|Hugo_Symbol||HUGO symbol for the gene||TP53|
|Protein_Change||Amino acid change||V600E|
IMHO, not as pretty as cBioPortal's but it gets you close to a solution.
EDIT / SHAMELESS PLUG: After seeing the data available and how easy it'd be, I made my own quick tool to fetch the data and draw the diagram for me in a style similar to cBioPortal - feel free to fork it and add features: https://github.com/pbnjay/lollipops
Example output (w/ labels per the comments)
You will npm in order to be able to install & run the library.
Examples may be found in the snippets folder or also the index.html - The one displayed here below
用my.surv <- surv(OS_MONTHS,OS_STATUS=='DECEASED')构建生存曲线。用kmfit2 <- survfit(my.surv~TUMOR_STAGE_2009)来做某一个因子的KM生存曲线。用 survdiff(my.surv~type, data=dat)来看看这个因子的不同水平是否有显著差异，其中默认用是的logrank test 方法。用coxph(Surv(time, status) ~ ph.ecog + tt(age), data=lung) 来检测自己感兴趣的因子是否受其它因子(age,gender等等)的影响。
step4:Unsupervised hierarchical clustering (1-Spearman distance, average linkage) was performed on the cell lines using the aCGH data.
Putative driver genes of which copy number aberrations correlated to mRNA gene expression were identified to determine subtypes or clusters that are driven by different mechanisms. This was done using Mann Whitney U-test with p<0.05, and Spearman Correlation Coefficient test with Rho >0.6.
step5:We then performed consensus clustering on the gene expression data of the 27 gastric cancer cell lines from CCLE using these putative driver genes. We selected k = 2 as it gives sufficiently stable similarity matrix.
step6: In order to assign new samples to this integrative cluster, significance analysis of microarray (SAM) with threshold q<2.0 was used to generate subtype signature based on the mRNA expression data of the 1762 genes from the 27 gastric cancer cell lines in CCLE.
先用甲基化数据来聚类，得到putative driver genes，然后再用这些基因的表达数据来再次聚类，分成两类，然后对这两类进行SAM找差异基因
结论就是：STK17A is highly expressed in glioma cell lines compared to other cancer types. Data was obtained through the Cancer Cell Line Encyclopedia (CCLE).
Here are a few more, a summary of the other answers, and updated links:
For a much more general discussion of variant calling (not necessarily somatic or limited to SNVs/InDels) check out this thread: What Methods Do You Use For In/Del/Snp Calling?
Some papers describing comparisons of these callers:
The ICGC-TCGA DREAM Mutation Calling challenge has a component on somatic SNV calling.
This paper used validation data to compare popular somatic SNV callers:
You'll need to update the link to MuTect. Broad Institute has begun to put portable versions of their tools on Github, like thelatest release of MuTect. The Genome Institute at WashU has been using Github for a while, but portable versions of their tools can be found here and here.
To rehash/expand on what Dan said, if you're sequencing normal tissue, you generally expect to see single-nucleotide variant sites fall into one of three bins: 0%, 50%, or 100%, depending on whether they're heterozygous or homozygous.
With tumors, you have to deal with a whole host of other factors:
These, and other factors, make calling somatic variants difficult and still an area that is being heavily researched. If someone tells you that somatic variant calling is a solved problem, they probably have never tried to call somatic variants.
Sounds like somatic / tumor variant calling is something that will be solved by improvements at the wet lab side ( single cell selection / amplification / sequencing ) . Rather than at the computational side.
Well, single cell has a role to play (and would have more of one if WGA wasn't so lossy), but realistically, you can't sequence billions of cells from a tumor individually. Bulk sequencing still is going to have a role for quite a while.
Hell germ line calling isn't even a solved problem. Still get lots of false positives (and false negatives). It just tends to work so well that it is hard to improve it much except by making it faster, less memory intensive, etc
Solved was the wrong word. I just meant improved. There is only so much you can do at the computational side. Wet lab also has its part to play.
A germline variant caller generally has a ploidy-based genotyping algorithm built in to part of the algorithm/pipeline. I believe, IIRC, the GATK UnifiedGenotyper for instance does both variant calling and then genotype calling. So to call a genotype for a variant it is expecting a certain number of reads to support the alternative allele. When working with somatic variants all of the assumptions about how many reads you expect with a variant at a position to distinguish between true and false positives are no longer valid. Except for fixed mutations throughout the tumor population only some proportion of cells will hold a somatic variation. You also typically have some contamination from normal non-cancerous cells. Add in complications from significant genomic instability with lots of copy number variations and such and you have a need for a major change in your model for calling variation while minimizing artifactual calls. So you have a host of other programs that have been developed specifically for looking at somatic variation in tumor samples.
High-throughput sequencing is rapidly becoming common practice in clinical diagnosis and cancer research. Many algorithms have been developed for somatic single nucleotide variant (SNV) detection in matched tumor-normal DNA sequencing. Although numerous studies have compared the performance of various algorithms on exome data, there has not yet been a systematic evaluation using PCR-enriched amplicon data with a range of variant allele fractions. The recently developed gold standard variant set for the reference individual NA12878 by the NIST-led “Genome in a Bottle” Consortium (NIST-GIAB) provides a good resource to evaluate admixtures with various SNV fractions.
