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View Code? Open in Web Editor NEWMicrosatellite Instability (MSI) detection using high-throughput sequencing data.
License: Other
Microsatellite Instability (MSI) detection using high-throughput sequencing data.
License: Other
Hi, when I used conda to install this software, this error "PackagesNotFoundError: The following packages are not available from current channels: - msisensor-pro". How could I solve it?
Hi, when I used conda to install this software, this error "PackagesNotFoundError: The following packages are not available from current channels: - msisensor-pro". How could I solve it?
Hi,
We are running MSISensor version 0.60 for some clinical trials using whole exome sequencing (WES) data.
We are using deduplicated BAM files as input. All other parameters set to default.
We noticed that we had significant differences in coverage between tumor-normal (higher coverage in tumor) reads thus, we turned on the normalization parameter.
After normalization, we noticed that the MSI Percent scores were magnitudes higher compared to the non-normalized version across all samples.
Is there an explanation for this? Is MSISensor 0.60 weighting rather than normalizing. We were not expecting the scores to have this drastic increase.
Thank you very much for all your help and developing MSISensor. Appreciate it!
Thanks, Fayaz.
Hi,
I have the Apple M1 chip (if it helps) and tried the following ways to install the tool:
exec format error
:➜ tools wget https://github.com/xjtu-omics/msisensor-pro/raw/master/binary/msisensor-pro
--2022-08-02 15:23:02-- https://github.com/xjtu-omics/msisensor-pro/raw/master/binary/msisensor-pro
Resolving github.com (github.com)... 140.82.114.4
Connecting to github.com (github.com)|140.82.114.4|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/xjtu-omics/msisensor-pro/master/binary/msisensor-pro [following]
--2022-08-02 15:23:02-- https://raw.githubusercontent.com/xjtu-omics/msisensor-pro/master/binary/msisensor-pro
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 214512 (209K) [application/octet-stream]
Saving to: ‘msisensor-pro’
msisensor-pro 100%[===========================================================>] 209.48K 1.30MB/s in 0.2s
2022-08-02 15:23:02 (1.30 MB/s) - ‘msisensor-pro’ saved [214512/214512]
➜ tools chmod +x msisensor-pro
➜ tools export PATH=`pwd`:$PATH
➜ ~ msisensor-pro pro --help
zsh: exec format error: msisensor-pro
PackagesNotFoundError
:(base) ➜ tools conda install msisensor-pro
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
PackagesNotFoundError: The following packages are not available from current channels:
- msisensor-pro
Current channels:
- https://repo.anaconda.com/pkgs/main/osx-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/r/osx-64
- https://repo.anaconda.com/pkgs/r/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
➜ tools conda install -c bioconda msisensor-pro
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /Users/rathik/miniconda/envs/myenv
added / updated specs:
- msisensor-pro
The following packages will be downloaded:
package | build
---------------------------|-----------------
llvm-openmp-12.0.0 | h0dcd299_1 262 KB
msisensor-pro-1.1.a | h4cb2ced_1 136 KB bioconda
ncurses-6.2 | h0a44026_1 749 KB
------------------------------------------------------------
Total: 1.1 MB
The following NEW packages will be INSTALLED:
libcxx pkgs/main/osx-64::libcxx-12.0.0-h2f01273_0
llvm-openmp pkgs/main/osx-64::llvm-openmp-12.0.0-h0dcd299_1
msisensor-pro bioconda/osx-64::msisensor-pro-1.1.a-h4cb2ced_1
ncurses pkgs/main/osx-64::ncurses-6.2-h0a44026_1
zlib pkgs/main/osx-64::zlib-1.2.12-h4dc903c_2
Proceed ([y]/n)? y
Downloading and Extracting Packages
llvm-openmp-12.0.0 | 262 KB | ########################################################################################### | 100%
msisensor-pro-1.1.a | 136 KB | ########################################################################################### | 100%
ncurses-6.2 | 749 KB | ########################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(myenv) ➜ tools msisensor-pro scan
