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基因组生信分析教程

DNA 1. Germline Mutation Vs. Somatic Mutation 傻傻分不清楚

DNA 2. SCI 文章中基因组变异分析神器之 maftools

DNA 3. SCI 文章中基因组变异分析神器之 maftools

DNA 4. SCI 文章中基因组的突变信号（maftools）

DNA 5. 基因组变异文件VCF格式详解

DNA 6. 基因组变异之绘制精美瀑布图（ComplexHeatmap）

DNA 7. 基因组拷贝数变异分析及可视化 (GISTIC2.0)

DNA 8. 癌症的突变异质性及寻找新的癌症驱动基因(MutSigCV)

DNA 9. 揭秘肿瘤异质性与TMB, MSI之间的相关性

DNA 10. 识别癌症驱动基因 (OncodriveCLUST)

DNA 11. 识别肿瘤蛋白质三维结构上突变热点(HotSpot3D)

DNA 12. SCI 文章绘图之全基因组关联分析可视化(GWAS)

DNA 13. SCI 文章肿瘤突变负荷计算方法（TMB）

DNA 14. SCI 文章肿瘤微卫星不稳定性计算方法（MSI）

今天介绍肿瘤微卫星不稳定性的计算软件（MSIsensor系列），涉及到4款软件，也是一种迭代升级，针对配对实体瘤，无对照的实体瘤，以及cfDNA的MSI score 检测，下面就给大家介绍一下。

1. MSIsensor

微卫星不稳定性(MSI)是大基因组不稳定性的重要指标，与包括林奇综合征在内的许多遗传疾病有关。MSI状态也是多种癌症类型(如结直肠癌和子宫内膜癌)良好生存的独立预后因素，还提示化疗药物的选择。然而，目前基于pcr -电泳的检测过程既费力又耗时，通常需要目测来对样品进行分类。开发了MSIsensor，一个c++程序，用于自动检测体细胞微卫星的变化。计算配对肿瘤和正常序列数据中每个位点微卫星的长度分布，随后使用这些数据对两个样本中观察到的分布进行统计比较。综合测试表明，MSIsensor是一种从标准肿瘤-正常配对序列数据中获得MSI状态的有效工具。

使用说明：

Install

You may already have these prerequisite packages. If not, and you're on Debian or Ubuntu:

sudo apt-get install zlib1g-dev libncurses5-dev libncursesw5-dev

讯享网

If you are using Fedora, CentOS or RHEL, you'll need these packages instead:

讯享网sudo yum install zlib-devel ncurses-devel ncurses

Using Pre-built

• For Linux and OSX binaries, look for msisensor.linux and/or msisensor.macos in attachments to each release

Using bioconda

conda install msisensor

Build from source code

Clone the msisensor master branch, and build the msisensor binary:

讯享网git clone https://github.com/ding-lab/msisensor.git cd msisensor make

Now you can put the resulting binary where your $PATH can find it. If you have su permissions, then we recommend dumping it in the system directory for locally compiled packages:

sudo mv msisensor /usr/local/bin/

Usage

讯享网Version 0.6 Usage: msisensor <command> [options]

Key commands:

scan scan homopolymers and miscrosatelites msi msi scoring

msisensor scan [options]:

讯享网-d <string> reference genome sequences file, *.fasta format -o <string> output homopolymer and microsatelittes file -l <int> minimal homopolymer size, default=5 -c <int> context length, default=5 -m <int> maximal homopolymer size, default=50 -s <int> maximal length of microsate, default=5 -r <int> minimal repeat times of microsate, default=3 -p <int> output homopolymer only, 0: no; 1: yes, default=0 -h help

msisensor msi [options]:

