutorial] [Talk]
Sproul-Landing building, 4th floor Virgilio Gomez Rubio Applied Spatial Data Analysis with R
Sproul-Landing building, 5th floor Martin Morgan Bioconductor

Afternoon Tutorials Monday, 14:00

Room Presenter Title
Palisades Salon A+B Hadley Wickham Data manipulation with dplyr
Palisades Salon C+F Garrett Grolemund Interactive data display with Shiny and R
Palisades Salon D+E Drew Schmidt Programming with Big Data in R
Hermosa S繪ren H繪jsgaard Graphical Models and Bayesian Networks with R
Venice John Nash Nonlinear parameter optimization and modeling in R [slides]
Sproul-Landing building, 3rd floor Dirk Eddelbuettel An Example-Driven Hands-on Introduction to Rcpp [slides]
Sproul-Landing building, 4th floor Ramnath Vaidyanathan Interactive Documents with R
Sproul-Landing building, 5th floor Thomas Petzoldt Simulating differential equation models in R

 

然后2015年的也要开始了,有兴趣的朋友可以June 30 - July 3, 2015
Aalborg, Denmark看看,有很多干货分享!

http://user2015.math.aau.dk/#BN

2015的内容如下

 

01

CHIP-seq第三讲之使用MACS软件寻找peaks

在使用Bowtie比对于完Chip-Seq的结果后,就需要用到MACS或者ERANGE来找出峰所在的位置了。但是由于ERANGE的设置比较复杂,所以最为流行的还是MACS。

一.首先安装MACS软件

MACS有两个版本,分别是MACS14和MACS2。MACS2在很多方面都对MACS14做了重大改进,但目前还在测试阶段。我们依然以MACS14为例进行说明。

MACS软件的下载地址在wget https://codeload.github.com/taoliu/MACS/zip/master

这是一个python软件,有152M,已经算是很大了!所以需要按照安装python的方法来安装它!但是,好像这个是最新版的,我们还是用1.4版本吧

wget http://github.com/downloads/taoliu/MACS/MACS-1.4.2-1.tar.gz

其实它的readme已经把这个软件的各种安装使用方法讲的很清楚了。

https://github.com/taoliu/MACS/blob/master/README.rst

MACS软件的具体原理,大家去看文献,或者参考这篇文章

http://www.plob.org/2014/05/08/7227.html

很简单的一个python命令即可安装该软件python setup.py install --user

CHIP-seq第三讲之使用MACS软件寻找peaks752

二.然后准备该软件所需要的数据

是我们在前两篇文章中提到的数据

CHIP-seq第三讲之使用MACS软件寻找peaks786

三.接着运行MACS的命令

/home/jmzeng/.local/bin/macs14 -t Xu_WT_rep2_BAF155.fastq.trimmed.single.bam \

> -c Xu_WT_rep2_Input.fastq.trimmed.single.bam \

> -f BAM -g hs --bw 300 -w -S -n Xu_WT_rep2

CHIP-seq第三讲之使用MACS软件寻找peaks974

四.最后解读一下结果

CHIP-seq第三讲之使用MACS软件寻找peaks987

56K Apr 30 21:54 Xu_WT_rep2_model.r

5.5K Apr 30 22:21 Xu_WT_rep2_negative_peaks.xls

783K Apr 30 22:21 Xu_WT_rep2_peaks.bed

865K Apr 30 22:21 Xu_WT_rep2_peaks.xls

766K Apr 30 22:21 Xu_WT_rep2_summits.bed

唉,反正这也不是我的课题,懒得解释这些结果啦,等后来有机会再慢慢玩吧

 

 

参考 http://www.plob.org/2014/05/08/7227.html

附录:我们现在来了解如何设置参数。

参考自 http://www.plob.org/2014/01/26/7118.html

-t TFILE, –treatment=TFILE 输入文件名

-c CFILE, –control=CFILE 输入阴对文件名

-n NAME, –name=NAME 输入出文件名前缀

-f FORMAT, –format=FORMAT 输入文件格式,默认值为AUTO,可选的值为”BEG”,”ELAND”,”ELANDMULTI”,”ELANDMULTIPER”,”ELANDEXPORT”,”SAM”,”BAM”,”BOWTIE”等。

-g GSIZE, –gsize=GSIZE 比对模板大小。格式可以是:1.0e+9,或者1000000000,也可以缩写:’hs’ for 人类 (2.7e9), ‘mm’ for 大鼠(1.87e9), ‘ce’ for 线虫 (9e7) and ‘dm’ for 果蝇 (1.2e8), 默认值:hs