dyld[70247]: Library not loaded: @rpath/libtinfo.6.dylib
Referenced from: /Users/rathik/miniconda/envs/myenv/bin/msisensor-pro
Reason: tried: '/Users/rathik/miniconda/envs/myenv/bin/../lib/libtinfo.6.dylib' (no such file), '/Users/rathik/miniconda/envs/myenv/bin/../lib/libtinfo.6.dylib' (no such file), '/usr/local/lib/libtinfo.6.dylib' (no such file), '/usr/lib/libtinfo.6.dylib' (no such file)
msisensor-pro
. The installation is fine but there is another error where it cannot find the reference file:➜ msi-sensor-test docker pull pengjia1110/msisensor-pro
Using default tag: latest
latest: Pulling from pengjia1110/msisensor-pro
Digest: sha256:d8c8a5bfcc8f5f9e03811ef05915edd61b851951710c7b6eab87ac368c189505
Status: Image is up to date for pengjia1110/msisensor-pro:latest
docker.io/pengjia1110/msisensor-pro:latest
➜ reference pwd
/Users/rathik/Projects/msi-sensor-test/data/reference
➜ reference ls -lt
total 6347808
-rw-r--r--@ 1 rathik 1768498755 160928 Aug 2 16:04 Homo_sapiens_assembly38.fasta.fai
-rw-r--r--@ 1 rathik 1768498755 3249912778 Aug 2 15:55 Homo_sapiens_assembly38.fasta
➜ reference docker run pengjia1110/msisensor-pro msisensor-pro scan -d Homo_sapiens_assembly38.fasta
WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested
scan -d Homo_sapiens_assembly38.fasta Start at: Tue Aug 2 20:35:09 2022
fatal error: failed to open ref file
illegal hardware instruction
error:# fixed the above error this using
conda install -y conda-forge::ncurses
conda install msisensor-pro
(myenv) ➜ reference msisensor-pro scan -d Homo_sapiens_assembly38.fasta -o reference.list
scan -d Homo_sapiens_assembly38.fasta -o reference.list Start at: Tue Aug 2 16:47:35 2022
[1] 77540 illegal hardware instruction msisensor-pro scan -d Homo_sapiens_assembly38.fasta -o reference.list
When I do the scanning on my genome (Hg19) and I set the minimum length of the homopolymer markers to 5, it finishes, but when I try to generate the baseline using this and set the minimum length of homopolymers to 5, it exits with a 'Killed' error message. When I set it to 6, it runs without problems. Could somebody please help me? Many thanks!
Commands I used:
msisensor-pro scan -d ../../../MSI_Data/References/hg19.fa -o MSI_mono_markers.txt -p 1 -l 5 -c 4
msisensor-pro baseline -d MSI_mono_markers.txt -i config_bams.txt -o Baseline/ -g ../../MSI_Data/References/hg19.fa -x 1 -p 5 baseline -d MSI_mono_markers.txt -i config_bams.txt -o Baseline/ -g ../../MSI_Data/References/hg19.fa -x 1 -p 5 Start at: Mon Jun 13 12:50:23 2022
Check for the environment ...
Current work path: /home/eso/MSI_Development/MSISensor_Pro/min_5
Microsatellites file: /home/eso/MSI_Development/MSISensor_Pro/min_5/MSI_mono_markers.txt
Configure file path: /home/eso/MSI_Development/MSISensor_Pro/min_5/config_bams.txt
Output path:/home/eso/MSI_Development/MSISensor_Pro/min_5/Baseline
/home/eso/MSI_Development/MSISensor_Pro/min_5/Baseline/ is existing! OK!
/home/eso/MSI_Development/MSISensor_Pro/min_5/Baseline/detail is existing! OK!
Load files ...
../../../MSI_Data/Fusion_BAMs/A22-7270_PH21-5216-H22-54_HS2-Lung_BC63_S5_20220320_220855_fusion.bam case1 /home/eso/MSI_Data/Fusion_BAMs/A22-7270_PH21-5216-H22-54_HS2-Lung_BC63_S5_20220320_220855_fusion.bam case1 load bam:/home/eso/MSI_Data/Fusion_BAMs/A22-7270_PH21-5216-H22-54_HS2-Lung_BC63_S5_20220320_220855_fusion.bam OK!
../../../MSI_Data/Fusion_BAMs/A22-7271_PH21-5214-H22-54_HS2-Lung_BC59_S1_20220320_230718_fusion.bam case2 /home/eso/MSI_Data/Fusion_BAMs/A22-7271_PH21-5214-H22-54_HS2-Lung_BC59_S1_20220320_230718_fusion.bam case2 load bam:/home/eso/MSI_Data/Fusion_BAMs/A22-7271_PH21-5214-H22-54_HS2-Lung_BC59_S1_20220320_230718_fusion.bam OK!
../../../MSI_Data/Fusion_BAMs/A22-7272_PH21-5214-H22-54_HS2-Lung_BC60_S2_20220321_000423_fusion.bam case3 /home/eso/MSI_Data/Fusion_BAMs/A22-7272_PH21-5214-H22-54_HS2-Lung_BC60_S2_20220321_000423_fusion.bam case3 load bam:/home/eso/MSI_Data/Fusion_BAMs/A22-7272_PH21-5214-H22-54_HS2-Lung_BC60_S2_20220321_000423_fusion.bam OK!