-d <string> homopolymer and microsates file -n <string> normal bam file -t <string> tumor bam file -o <string> output distribution file -e <string> bed file, optional -f <double> FDR threshold for somatic sites detection, default=0.05 -c <int> coverage threshold for msi analysis, WXS: 20; WGS: 15, default=20 -z <int> coverage normalization for paired tumor and normal data, 0: no; 1: yes, default=0 -r <string> choose one region, format: 1:- -l <int> minimal homopolymer size, default=5 -p <int> minimal homopolymer size for distribution analysis, default=10 -m <int> maximal homopolymer size for distribution analysis, default=50 -q <int> minimal microsates size, default=3 -s <int> minimal microsates size for distribution analysis, default=5 -w <int> maximal microstaes size for distribution analysis, default=40 -u <int> span size around window for extracting reads, default=500 -b <int> threads number for parallel computing, default=1 -x <int> output homopolymer only, 0: no; 1: yes, default=0 -y <int> output microsatellite only, 0: no; 1: yes, default=0 -h help

Example

1. Scan microsatellites from reference genome:

   讯享网msisensor scan -d reference.fa -o microsatellites.list

2. MSI scoring:

   msisensor msi -d microsatellites.list -n normal.bam -t tumor.bam -e bed.file -o output.prefix

   Note: normal and tumor bam index files are needed in the same directory as bam files

Output

The list of microsatellites is output in "scan" step. The MSI scoring step produces 4 files:

讯享网output.prefix output.prefix_dis_tab output.prefix_germline output.prefix_somatic

1. microsatellites.list: microsatellite list output ( columns with *_binary means: binary conversion of DNA bases based on A=00, C=01, G=10, and T=11 )

   chromosome location repeat_unit_length repeat_unit_binary repeat_times left_flank_binary right_flank_binary repeat_unit_bases left_flank_bases right_flank_bases 1 10485 4 149 3 150 685 GCCC AGCCG GGGTC 1 10629 2 9 3 258 409 GC CAAAG CGCGC 1 10652 2 2 3 665 614 AG GGCGC GCGCG 1 10658 2 9 3 546 409 GC GAGAG CGCGC 1 10681 2 2 3 665 614 AG GGCGC GCGCG

2. output.prefix: msi score output

   讯享网Total_Number_of_Sites Number_of_Somatic_Sites % 640 75 11.72

3. output.prefix_dis_tab: read count distribution (N: normal; T: tumor)

   1  ACCTC 11 T AAAGG N 0 0 0 0 1 38 0 0 0 0 0 0 0 1  ACCTC 11 T AAAGG T 0 0 0 0 17 22 1 0 0 0 0 0 0

4. output.prefix_somatic: somatic sites detected ( FDR: false discovery rate )

   讯享网chromosome location left_flank repeat_times repeat_unit_bases right_flank difference P_value FDR rank 1  TAAGA 10 T CTTGT 0.55652 2.8973e-15 1.8542e-12 1 1  TTTAC 14 T AAGGT 0.82764 5.1515e-15 1.6485e-12 2 1  CCAGG 21 A GATGA 0.80556 1e-14 2.1333e-12 3 1  GTTTG 13 A CAGCT 0.8653 1e-14 1.6e-12 4 1  TTCTC 11 T CCCCT 0.84682 1e-14 1.28e-12 5 1  GTGGT 14 A GAAAA 0.57471 1e-14 1.0667e-12 6 1  TGGAA 14 T GAGTC 0.66023 1e-14 9.1429e-13 7 1  TAGAG 16 A GGAAA 0.53141 1e-14 8e-13 8 1  CTAAC 14 T CTTTT 0.76286 1e-14 7.1111e-13 9

5. output.prefix_germline: germline sites detected

   chromosome location left_flank repeat_times repeat_unit_bases right_flank genotype 1  AATAC 11 A TTAGC 5|5 1  CTGCC 5 AG CACAG 5|5 1  AATAC 12 A TTAGC 5|5 1  AAAAG 14 A GAAAA 1|1 1  TTTTC 11 T CTTTT 1|1

Test sample

We provided one small dataset (tumor and matched normal bam files) to test the msi scoring step:

讯享网cd ./test bash run.sh

We also provided a R script to visualize MSI score distribution of MSIsensor output. ( msi score list only or msi score list accompanied with known msi status). For msi score list only as input:

R CMD BATCH "--args msi_score_only_list msi_score_only_distribution.pdf" plot.r

For msi score list accompanied with known msi status as input:

讯享网R CMD BATCH "--args msi_score_and_status_list msi_score_and_status_distribution.pdf" plot.r

