MSIsensor is a C++ program to detect replication slippage variants at microsatellite regions, and differentiate them as somatic or germline. Given paired tumor and normal sequence data, it builds a distribution for expected (normal) and observed (tumor) lengths of repeated sequence per microsatellite, and compares them using Pearson's Chi-Squared Test. Comprehensive testing indicates MSIsensor is an efficient and effective tool for deriving MSI status from standard tumor-normal paired sequence data. Since there are many users complained that they don't have paired normal sequence data or related normal sequence data can be used to build a paired normal control, we released MSIsensor2 and MSIsensor-pro, and which are specially designed for MSI detection using tumor only or ctDNA sequencing data.
MSIsensor-pro is an updated version of msisensor. MSIsensor-pro evaluates MSI for cancer patients with next generation sequencing data. It accepts the whole genome sequencing, whole exome sequencing and target region (panel) sequencing data as input. MSIsensor-pro introduces a multinomial distribution model to quantify polymerase slippages for each tumor sample and a discriminative sites selection method to enable MSI detection without matched normal samples. For samples of various sequencing depths and tumor purities, MSIsensor-pro significantly outperformed the current leading methods in terms of both accuracy and computational cost. If you want to know more detail about MSIsensor-pro, please see out research paper on Genomics Proteomics & Bioinformatics.
Our test results show that the performance of MSIsensor2 is comparable with paired tumor and normal sequence data input. In particular, the MSIsensor2 is 10 times faster than the MSIsensor. A typical WES data can be finished within 180 seconds (test on both hg19 and hg38 bams). Please try the MSIsensor2 here: https://github.com/niu-lab/msisensor2 or require any further details here: http://niulab.scgrid.cn/msisensor2/index.html .
If you used this tool for your work, please cite PMID 24371154
Beifang Niu*, Kai Ye*, Qunyuan Zhang, Charles Lu, Mingchao Xie, Michael D. McLellan, Michael C. Wendl and Li Ding#.MSIsensor: microsatellite instability detection using paired tu-mor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).
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
If you are using Fedora, CentOS or RHEL, you'll need these packages instead:
sudo yum install zlib-devel ncurses-devel ncurses
- For Linux and OSX binaries, look for
msisensor.linuxand/ormsisensor.macosin attachments to each release
conda install msisensor
Clone the msisensor master branch, and build the msisensor binary:
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/
Version 0.6
Usage: msisensor <command> [options]
Key commands:
scan scan homopolymers and miscrosatelites
msi msi scoring
msisensor scan [options]:
-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: 1:10000000-20000000
-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
-
Scan microsatellites from reference genome:
msisensor scan -d reference.fa -o microsatellites.list -
MSI scoring:
msisensor msi -d microsatellites.list -n normal.bam -t tumor.bam -e bed.file -o output.prefixNote: normal and tumor bam index files are needed in the same directory as bam files
The list of microsatellites is output in "scan" step. The MSI scoring step produces 4 files:
output.prefix
output.prefix_dis_tab
output.prefix_germline
output.prefix_somatic
-
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 -
output.prefix: msi score output
Total_Number_of_Sites Number_of_Somatic_Sites % 640 75 11.72 -
output.prefix_dis_tab: read count distribution (N: normal; T: tumor)
1 16248728 ACCTC 11 T AAAGG N 0 0 0 0 1 38 0 0 0 0 0 0 0 1 16248728 ACCTC 11 T AAAGG T 0 0 0 0 17 22 1 0 0 0 0 0 0 -
output.prefix_somatic: somatic sites detected ( FDR: false discovery rate )
chromosome location left_flank repeat_times repeat_unit_bases right_flank difference P_value FDR rank 1 16200729 TAAGA 10 T CTTGT 0.55652 2.8973e-15 1.8542e-12 1 1 75614380 TTTAC 14 T AAGGT 0.82764 5.1515e-15 1.6485e-12 2 1 70654981 CCAGG 21 A GATGA 0.80556 1e-14 2.1333e-12 3 1 65138787 GTTTG 13 A CAGCT 0.8653 1e-14 1.6e-12 4 1 35885046 TTCTC 11 T CCCCT 0.84682 1e-14 1.28e-12 5 1 75172756 GTGGT 14 A GAAAA 0.57471 1e-14 1.0667e-12 6 1 76257074 TGGAA 14 T GAGTC 0.66023 1e-14 9.1429e-13 7 1 33087567 TAGAG 16 A GGAAA 0.53141 1e-14 8e-13 8 1 41456808 CTAAC 14 T CTTTT 0.76286 1e-14 7.1111e-13 9 -
output.prefix_germline: germline sites detected
chromosome location left_flank repeat_times repeat_unit_bases right_flank genotype 1 1192105 AATAC 11 A TTAGC 5|5 1 1330899 CTGCC 5 AG CACAG 5|5 1 1598690 AATAC 12 A TTAGC 5|5 1 1605407 AAAAG 14 A GAAAA 1|1 1 2118724 TTTTC 11 T CTTTT 1|1
We provided one small dataset (tumor and matched normal bam files) to test the msi scoring step:
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:
R CMD BATCH "--args msi_score_and_status_list msi_score_and_status_distribution.pdf" plot.r
If you have any questions, please contact one or more of the following folks: Beifang Niu [email protected] Kai Ye [email protected] Li Ding [email protected]