Using the NIST-GIAB gold standard, we compared the performance of five popular somatic SNV calling algorithms (GATK UnifiedGenotyper followed by simple subtraction, MuTect, Strelka, SomaticSniper and VarScan2) for matched tumor-normal amplicon and exome sequencing data.
Nevertheless, detecting somatic mutations is still challenging, especially for low-allelic-fraction variants caused by tumor heterogeneity, copy number alteration, and sample degradation
We used QIAGEN’s GeneRead DNAseq Comprehensive Cancer Gene Panel (CCP, Version 1) for enrichment and library construction in triplicate。
QIAGEN’s GeneRead DNAseq Comprehensive Cancer Gene Panel (Version 1) was used to amplify the target region of interest (124 genes, 800 Kb).
When analyzing different types of data, use of different algorithms may be appropriate.
DNA samples of NA12878 and NA19129 were purchased from Coriell Institute. Sample mixtures were created based on the actual amplifiable DNA in each sample, resulting in 0%, 8%, 16%, 36%, and 100% of NA12878 sample mixed in the NA19129 sample, respectively.We treated the mixed samples at 8%, 16%, 36%, and 100% as the virtual tumor samples and the 0% as the virtual normal sample.
1. NaiveSubtract — SNVs were called separately from virtual tumor and normal samples using GATK UnifiedGenotyper . For exome sequencing data, reads were already mapped, locally realigned and recalibrated by the 1,000 Genomes Project. So SNVs were directly called on the BAM files using GATK Unified Genotyper. Then, SNVs detected in the virtual normal sample were removed from the list of SNVs detected in the virtual tumor sample, leaving the “somatic” SNVs.
2. MuTect — MuTect is a method developed for detecting the most likely somatic point mutations in NGS data using a Bayesian classifier approach. The method includes pre-processing aligned reads separately in tumor and normal samples and post-processing resulting variants by applying an additional set of filters. We ran MuTect under the High-Confidence mode with its default parameter settings. We disabled the “Clustered position” filter and the “dbSNP filter” for the amplicon sequencing reads, and we disabled the “dbSNP filter” for the exome sequencing.
3. SomaticSniper — SomaticSniper calculates the Bayesian posterior probability of each possible joint genotype across the normal and cancer samples. We tuned the software’s parameters to increase sensitivity and then filtered raw results using a Somatic Score cut-off of 20 to improve specificity.
4. Strelka — Strelka reports the most likely genotype for tumor and normal samples based on a Bayesian probability model. Post-calling filters built into the software are based on factors such as read depth, mismatches, and overlap with indels. We skipped depth filtration for exome and amplicon sequencing data as recommended by the Strelka authors. For the amplicon sequencing reads, we set the minimum MAPQ score at 17 for consistency with the defaults in GATK UnifiedGenotyper. We used variants passing Strelka post-calling filters for analysis.
5. VarScan2 — VarScan2 performs analyses independently on pileup files from the tumor and normal samples to heuristically call a genotype at positions achieving certain thresholds of coverage and quality. Then, sites of the genotypes not matched in tumor and normal samples are classified into somatic, germline, or ambiguous groups using Fisher’s exact test. We generated the pileup files using SAMtools mpileup command.
The compatibility of the output VCF files between different methods as well as the NIST-GIAB gold standard was examined using bcbio.variation tools and manual inspection. The reported SNP call representations between files are comparable to each other.
ACC BLCA BRCA CESC COAD COADREAD DLBC ESCA GBM HNSC KICH KIRC KIRP LAML LGG LIHC LUAD LUSC OV PAAD PANCANCER PANCAN8 PANCAN12 PRAD READ SARC SKCM STAD THCA UCEC UCS
从数以万计的突变里面找到driver mutation这个课题很大，里面的软件我接触的就有十几个了，但是我尝试了其中几个，总是无法运行成功，不知道为什么，终于今天成功了一个，就是mutsig软件！ 其实关于突变数据找driver mutation ，台湾一个大学做了一个数据库DriverDB http://ngs.ym.edu.tw/driverdb/： 还因此发了一篇文章：http://nar.oxfordjournals.org/content/early/2013/11/07/nar.gkt1025.full.pdf，挺不错的！
该nature文章是这样描述这个软件的优点的：We used the most recent version of the MutSig suite of tools, which looks for three independent signals: highmutational burden relative to background expectation, accounting for heterogeneity; clustering of mutations within the gene; and enrichment of mutations in evolutionarily conserved sites. Wecombined the significance levels (P values) fromeach test to obtain a single significance level per gene (Methods).