2. MSIsensor2

MSIsensor2专门对单样本进行微卫星检测。而且，MSIsensor2 号称同时适用于cfDNA样本和FFPE样本。输入文件需要准备 比对后的 BAM 文件即可。

使用说明：

MSIsensor2 下载和安装:

git clone https://github.com/niu-lab/msisensor2.git cd msisensor2 chmod +x msisensor2

MSIsensor2使用:

讯享网Version 0.1 Usage:  msisensor2 <command> [options]

msisensor2 msi [options]:

讯享网-M <string> models directory for tumor only data -t <string> tumor bam file -o <string> output distribution file -c <int> coverage threshold for msi analysis, WXS: 20; WGS: 15, default=20 -b <int> threads number for parallel computing, default=1 -x <int> output homopolymer only, 0: no; 1: yes, default=0 -y <int> output microsatellite only, 0: no; 1: yes, default=0 -h help

Example

计算MSI scoring: 只有一个肿瘤的bam数据，参考基因组为hg38。

注意:bam索引文件需要与bam文件在同一个目录中

msisensor2 msi -M ./models_hg38 -t ./test/example.tumor.only.hg38.bam -o output.tumor.prefix

hg19 or GRCh37 bam:

讯享网msisensor2 msi -M ./models_hg19_GRCh37 -t ./test/example.tumor.only.hg19.bam -o output.tumor.prefix

b37 or humanG1Kv37 bam:

msisensor2 msi -M ./models_b37_HumanG1Kv37 -t ./test/example.tumor.only.b37.bam -o output.tumor.prefix

for tumor only input, the MSI scoreing step produces 3 files:

讯享网output.tumor.prefix output.tumor.prefix_dis output.tumor.prefix_somatic

1. output.prefix: msi score 

Total_Number_of_Sites Number_of_Somatic_Sites % 2 1 50.00

讯享网chr22  AAAGC 12[T] CTCTT  T: 0 0 0 0 0 0 0 0 25 71 4 86 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3. output.prefix_somatic: somatic sites detected

chromosome location left_flank repeat_times repeat_unit_bases right_flank discrimination_value_ML chr22  AAAGC 12 T CTCTT 0.98852

我们提供了一个小数据集(只有肿瘤的bam文件)来测试msi评分步骤:

讯享网msisensor2 msi -M ./models_hg38 -t ./test/example.tumor.only.hg38.bam -o output.tumor.prefix msisensor2 msi -M ./models_hg19 -t ./test/example.tumor.only.hg19.bam -o output.tumor.prefix

我们还提供了一个R脚本来可视化MSIsensor2输出的MSI分数分布。(只有msi分数列表或msi分数列表附带已知的msi状态)。对于msi分数列表仅作为输入:

R CMD BATCH "--args msi_score_only_list msi_score_only_distribution.pdf" plot.r

对于msi分数列表，并将已知的msi状态作为输入:

讯享网R CMD BATCH "--args msi_score_and_status_list msi_score_and_status_distribution.pdf" plot.r

3. MSIsensor-ct

微卫星不稳定性(MSI)是一种很有前途的癌症预后和化学敏感性的生物标志物。从肿瘤-正常配对或仅肿瘤测序数据检测MSI的技术正在迅速发展。然而，肿瘤组织往往是不足的，不可用的，或以其他方式难以获得。越来越多的临床证据表明，血浆循环细胞游离DNA (cfNDA)技术作为一种无创MSI检测方法的巨大潜力。结果:我们开发了基于机器学习协议的生物信息学工具MSIsensor-ct，致力于使用cfDNA测序数据检测MSI状态，潜在稳定的MSI评分阈值为20%。MSIsensor-ct在不同水平的循环肿瘤DNA (ctDNA)和测序深度的独立检测数据集上的评估显示，在0.05% ctDNA含量的检测限(LOD)内，准确率为100%。MSIsensor-ct只需要BAM文件作为输入，使其用户友好，易于集成到下一代测序(NGS)分析管道。https://github.com/niu-lab/MSIsensor-ct 免费获得