-s TSIZE, –tsize=TSIZE 设置为短序列的长度,默认值为25

-p PVALE, –pvalue=PVALUE 非峰可能性截取值,默认值为1e-5,这个值不能大太,超过0.9的话,可能无法输出正确的结果

-m MFOLD, –mfold=MFOLD 峰值高度相对于本底的比值,默认值为10,30。也就是说,最低值不能少于10,但比值超过30也不认为它是正常的一个峰。一般而言,低值设置为10是一个很好的区分点。如果这个值还是无法得到满意的结果,那么可以设置得更低,但最好还是使用–nomodel参数,使–nomodel设置为True,然后再传递–shiftsize及–bw参数给MACS。–shiftsize默认值为100,而–bw的默认值为300。

–diag 生成完整报表,会包括是否为真峰的可能性,但会严重拖累运算速度。

 

30

自学CHIP-seq第二讲之过滤数据并比对

这个是有着非常成熟的流程了,我就不细讲了!

我们随机挑选两个文件来跑一下CHIP-seq的流程吧,其中一个是.部分进行免疫共沉淀前的DNA(input DNA)作为空白对照。

5.5G Apr 30 10:31 Xu_WT_rep2_BAF155.fastq

18G Feb 13 20:37 Xu_WT_rep2_Input.fastq

首先进行质量控制,过滤低质量的reads

这里我选取的是DynamicTrim.pl 和

脚本如下

for id in *fastq

do

echo $id

perl DynamicTrim.pl $id

done

接下来

for id in *.trimmed

do

echo $id

perl LengthSort.pl $id

Done

这样就得到了过滤后的reads,可以进行比对啦!

图片1

当然,中间文件可以删掉啦,不然太占空间了,我还只是取了两个数据,要是把这个文章的八个数据都跑完就太纠结了。

然后用bowtie比对

#samtools faidx hg19.fa

#Bowtie2-build hg19.fa hg19

for i in *single

do

bowtie2 -x /home/jmzeng/ref-database/hg19 -U $i -S  $i.sam

samtools view -bS $i.sam> $i.bam

done

输出的bam文件就需要用MASC这个软件来找peak了

30

自学CHIP-seq第一讲之文献解读

我这里选择的CHIP-seq文章题目是

CARM1 Methylates Chromatin Remodeling Factor BAF155 to Enhance Tumor Progression and Metastasis

文章链接http://www.sciencedirect.com/science/article/pii/S1535610813005369

这是2013年的文章,算是蛮新的了,主要探究了CARM1这个基因

然后我简单搜索了一些这个基因的信息

9606 10498 CARM1

- PRMT4

MIM:603934|HGNC:HGNC:23393|

Ensembl:ENSG00000142453|HPRD:09158|Vega:OTTHUMG00000180699

19 19p13.2 coactivator-associated arginine methyltransferase 1

protein-coding CARM1 coactivator-associated arginine methyltransferase histone-arginine methyltransferase CARM1|protein arginine N-methyltransferase 4 20150308

该基因是多种肿瘤相关的转录因子的共激活剂(激活蛋白;转录辅助激活蛋白;转录共同活化子)。

文章作者做了以下四件事

Knockout of CARM1 Using ZFN in Breast Cancer Cells

Identification of BAF155 as a Novel CARM1 Substrate

Methylation of BAF155 Promotes Tumor Growth and Metastasis

Methylated BAF155 Gains Unique Chromatin Association

 

所以就有两种细胞,一种是野生型WT,一种是突变的MUT细胞

然后它们分别做了两个重复,一种是input一种是BAF155免疫测序。

CHIP-seq一定是有一个input对照文件,和一个真正的免疫共沉淀的测序文件。

这样就有八个测序文件。

我们随机挑选两个文件来跑一下CHIP-seq的流程吧,其中一个是.部分进行免疫共沉淀前的DNA(input DNA)作为空白对照。

5.5G Apr 30 10:31 Xu_WT_rep2_BAF155.fastq

18G Feb 13 20:37 Xu_WT_rep2_Input.fastq

然后我随便在网上找了一个生信分析流程

  1. 标准信息分析
    a)   对测序数据进行base calling、raw data 数据整理及数据质量评估;
    b)   去接头污染,去低质量reads和产量情况统计
    c)   Bisulfite 测序序列与参考基因组序列的比对
    d)   深度和覆盖度分析
    e)   C 碱基的甲基化水平
    f)   全基因组甲基化水平分布趋势
  2. g)  全基因组DNA甲基化图谱
  3. h)  差异性甲基化区域(DMR)分析

 

参考

http://www.plob.org/2012/09/29/3760.html

http://www.plob.org/2012/01/09/1605.html

http://www.plob.org/2012/01/08/1538.html