如果你研究癌症，那么TCGA计划的如此丰富的公共数据你肯定不能错过，一般人只能获取到level3的数据，当然，其实一般人也没办法使用level1和level2的数据，毕竟近万个癌症样本的原始测序数据，还是很恐怖的，而且我们拿到原始数据，再重新跑pipeline，其实并不一定比人家TCGA本身分析的要好，所以我们直接拿到分析结果，就足够啦！
五
06
Tag Archives: maf
五
06
突变频谱探究mutation siganures
这也是对TCGA数据的深度挖掘，从而提出的一个统计学概念。文章研究了30种癌症，发现21种不同的mutation signature。如果理解了，就会发现这个其实蛮简单的，他们并不重新测序，只是拿已经有了的TCGA数据进行分析，而且居然是发表在nature上面！
研究了4,938,362 mutations from 7,042 cancers样本，突变频谱的概念只是针对于somatic 的mutation。一般是对癌症病人的肿瘤组织和癌旁组织配对测序，过滤得到的somatic mutation，一般一个样本也就几百个somatic 的mutation。
paper链接是：http://www.nature.com/nature/journal/v500/n7463/full/nature12477.html
从2013年提出到现在，已经有30种mutation siganures，在cosmic数据库有详细记录，更新见：http://cancer.sanger.ac.uk/cosmic/signatures
它的概念就是：根据突变上下文分成96类，然后每类突变的频率不一样画一个条形图，可视化展现。
Each signature is displayed according to the 96 substitution classification defined by the substitution class and sequence context immediately 3′ and 5′ to the mutated base. The probability bars for the six types of substitutions are displayed in different colours.
仔细看paper，还是蛮好理解的，自己写一个脚本就可以做这个分析了，前提是下载各个癌症的somatic mutation文件，一般是maf格式的，很多途径下载。
In principle, all classes of mutation (such as substitutions, indels, rearrangements) and any accessory mutation characteristic, for example, the sequence context of the mutation or the transcriptional strand on which it occurs, can be incorporated into the set of features by which a mutational signature is defined. In the first instance, we extracted mutational signatures using base substitutions and additionally included information on the sequence context of each mutation. Because there are six classes of base substitution—C>A, C>G, C>T, T>A, T>C, T>G (all substitutions are referred to by the pyrimidine of the mutated Watson–Crick base pair)—and as we incorporated information on the bases immediately 5′ and 3′ to each mutated base, there are 96 possible mutations in this classification. This 96 substitution classification is particularly useful for distinguishing mutational signatures that cause the same substitutions but in different sequence contexts.
很多癌症都发现了不止一种mutation signature，甚至高达6种，说明癌症之间差异还是蛮大的！
In most cancer classes at least two mutational signatures were observed, with a maximum of six in cancers of the liver, uterus and stomach. Although these differences may, in part, be attributable to differences in the power to extract signatures, it seems likely that some cancers have a more complex repertoire of mutational processes than others.
Most individual cancer genomes exhibit more than one mutational signature and many different combinations of signatures were observed
但是，我最后也没能绝对的界限是什么，因为总不能用肉眼来看每个突变频谱不一样吧？
The set of signatures will be updated in the future. This will include incorporating additional mutation types (e.g., indels, structural rearrangements, and localized hypermutation such as kataegis) and cancer samples. With more cancer genome sequences and the additional statistical power this will bring, new signatures may be found, the profiles of current signatures may be further refined, signatures may split into component signatures and signatures may be found in cancer types in which they are currently not detected.
分类会持续不断更新，随着更多的cancer type和样本加入，新的signature会被发现，现有的signature也可能会被重新定义，或者被分割成多个小的signature
一
05
使用oncotator做突变注释
功能：vcf格式突变数据进一步注释成maf格式
做过癌症数据分析的童鞋都知道，TCGA里面用maf格式来记录突变！那么maf格式的数据是如何得来的呢，我们都知道，做完snp-calling一般是得到vcf格式的突变记录数据文件，然后再用annovar或者其它蛋白结构功能影响预测软件注释一下，还远达不到maf的近100条记录。
而大名鼎鼎的broad institute就规定了maf格式的突变注释文件，他就是利用了十几个常见的已知数据库来注释我们得到的vcf突变记录，通常是对somatic的突变才注释成maf格式的数据！
大名鼎鼎的broadinstitute出品的突变注释工具：http://www.ncbi.nlm.nih.gov/pubmed/25703262
源码在github： https://github.com/broadinstitute/oncotator
本身也是一个在线工具：
input data数据指南：https://www.broadinstitute.org/oncotator/help/#inputformat
集成了下面所有的分析资源
而且还提供了API
Genomic Annotations
- Gene, transcript, and functional consequence annotations using GENCODE for hg19.
- Reference sequence around a variant.
- GC content around a variant.
- Human DNA Repair Gene annotations from Wood et al.
Protein Annotations
Cancer Variant Annotations
- Observed cancer mutation frequency annotations from COSMIC.
- Cancer gene and mutation annotations from the Cancer GenCensus.
- Overlapping mutations from the Cancer Cell Line Encyclopedia.
- Cancer gene annotations from the Familial Cancer Database.
- Cancer variant annotations from ClinVar.
Non-Cancer Variant Annotations
- Common SNP annotations from dbSNP.
- Variant annotations from 1000 Genomes.
- Variant annotations from NHLBI GO Exome Sequencing Project (ESP).
因为要下载的数据有点多，我这里就不用自己的电脑测试了，安装过程也很简单的！