使用说明：

Install

Currently, MSIsensor-ct is based on Linux system, and we provide binaries only. Please note your GCC version should be at least 5.0.x.

git clone https://github.com/niu-lab/msisensor-ct.git cd msisensor-ct chmod +x msisensor-ct

Usage

讯享网Version 0.1 Usage: msisensor-ct <command> [options]

Key commands:

msi msi scoring

msisensor-ct msi [options]:

讯享网-D <boolean> activate processing for ctDNA samples -M <string> models directory for tumor only data -t <string> tumor bam file -o <string> output distribution file -c <int> coverage threshold for msi analysis, WXS: 20; WGS: 15, default=20 -b <int> threads number for parallel computing, default=1 -x <int> output homopolymer only, 0: no; 1: yes, default=0 -y <int> output microsatellite only, 0: no; 1: yes, default=0 -h help

Example

MSI scoring:

hg38 bam:

msisensor-ct msi -D -M ./models_hg38 -t ./test/example.cfdna.hg38.bam -o output.prefix

hg19 or GRCh37 bam:

讯享网msisensor-ct msi -D -M ./models_hg19_GRCh37 -t ./test/example.cfdna.hg19.bam -o output.prefix

b37 or HumanG1Kv37 bam:

msisensor-ct msi -D -M ./models_b37_HumanG1Kv37 -t ./test/example.cfdna.b37.bam -o output.prefix

Note: bam index files are needed in the same directory as bam files

Output

The MSI scoring step produces 3 files:

讯享网output.prefix output.prefix_dis output.prefix_somatic

1. output.prefix: msi score output

   Total_Number_of_Sites Number_of_Somatic_Sites % 2 2 100.00

2. output.prefix_dis: read count distribution (T: tumor)

   讯享网chr22  AAAGC 12[T] CTCTT T: 0 0 0 0 0 0 0 0 25 71 4 86 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3. output.prefix_somatic: somatic sites detected

   chromosome location left_flank repeat_times repeat_unit_bases right_flank discrimination_value_ML chr22  AAAGC 12 T CTCTT 0.98852

Test sample

We provided one small dataset to test the msi scoring step:

讯享网msisensor-ct msi -D -M ./models_hg38 -t ./test/example.cfdna.hg38.bam -o output.prefix msisensor-ct msi -D -M ./models_hg19_GRCh37 -t ./test/example.cfdna.hg19.bam -o output.prefix msisensor-ct msi -D -M ./models_b37_HumanG1Kv37 -t ./test/example.cfdna.b37.bam -o output.prefix

We also provided a R script to visualize MSI score distribution of MSIsensor-ct output. ( msi score list only or msi score list accompanied with known msi status).

For msi score list only as input:

R CMD BATCH "--args msi_score_only_list msi_score_only_distribution.pdf" plot.r

For msi score list accompanied with known msi status as input:

讯享网R CMD BATCH "--args msi_score_and_status_list msi_score_and_status_distribution.pdf" plot.r

4. MSIsensor-pro

微卫星不稳定性(MSI)是癌症治疗和预后的重要生物标志物。传统的实验分析既费力又耗时，而下一代基于测序的计算方法不适用于白血病样本、石蜡包埋样本或其他疾病患者来源的异种移植物/类器官，因为需要匹配的正常样本。在此,

开发了 MSIsensor-pro，这是一个开源的单样本MSI评分方法临床应用。MSIsensor-pro 引入了一个多项分布模型来量化每个肿瘤样本的聚合酶滑移，并引入了一种判别位点选择方法来实现MSI未匹配正常样品的检测。我们证明了mssensor -pro是一个超快的，准确、鲁棒的MSI调用方法。使用不同测序深度和肿瘤的样本在纯度方面，mssensor -pro在准确度方面均显著优于目前领先的方法计算成本。MSIsensor-pro可在https://github.com/xjtu-omics/msisensor-pro上获得。

使用说明：

Install

Directly using binary version

wget https://github.com/xjtu-omics/msisensor-pro/raw/master/binary/msisensor-pro chmod +x msisensor-pro export PATH=`pwd`:$PATH

Install Using Docker

讯享网docker pull pengjia1110/msisensor-pro docker run pengjia1110/msisensor-pro msisensor-pro

Install Using Bioconda

conda install msisensor-pro

Install from source code

( Recommended For Developers )

Install the dependencies

Dependent packages including zlib, ncurses and nurses-dev are required for MSIsensor-pro. You may already have these prerequisite packages. If not, you need to run the following code to obtain dependent packages.

