MSIsensor-ct is a novel method based machine learning, which can accurately determine the MSI status of cfDNA sequencing data with ultra-low ctDNA content. In practice, we recommend aiming for a minimum of 30 valid microsatellites to classify the MSI status using MSIsensor-ct. MSIsensor (https://github.com/niu-lab/msisensor) is a viable option if paired tumor and normal sequencing data are available, and MSIsensor2 (https://github.com/niu-lab/msisensor2) is effective for tumor-only samples.
- Xinyin Han, Shuying Zhang, Daniel Cui Zhou, Dongliang Wang, Xiaoyu He, Danyang Yuan, Ruilin Li, Jiayin He, Xiaohong Duan, Michael C Wendl, Li Ding, Beifang Niu, MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data, Briefings in Bioinformatics, 2021. https://doi.org/10.1093/bib/bbaa402.
- Beifang Niu, Kai Ye, Qunyuan Zhang, Charles Lu, Mingchao Xie, Michael D. McLellan, Michael C. Wendl, Li Ding, MSIsensor: microsatellite instability detection using paired tumor-normal sequence data, Bioinformatics, 2014. https://doi.org/10.1093/bioinformatics/btt755.
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High sensitivity and specificity
For detecting MSI status in cfDNA sequencing data, MSIsensor-ct reached 100% sensitivity and specificity in our genuine samples and 17 simulation datasets.
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A stable threshold
According to the robustness test, if a panel contains 30 microsatellites overlapped with 1,476 site-classifiers in MSIsensor-ct, the AUC for MSI calling can attain up to 0.99. In addition, no matter how many sites overlapped with our classifiers, MSIscore = 20% can always be a potential stable threshold to distinguish MSI and MSS samples.
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Suitable for extra-low ctDNA content
The limitation test demonstrated that MSIsensor-ct accurately detected the MSI status on cfDNA samples with the sequencing depth of 3,000× and ctDNA content at the level of 0.05%.
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User-friendly installation and operation process
MSIsensor-ct requires only BAM files as input and is free from additional baseline establishment, which is user-friendly and can be flexibly integrated into the routine next generation sequencing analysis.
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
Version 0.1
Usage: msisensor-ct <command> [options]
Key commands:
msi msi scoring
msisensor-ct msi [options]:
-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
MSI scoring:
hg38 bam:
msisensor-ct msi -D -M ./models_hg38 -t ./test/example.cfdna.hg38.bam -o output.prefix
hg19 or GRCh37 bam:
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
The MSI scoring step produces 3 files:
output.prefix
output.prefix_dis
output.prefix_somatic
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output.prefix: msi score output
Total_Number_of_Sites Number_of_Somatic_Sites % 2 2 100.00
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output.prefix_dis: read count distribution (T: tumor)
chr22 29286892 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
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output.prefix_somatic: somatic sites detected
chromosome location left_flank repeat_times repeat_unit_bases right_flank discrimination_value_ML chr22 29286892 AAAGC 12 T CTCTT 0.98852
We provided one small dataset to test the msi scoring step:
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:
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]; Xinyin Han [email protected]; Shuying Zhang [email protected].