../../../MSI_Data/Fusion_BAMs/A22-7273_PH21-5214-H22-54_HS2-Lung_BC61_S3_20220321_010253_fusion.bam case4 /home/eso/MSI_Data/Fusion_BAMs/A22-7273_PH21-5214-H22-54_HS2-Lung_BC61_S3_20220321_010253_fusion.bam case4 load bam:/home/eso/MSI_Data/Fusion_BAMs/A22-7273_PH21-5214-H22-54_HS2-Lung_BC61_S3_20220321_010253_fusion.bam OK!
../../../MSI_Data/Fusion_BAMs/A22-7276_PH21-5217-H22-54_HS2-Lung_BC66_S8_20220321_041013_fusion.bam case5 /home/eso/MSI_Data/Fusion_BAMs/A22-7276_PH21-5217-H22-54_HS2-Lung_BC66_S8_20220321_041013_fusion.bam case5 load bam:/home/eso/MSI_Data/Fusion_BAMs/A22-7276_PH21-5217-H22-54_HS2-Lung_BC66_S8_20220321_041013_fusion.bam OK!
Loading homopolymer and microsatellite sites ...
Killed
Looks like r
missing here
hi Jia,
Is it as same as msisensor2? cut-off value =20?
and,
for different sample type should have the different cut-off value ?
Json
大批量无Normal肿瘤样品,如何创建baseline?目前手上有从SRA上搜罗的接近600例肿瘤RNA seq数据,目前尝试同时跑着批数据构建baseline,但尝试两次均失败了,detail中留下大约60样品的运行记录,然后得到一个空白的baseline。
那么,msisensor-pro可否分批次构建baseline,然后再合并呢?希望给点意见。
I am trying to run msisensor using the dockerhub image pengjia1110/msisensor-pro:latest
. i am running with singularity v 3.3.0 and getting the following error:
$ singularity exec ./msisensor-pro_latest.sif msisensor-pro
msisensor-pro: error while loading shared libraries: libhts.so.3: cannot open shared object file: No such file or directory
i get this error regardless of whether i use the singularity option -e
for a clean environment. maybe something is missing in the container because it was present in the host environment during testing?
I installed msisensor-pro using conda:
conda install -c bioconda msisensor-pro=v1.2.0
My commands:
# scan
msisensor-pro scan -d data/reference/wgs/Homo_sapiens_assembly38.fasta -o data/reference/wgs/reference.list
# msi
msisensor-pro msi -d data/reference/wgs/reference.list -n data/wgs/032d9fae-fcca-4f1c-8171-c103c0c6267e.cram -t data/wgs/a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46.cram -o output/wgs_test
loading bed regions ...
ERROR: LocalClientFile::read - failed to read all the data (handle 3): 0/8191: 21 Is a directory
Open reference file failed( ), please provide valid reference file !
Header of my cram file showing the same reference file Homo_sapiens_assembly38.fasta
was used for alignment:
# show first two lines of header
samtools view -H data/wgs/a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46.cram | head -n 2
@HD VN:1.5 SO:coordinate
@PG ID:bwa PN:bwa CL:bwa mem -K 100000000 -v 3 -t 36 -T 30 -Y /sbgenomics/workspaces/bb93ad01-b343-4c4b-8905-3810e95ef1ef/tasks/a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46/bundle_secondaries/Homo_sapiens_assembly38.fasta -R @RG\tID:A1104372_CSFP210003663-1a_HF37JDSX2_L2\tLB:CSFP210003663-1a\tPL:ILLUMINA\tSM:BS_82KENKT0 /sbgenomics/workspaces/bb93ad01-b343-4c4b-8905-3810e95ef1ef/tasks/a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46/process_pe_reads_1_s_process_pe_set_1_s_zcat_split_reads/A1104372_CSFP210003663-1a_HF37JDSX2_L2_1.fq.gz /sbgenomics/workspaces/bb93ad01-b343-4c4b-8905-3810e95ef1ef/tasks/a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46/process_pe_reads_1_s_process_pe_set_1_s_zcat_split_mates/A1104372_CSFP210003663-1a_HF37JDSX2_L2_2.fq.gz VN:0.7.17-r1188
Directory structure:
# reference files
data/reference/wgs/
├── Homo_sapiens_assembly38.fasta
├── Homo_sapiens_assembly38.fasta.fai
└── reference.list
# cram files
data/wgs/
├── 032d9fae-fcca-4f1c-8171-c103c0c6267e.cram
├── 032d9fae-fcca-4f1c-8171-c103c0c6267e.cram.crai
├── a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46.cram
└── a20ccf5b-94c4-426e-b9c2-cbeb53b3eb46.cram.crai
Does anyone know how to download the Baseline files?