• For Debian or Ubuntu:

  讯享网sudo apt-get install libbz2-dev zlib1g-dev libcurl4-openssl-dev libncurses5-dev libncursesw5-dev

• For Fedora, CentOS or RHEL

  sudo yum install bzip2-devel xz-devel zlib-devel ncurses-devel ncurses

Build MSIsensor-pro from source code

• colne the repository from our github

  讯享网git clone https://github.com/xjtu-omics/msisensor-pro

• make

  cd msisensor-pro/ ./INSTALL

• install

  讯享网sudo mv msisensor-pro /usr/local/bin/

Usage:

msisensor-pro <command> [options]

Key Commands:

• scan

  讯享网scan the reference genome to get microsatellites information

• baseline

  build baseline for tumor only detection

• msi

  讯享网evaluate MSI using paired tumor-normal sequencing data

• pro

  evaluate MSI using single (tumor) sample sequencing data

See more detail in the Key Commands page and Best Practices page.

Best Practices for MSI classification using MSIsensor-pro

(a) For tumor only samples:

1. scan : scan the reference genome to get microsatellites information

讯享网msisensor-pro scan -d /path/to/reference.fa -o /path/to/reference.list

This module scans the reference genome to get microsatellites information. You need to input (-d) a reference file (*.fa or *.fasta), and you will get a microsatellites file (-o) for following analysis.

2. baseline : build baseline for tumor only detection

msisensor-pro baseline -d /path/to/reference.list -i /path/to/configure.txt -o /path/to/baseline/directory

This module builds baseline for the input microsatellites (-d) from the scan module output or our github. You also need to offer some normal sample sequence data (-i，click here for more detail about configure file) from the sample sequencing center or platform and the output directory (-o).

3. pro : evaluate MSI using single (tumor) sample sequencing data

讯享网msisensor-pro pro -d /path/to/baseline/directory/reference_baseline.list -t /path/to/tumor/case1_sorted.bam -o /path/to/output

This module scores the MSI using the tumor only sequence data. You need to offer the microsatellites with baseline (-d) from the baseline module, the aligned sequencing file (-t) and the output prefix (-o).

(b) For tumor-normal paired samples:

1. scan : scan the reference genome to get microsatellites information

msisensor-pro scan -d /path/to/reference.fa -o /path/to/reference.site

This module scans the reference genome to get microsatellites information. You need to input (-d) a reference file (*.fa or *.fasta), and you will get a microsatellites file (-o) for following analysis.

2. msi : evaluate MSI using paired tumor-normal sequencing data

讯享网msisensor-pro msi -d /path/to/reference.site -n /path/to/case1_normal_sorted.bam -t /path/to/case1_tumor_sorted.

References:

1. Niu B, Ye K, Zhang Q, et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics. 2014;30(7):1015-1016. doi:10.1093/bioinformatics/btt755

2. Han X, Zhang S, Zhou DC, et al. MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data. Brief Bioinform. 2021;22(5):bbaa402. doi:10.1093/bib/bbaa402

3. Jia P, Yang X, Guo L, et al. MSIsensor-pro: Fast, Accurate, and Matched-normal-sample-free Detection of Microsatellite Instability. Genomics Proteomics Bioinformatics. 2020;18(1):65-71. doi:10.1016/j.gpb.2020.02.001

MSIsensor 系列检测 MSI的软件整理完成，其实使用起来并不难，输入文件都是 比对后的 BAM 文件，需要对其进行去重和排序，例外就是需要注意MSI 检测输入的基因组序列必须跟比对时使用的基因组序列版本一致，否是就会出现不停的报错，这点需要大家注意下，其他问题不大！

桓峰基因，铸造成功的您！

未来桓峰基因公众号将不间断的推出单细胞系列生信分析教程，

敬请期待！！

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