run_MutSigCV.sh <path_to_MCR> mutations.maf coverage.txt covariates.txt output.txt 即可，其中所有的数据都是可以下载的，
运行完了测试数据， 就证明你的软件安装没有问题啦！如果你只有突变数据的maf格式，maf格式可以参考：https://www.biostars.org/p/69222/ ，也可以使用该软件：如下
run_MutSigCV.sh <path_to_MCR> my_mutations.maf exome_full192.coverage.txt gene.covariates.txt my_results mutation_type_dictionary_file.txt chr_files_hg19
|膀胱，尿路上皮||Bladder urothelial carcinoma||BLCA||412||Browse||Browse|
|乳腺癌||Breast invasive carcinoma||BRCA||1098||Browse||Browse|
|子宫颈||Cervical and endocervical cancers||CESC||307||Browse||Browse|
|淋巴肿瘤弥漫性大B细胞淋巴瘤||Lymphoid Neoplasm Diffuse Large B-cell Lymphoma||DLBC||58||Browse||Browse|
|FFPE试点二期||FFPE Pilot Phase II||FPPP||38||None||Browse|
|头颈部鳞状细胞癌||Head and Neck squamous cell carcinoma||HNSC||528||Browse||Browse|
|泛肾||Pan-kidney cohort (KICH+KIRC+KIRP)||KIPAN||973||Browse||Browse|
|肾透明细胞癌||Kidney renal clear cell carcinoma||KIRC||537||Browse||Browse|
|肾乳头细胞癌||Kidney renal papillary cell carcinoma||KIRP||323||Browse||Browse|
|急性髓系白血病||Acute Myeloid Leukemia||LAML||200||Browse||Browse|
|脑低级神经胶质瘤||Brain Lower Grade Glioma||LGG||516||Browse||Browse|
|肝癌||Liver hepatocellular carcinoma||LIHC||377||Browse||Browse|
|肺鳞状细胞癌||Lung squamous cell carcinoma||LUSC||504||Browse||Browse|
|卵巢浆液性囊腺癌||Ovarian serous cystadenocarcinoma||OV||602||Browse||Browse|
|嗜铬细胞瘤和副神经节瘤||Pheochromocytoma and Paraganglioma||PCPG||179||Browse||Browse|
|皮肤皮肤黑色素瘤||Skin Cutaneous Melanoma||SKCM||470||Browse||Browse|
|胃和食管癌||Stomach and Esophageal carcinoma||STES||628||Browse||Browse|
|睾丸生殖细胞肿瘤||Testicular Germ Cell Tumors||TGCT||150||Browse||Browse|
|子宫内膜癌||Uterine Corpus Endometrial Carcinoma||UCEC||560||Browse||Browse|
文献名：Differential Pathogenesis of Lung Adenocarcinoma Subtypes Involving Sequence Mutations, Copy Number, Chromosomal Instability, and Methylation
Lung adenocarcinoma (LAD)的遗传变异度很大。
这个癌症可以分成三类：The LAD molecular subtypes (Bronchioid, Magnoid, and Squamoid)
1、Gene mutation rates (EGFR, KRAS, STK11, TP53),
3、regional copy number
4、genomewide DNA methylation
1、Patient overall survival,
2、cisplatin plus vinorelbine therapy response
3、predicted gefitinib sensitivity
1，DNA copy number
2，gene sequence mutation
即使是TP53这样的基因在LAD的突变率也才35%，所以我们的LAD应该更加细分，因为EGFR mutation and KRAS mutation这样的突变对治疗很有指导意义，细分更加有助于临床针对性治疗方案的选择。
我们选取了116个LAD样本的数据，分析了1，genome-wide gene expression,,2，genomewide DNA copy number, 3，genome-wide DNA methylation, 4，selected gene sequence mutations
得到的结论是：LAD molecular subtypes correlate with grossly distinct genomic alterations and patient therapy response
Gene expression --> Agilent 44 K microarrays.
DNA copy number --> Affymetrix 250 K Sty and SNP6 microarrays.
DNA methylation --> MSNP microarray assay.
DNA from EGFR, KRAS, STK11 and TP53 exons --> ABI sequencers
我们用的是R语言包 ConsensusClusterPlus根据gene expression 来对我们的LAD进行分类molecular subtypes
分类的基因有506个(the top 25% most variable genes, 3,045, using ConsensusClusterPlus)，A nearest centroid subtype predictor utilizing 506 genes
Bronchioid – excretion genes, asthma genes, and surfactants (SFTPB, SFTPC, SFTPD);
Magnoid – DNA repair genes, such as thymine-DNA glycosylase (TDG);
Squamoid – defense response genes, such as chemokine ligand 10 (CXCL10)
Bronchioid had the most females, nonsmokers, early stage tumors, and low grade tumors, the greatest acinar content, the least necrosis, and the least invasion.
Squamoid had the most high grade tumors, the greatest solid content, and the lowest papillary content.
Magnoid had themost smokers and the heaviest smokers by pack years.
Bronchioid had the greatest EGFR mutation frequency
Magnoid had the greatest mutation frequencies in TP53, KRAS and STK11.
为了研究不同亚型癌症的突变模式的不同（genomewide mutation rates），我们同时又研究了a large set of rarely mutated genes (n = 623) from the Ding et al. cohort
Bronchioid subtype 更有可能受益于EGFR inhibitory therapy
Magnoid tumors also have severe genomic alterations including the greatest CIN, the most regional CN alterations, DNA hypermethylation, and the greatest genomewide mutation rate.
the Squamoid subtype displayed the fewest distinctive alterations that included only regional CN alterations