Add scripts for user to determine the cuf-off of MSI-H and MSS/MSI-L ?
Greetings,
I ran MSIsensor-pro on tumor-normal paired samples from oncogene panel reads, following the workflow of your best practices precisely. However, in my output no MSI is detected whatsoever. Are there any additional parameters or input flags that I should be considering?
I have a directory with a cram and cram index file which are both symlinks.
9876T.recal.crai -> /home/fbdtemme/Documents/sarek/work/stage/32/6f1c18/9876T.recal.cram.crai
9876T.recal.cram -> /home/fbdtemme/Documents/sarek/work/stage/2f/682545/9876T.recal.cram
When running msisensor-pro baseline in that directory with a configuration file containing this:
9876T 9876T.recal.cram
it complains that the crai index is not found.
If I copy to index file to /home/fbdtemme/Documents/sarek/work/stage/2f/682545/
it works.
msisensor-pro seem to resolve the symlink to its realpath and looks into the directory there, instead of looking in the path of the symlink.
Feature request: please can you support cram files?
I'm looking at MSI Sensor output distribution file.
All of the lines in my output are at minimum 10 base repeats, but nothing that are 2 to 9 base repeats.
For example:
1 993467 GTTTC 10[A] TGAAA
is the smallest base pair repeat that is in the distribution file. There are none that are 9bp and below
Is there anyway to get this data?
Hi,
I have tried msisensor-pro (both installed by conda and pulled from docker). When I was using hg19 as a reference and tried to make a baseline using my .bam files,
docker run pengjia1110/msisensor-pro msisensor-pro baseline -d microsatellites_new.list -i normal_2.tsv -o baseline
the software returned following errors:
`Check for the environment ...
Current work path: /
Microsatellites file: /microsatellites_new.list
Configure file path: /normal_2.tsv
Output path:/baseline
/baseline/ is not existing! now make it! OK!
/baseline/detail is not existing! now make it ! OK!
Load files ...
Open configure file failed, please provide valid configure file ! `
I am sure that my .bam files were indexed correctly and the names were *.sorted.dedup.bam
and *.sorted.dedup.bam.bai
.
When I using version 1.0.2 it returned as cannot find the index files.
So what should I do to figure this out? Thanks!
Hey,
I needed to install libcurl4-openssl-dev
on ubuntu 20.04. You might want to add it to your readme.
Thanks
Dear development team,
I wanted to test msisensor, but when starting the msisensor scan
programm, an immediate Segmentation fault occurs.
I tested the version hosted on bioconda (v 0.5) on two different PCs, and additionally built the current version (0.6) from source code on one PC. In all three cases a Segmentation fault occured immediately.
Additionally, I tested the msisensor-pro scan
command (built from source code), giving the same result.
Do you have any suggestions, how I could circumvent this problem?
I further tested the scan
command of msisensor2, which managed to scan the genome. The resulting file seemed to be compatible with msisensor msi
, as it did not give any errors. Do you have any experience whether msisensor scan
and msisensor2 scan
give the same result or just share the same file format?
Thanks for your time
Barbara
Hello, When I use the docker to run msisensor-pro, at the baseline(for tumor only samples), the terminal shows:Program aborted:Same reference genome file should be used in both 'scan' and msi/pro/baseline steps. In the step of scan, I used GRCh38.d1.vd1.fa to generate reference.list.
=====here is full result=====================================================================
Test with bam input
baseline -d /data/reference/reference.list -i /data/data4baseline/baseline.bam.configure4docker -o /data/output/tumor_normal/tumor_only_bam Start at: Wed Mar 31 04:39:26 2021
Check for the environment ...
Current work path: /
Microsatellites file: /data/reference/reference.list
Configure file path: /data/data4baseline/baseline.bam.configure4docker
Output path:/data/output/tumor_normal/tumor_only_bam
/data/output/tumor_normal/tumor_only_bam/ is existing! OK!
/data/output/tumor_normal/tumor_only_bam/detail is existing! OK!
Load files ...
/data/data4baseline/bamForTumorOnlyBaseLine/case1_normal_sorted.bam case1
/data/data4baseline/bamForTumorOnlyBaseLine/case1_normal_sorted.bam case1
load bam:/data/data4baseline/bamForTumorOnlyBaseLine/case1_normal_sorted.bam OK!
/data/data4baseline/bamForTumorOnlyBaseLine/case2_normal_sorted.bam case2
/data/data4baseline/bamForTumorOnlyBaseLine/case2_normal_sorted.bam case2
load bam:/data/data4baseline/bamForTumorOnlyBaseLine/case2_normal_sorted.bam OK!
