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Today, I would like to talk to you about how to understand the analysis results in CGA. Many people may not understand it very well. In order to make you understand better, the editor has summarized the following contents for you. I hope you can get something from this article.

TCGA has a unique processing flow for different types of data, as follows

1. DNA-Seq Analysis Pipeline

DNA sequencing in TCGA is mainly used to analyze somatic mutation in tumor patients, which is similar to that of GATK. It goes through a pre-processing step, which is called co-cleanning, and the flow chart is as follows.

This is the classic sort- > markduplicate- > Realign- > BQSR step to get the co-cleaned BAM file. Then the paired tumor and normal samples were used for somatic variant calling to get the VCF file. Then annotate the somatic mutation and get the mutation annotation file MAF, as shown below

In the analysis of somatic mutation sites, the following four different softwares were used to analyze the somatic mutation sites simultaneously.

MuSE

Mutect2

SomaticSniper

Varscan2

The corresponding pipeline is shown below.

The VCF files obtained by their respective pipeline use VEP software to annotate the somatic mutation sites, and use the following database to annotate the mutation sites.

GENCODE v.22

Sift v.5.2.2

ESP v.20141103

Polyphen v.2.2.2

DbSNP v.146

Ensembl genebuild v.2014-07

Ensembl regbuild v.13.0

HGMD public v.20154

ClinVar v.201601

After the annotation is completed, the mutation sites will be filtered to remove low-quality mutation sites and potential germ cell mutation sites, and the remaining sites as the final somatic mutation sites will be saved in the MAF file for download.

Of course, for normal samples that are not paired, there is also a tumor-only variant calling workflow to deal with. For details, please refer to the link below.

Https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline

2. MRNA Analysis Pipeline

MRNA analysis is based on the comparison of hg38 reference genomes by STAR's 2-pass model, and then quantified using HTSeq, which is based on the Gencode V22 version of the GTF file. The process is as follows

In quantification, the following three strategies are provided

Raw count

FPKM

FPKM-UQ

Raw count and FPKM are classical quantitative strategies in transcriptome analysis, while FPKM-UQ is a new strategy based on FPKM. The calculation formula is as follows.

Unlike FPKM, the upper quartile of the number of Mapping reads of all genes is used to replace the total number of Mapping Reads of all genes in FPKM-UQ. The official also provides an example to help us understand the specific calculation process.

3. MiRNA Analysis Pipeline

The analysis of miRNA uses the miRNA quantitative process developed by BCGSC, which is quantitative only for known miRNA. The link is as follows

Https://github.com/bcgsc/mirna

The flow chart is as follows

4. Copy Number Variation Analysis Pipeline

Using Affymetrix SNP 6.0chip to analyze CNV, first use the R packet of DNACopy to calculate the copy number, and then use GISTIC2 to evaluate the gene changes according to CNV, whether it is loss or gain. The flow chart is as follows

5. Methylation Liftover Pipeline

DNA methylation was analyzed by illumina Infinum HumanMethylation 27 and HumanMethylation450 chip platforms, and the quantitative strategy of beta value was adopted. At the same time, considering that the two probes are designed for hg19, compare the probe sequence with hg38, when MAPQ

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