/data/data4baseline/bamForTumorOnlyBaseLine/case3_normal_sorted.bam case3
/data/data4baseline/bamForTumorOnlyBaseLine/case3_normal_sorted.bam case3
load bam:/data/data4baseline/bamForTumorOnlyBaseLine/case3_normal_sorted.bam OK!
Loading homopolymer and microsatellite sites ...
Total loading windows: 7123 !OK
Total loading homopolymer and microsatellites: 1806147 !OK
Process the 1 case : case1 /data/data4baseline/bamForTumorOnlyBaseLine/case1_normal_sorted.bam
Process 1 case1 window: 0 done...:chr1:25453-524883
Process 1 case1 window: 1 done...:chr1:527455-1029320
Process 1 case1 window: 2 done...:chr1:1028590-1528958
Process 1 case1 window: 3 done...:chr1:1529828-2027833
Process 1 case1 window: 4 done...:chr1:2041044-2542469
Process 1 case1 window: 5 done...:chr1:2547807-3042997
Process 1 case1 window: 6 done...:chr1:3050534-3539696
Process 1 case1 window: 7 done...:chr1:3554158-4055367
Process 1 case1 window: 8 done...:chr1:4055250-4556668
Process 1 case1 window: 9 done...:chr1:4559995-5053554
Process 1 case1 window: 10 done...:chr1:5062293-5562861
Process 1 case1 window: 11 done...:chr1:5562358-6064030
Process 1 case1 window: 12 done...:chr1:6062409-6562714
Process 1 case1 window: 13 done...:chr1:6566563-7068418
Process 1 case1 window: 14 done...:chr1:7074662-7570620
Process 1 case1 window: 15 done...:chr1:7575000-8075874
Process 1 case1 window: 16 done...:chr1:8075744-8577216
Process 1 case1 window: 17 done...:chr1:8576162-9077795
Process 1 case1 window: 18 done...:chr1:9076398-9576212
Process 1 case1 window: 19 done...:chr1:9579854-10081429
Process 1 case1 window: 20 done...:chr1:10080063-10581782
Process 1 case1 window: 21 done...:chr1:10580305-11081564
Process 1 case1 window: 22 done...:chr1:11081079-11578399
Process 1 case1 window: 23 done...:chr1:11581298-12082657
Process 1 case1 window: 24 done...:chr1:12081549-12577561
Process 1 case1 window: 25 done...:chr1:12581839-13082508
Process 1 case1 window: 26 done...:chr1:13081924-13581644
Process 1 case1 window: 27 done...:chr1:13585763-14085920
Process 1 case1 window: 28 done...:chr1:14086994-14588447
Process 1 case1 window: 29 done...:chr1:14588331-15088399
Process 1 case1 window: 30 done...:chr1:15088732-15590020
Process 1 case1 window: 31 done...:chr1:15589835-16091093
Process 1 case1 window: 32 done...:chr1:16092251-16589441
Process 1 case1 window: 33 done...:chr1:16593002-17094080
Process 1 case1 window: 34 done...:chr1:17099660-17600351
Process 1 case1 window: 35 done...:chr1:17600049-18099391
Process 1 case1 window: 36 done...:chr1:18100391-18601812
Process 1 case1 window: 37 done...:chr1:18601691-19103362
Process 1 case1 window: 38 done...:chr1:19104529-19604331
Process 1 case1 window: 39 done...:chr1:19605749-20105636
Process 1 case1 window: 40 done...:chr1:20107588-20609393
Process 1 case1 window: 41 done...:chr1:20608485-21109902
Process 1 case1 window: 42 done...:chr1:21109300-21610391
Process 1 case1 window: 43 done...:chr1:21614399-22112466
Process 1 case1 window: 44 done...:chr1:22118060-22619040
Process 1 case1 window: 45 done...:chr1:22619601-23121371
Process 1 case1 window: 46 done...:chr1:23121022-23622251
Process 1 case1 window: 47 done...:chr1:23621302-24120750
Process 1 case1 window: 48 done...:chr1:24121797-24622967
Process 1 case1 window: 49 done...:chr1:24621896-25123622
Process 1 case1 window: 50 done...:chr1:25122191-25622631
Process 1 case1 window: 51 done...:chr1:25623998-26124176
Process 1 case1 window: 52 done...:chr1:26124504-26626451
Process 1 case1 window: 53 done...:chr1:26626403-27126237
Process 1 case1 window: 54 done...:chr1:27127657-27629516
Process 1 case1 window: 55 done...:chr1:27628074-28129293
Process 1 case1 window: 56 done...:chr1:28128880-28629675
Process 1 case1 window: 57 done...:chr1:28629551-29128426
Process 1 case1 window: 58 done...:chr1:29130806-29632659
Process 1 case1 window: 59 done...:chr1:29636619-30138386
Process 1 case1 window: 60 done...:chr1:30138318-30638037
Process 1 case1 window: 61 done...:chr1:30643862-31144971
Process 1 case1 window: 62 done...:chr1:31144080-31641762
Process 1 case1 window: 63 done...:chr1:31647130-32148650
Process 1 case1 window: 64 done...:chr1:32147557-32649038
Process 1 case1 window: 65 done...:chr1:32647963-33145135
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Process 1 case1 window: 411 done...:chr1:225630409-226131469
Process 1 case1 window: 412 done...:chr1:226130631-226631763
Process 1 case1 window: 413 done...:chr1:226631517-227131287
Process 1 case1 window: 414 done...:chr1:227132973-227630944
Process 1 case1 window: 415 done...:chr1:227633646-228131659
Process 1 case1 window: 416 done...:chr1:228136776-228636808
Process 1 case1 window: 417 done...:chr1:228636822-229137062
Process 1 case1 window: 418 done...:chr1:229139152-229640336
Process 1 case1 window: 419 done...:chr1:229640009-230139524
Process 1 case1 window: 420 done...:chr1:230140788-230641922
Process 1 case1 window: 421 done...:chr1:230644233-231145942
Process 1 case1 window: 422 done...:chr1:231151931-231647058
Process 1 case1 window: 423 done...:chr1:231652573-232151525
Process 1 case1 window: 424 done...:chr1:232154726-232655334
Process 1 case1 window: 425 done...:chr1:232655016-233156067
Process 1 case1 window: 426 done...:chr1:233155918-233656904
Process 1 case1 window: 427 done...:chr1:233656714-234156782
Process 1 case1 window: 428 done...:chr1:234158551-234657700
Process 1 case1 window: 429 done...:chr1:234662469-235163989
Process 1 case1 window: 430 done...:chr1:235164830-235666612
Process 1 case1 window: 431 done...:chr1:235665225-236165886
Process 1 case1 window: 432 done...:chr1:236165493-236666618
Process 1 case1 window: 433 done...:chr1:236666948-237168708
Process 1 case1 window: 434 done...:chr1:237168288-237669539
Process 1 case1 window: 435 done...:chr1:237670502-238170824
Process 1 case1 window: 436 done...:chr1:238175033-238672897
Process 1 case1 window: 437 done...:chr1:238679909-239181886
Process 1 case1 window: 438 done...:chr1:239180794-239681831
Process 1 case1 window: 439 done...:chr1:239681637-240181957
Process 1 case1 window: 440 done...:chr1:240181919-240683547
Process 1 case1 window: 441 done...:chr1:240682011-241183944
Process 1 case1 window: 442 done...:chr1:241185510-241684864
Process 1 case1 window: 443 done...:chr1:241686304-242187183
Process 1 case1 window: 444 done...:chr1:242187004-242686012
Process 1 case1 window: 445 done...:chr1:242687791-243185363
Process 1 case1 window: 446 done...:chr1:243188556-243690482
Process 1 case1 window: 447 done...:chr1:243688635-244187079
Process 1 case1 window: 448 done...:chr1:244189949-244691708
Process 1 case1 window: 449 done...:chr1:244692435-245192059
Process 1 case1 window: 450 done...:chr1:245193399-245691740
Process 1 case1 window: 451 done...:chr1:245698494-246197994
Process 1 case1 window: 452 done...:chr1:246201013-246702950
Process 1 case1 window: 453 done...:chr1:246701292-247198791
Process 1 case1 window: 454 done...:chr1:247204109-247702651
Process 1 case1 window: 455 done...:chr1:247709523-248211002
Process 1 case1 window: 456 done...:chr1:248210525-248711015
Process 1 case1 window: 457 done...:chr1:248713388-248944637
Program aborted:
Same reference genome file should be used in both 'scan' and msi/pro/baseline steps!!!
Hello, I am using pro
to scan homopolymer sites. When I increase the context length from 5(default) to 9, the homopolymer A results from *_all got dropped. When I use context length 8, however, everything is fine. There is an intrinsic problem with using short context lenght. That's why I increase to 9. If you are interested, we can discuss the problem. But that is for another topic.
The command I use, pro version 1.2.0
msisensor-pro scan -d $HG19 -o hg19_hp.tsv -l 8 -c 9 -p 1
msisensor-pro pro -d hg19_hp.tsv -t $BAM -c 1 -x 1 -b 4 -o regular -e $BED -i 0.1 -l 5
My code below:
msisensor-pro msi -d ${MSI_LIST} \
-t ${T_CRAM} \
-n ${N_CRAM} \
-g ${REF} \
-z 1 \
-e ${BED_FILE} \
-o ${RUN_DIR}${T_CRAM}${N_CRAM} -b 2
Message I'm getting (1 file out of the 100 in the batch):
[rajpara@discovery1 MSI]$ cat msisensor_100_11248368.out
==========================================
SLURM_JOB_ID = 11248368
SLURM_JOB_NODELIST = e11-26
TMPDIR = /tmp/SLURM_11248368
==========================================
msi -d /scratch1/rajpara/WES/MSI/microsatellites.list -t /scratch1/rajpara/WES/tumor_normal_CRAMs2/A37649_st_t_markdup.cram -n /scratch1/rajpara/WES/tumor_normal_CRAMs2/A37650_st_g_markdup.cram -g /scratch1/rajpara/resources/hs38DH.fa -z 1 -e /scratch1/rajpara/resources/hg38_IDT_targets_sorted_merged_extended_sorted_merged.bed -o /scratch1/rajpara/WES/MSI//scratch1/rajpara/WES/tumor_normal_CRAMs2/A37649_st_t_markdup.cram/scratch1/rajpara/WES/tumor_normal_CRAMs2/A37650_st_g_markdup.cram -b 2 Start at: Sat Oct 1 16:36:00 2022
ERROR: unrecognized option -g
please provide valid format normal bam file !
I saw that CRAM files were acceptable as input. If there is an option I'm missing, please let me know. Thank you so much!
Hi,
As a quick check for the calls obtained, I ran the analysis in reverse, that is using the tumour as a normal sample, and the normal sample as a tumour. Strangely, this gives exactly the same results as a normal analysis.
Is this expected?
Thanks,
Tim
I'm using a cluster and installed msisensor-pro via conda. Here is my command and results:
(msisensor-env) [myid@compute-a-xx-xxx msisensor_test]$ msisensor-pro baseline -d hg19_msi_reference.list -i configure.txt -o .
baseline -d hg19_msi_reference.list -i configure.txt -o .
Check for the environment ...
Current work path: /home/myid/msisensor_test
Microsatellites file: /home/myid/msisensor_test/hg19_msi_reference.list
Configure file path: /home/myid/msisensor_test/configure.txt
Output path:/home/myid/msisensor_test
/home/myid/msisensor_test/ is existing! OK!
/home/myid/msisensor_test/detail is existing! OK!
Load files ...
load bam:/home/myid/msisensor_test/normal/case1_sorted.bam OK!
load bam:/home/myid/msisensor_test/normal/case2_sorted.bam OK!
Open bam file failed( ), please provide valid bam file with its index file !
However, I have both the .bam and .bai files under the normal folder. I've also checked the bam files using samtools and they are correct with completed EOF. I tried both case1_sorted.bam.bai and case1_sorted.bai, but neither of them worked for me.
Dear professor,
as you can see, target panel especially some small panels are economic for clinic, so is there a the least msi sites for a panel, is there any suggeestion about this?
thanks a lot
Hi all,
I am trying to run msisensor-pro scan to get a list of msi sites but keep getting "Segmentation fault (core dumped)" error.
I running this on a HPC cluster with 40gb of RAM so I don't think it should be a memory issue.
hope to get somehelp.
regards
When i use the docker to run msisensor-pro, at the scan the default reference.fa.
The terminal alarm: fatal error: failed to open ref file
How to solve this problem? many thanks!
Hi,
Could you kindly tell me how to use custom MS file to run msisensor-pro?
Thanks very much,
Kimdo
Dear author :
i got a very strange error when i runing
msisensor-pro baseline -d $ref_list/reference.list -i configure.txt -o ./
Open bam file failed( ), please provide valid bam file with its index file !
it seem to say that i have to set index file for every bam file, so i use samtools index to do this then i got .bai index files for every bam file.
then I repat the code agagin ,however the same error happend
where i was wrong?!
thank you for your answer
when use msisensor or msisensor-pro msi to analyze the tumor-normal pair data , some MSI loci both in output.prefix_germline and output.prefix_somatic,such as:
CRC_16_germline
chr1 151196702 GATTG 9 T CCCCC 9|9
chr1 151196711 TTTTT 6 C TAGCA 6|6
chr1 200594041 AACAA 8 T AAGGC 8|8
chr2 148683685 TGCAT 8 A GAGGC 8|8
chr3 51417603 TGATA 7 C AGCCC 7|7
chr7 55273591 ATTTG 13 A GTATA 14|14
chr7 74608740 ACTGC 13 T ATGGT 13|13
chr7 143003662 TTTTT 19 A CTTCC 19|19
chr8 7346866 GTCCC 9 T GGTGG 9|9
chr8 7679727 CCACC 9 A GGGAC 9|9
chr11 120350636 ATATG 8 T CCCCC 8|8
chr11 120350644 TTTTT 5 C TCCTT 5|5
chr16 14983091 GAGTT 9 A CCCAT 9|9
chr16 56718015 CTGGA 20 T GTACA 19|19
chr17 56435160 AGGGA 7 C GCCTT 7|7
CRC_16_somatic
chr17 56435160 AGGGA 7 C GCCTT 0.67666 1e-14 2.4e-13 1
chr16 56718015 CTGGA 20 T GTACA 0.94734 1e-14 1.2e-13 2
chr16 14983091 GAGTT 9 A CCCAT 0.040581 1e-14 8e-14 3
chr14 23652346 TTGCT 21 A GGCCA 0.98116 1e-14 6e-14 4
chr11 125490765 GAAGA 21 T AATAT 0.84764 1e-14 4.8e-14 5
chr11 120350636 ATATG 8 T CCCCC 0.031458 1e-14 4e-14 6
chr11 102193508 CTGGT 26 A GCCAC 0.87536 1e-14 3.4286e-14 7
chr8 7679727 CCACC 9 A GGGAC 0.74167 1e-14 3e-14 8
chr8 7346866 GTCCC 9 T GGTGG 0.74167 1e-14 2.6667e-14 9
chr7 143003662 TTTTT 19 A CTTCC 0.73456 1e-14 2.4e-14 10
chr7 143003342 AAGAC 25 T GAGAC 0.95439 1e-14 2.1818e-14 11
chr7 74608740 ACTGC 13 T ATGGT 0.8415 1e-14 2e-14 12
chr7 55273591 ATTTG 13 A GTATA 0.97535 1e-14 1.8462e-14 13
chr4 55598211 TTTGA 25 T GAGAA 0.98916 1e-14 1.7143e-14 14
chr3 51417603 TGATA 7 C AGCCC 0.71456 1e-14 1.6e-14 15
chr2 148683685 TGCAT 8 A GAGGC 0.99445 1e-14 1.5e-14 16
chr2 95849361 TCCTA 23 T GTGAG 0.87912 1e-14 1.4118e-14 17
chr2 47641559 CAGGT 27 A GGGTT 0.99225 1e-14 1.3333e-14 18
chr2 39564893 TCCAG 28 T GAGAC 0.90117 1e-14 1.2632e-14 19
chr2 39536689 CAGGA 27 T GAGGC 0.88219 1e-14 1.2e-14 20
chr1 200594041 AACAA 8 T AAGGC 0.99484 1e-14 1.1429e-14 21
chr1 151196702 GATTG 9 T CCCCC 0.67961 1e-14 1.0909e-14 22
chr1 151196711 TTTTT 6 C TAGCA 0.0095938 3.0295e-05 3.1612e-05 23
but the output.prefix :
Total_Number_of_Sites Number_of_Somatic_Sites %
24 23 95.83
the msi loci in output.prefix_germline means the stable loci? if that,the msi result will be:
Total_Number_of_Sites Number_of_Somatic_Sites %
24 (23-15) 33.33
I'm very confused about this, and hope your reply!
Microsatellites located at some large mutation regions (such as loss of heterozygosity (LOH)) in tumor sample may be estameated as unstable sites for MSI detection. In this case, MSS sample may be misjudged as MSI one.
Hi,
I found the sample.bai that was generate from GATK can not be directly loaded in msisensor-pro baseline option. Rather, it required a sample.bam.bai that newly generated by samtools.
[bam_index_load] fail to load BAM index. msisensor-pro: bamreader.cpp:172: bool ReadInBamReads(const char*, const string&, unsigned int, unsigned int, std::vector<SPLIT_READ>&, std::string): Assertion
idx' failed.`
However, this kind of index file can be loaded when "msisensor-pro pro" command is applied.
It will be great of help if you can offer a solution.
Thanks
Hello,
Thanks for your tool which looks very great!
I'm wondering what kind of data is needed to build a good baseline in order to perform tumor-only analyses?
In your wiki, you said that 20 normal samples are needed for this baseline. Is it better to build a baseline with normal samples from patients with a particular tumor type or whatever?
For example, if we have 20 normal samples from patients with CRC and 20 normal samples from patients with prostate cancer, is it better to build two different baselines (one to use for MSI detection in CRC tumor samples and one to use with prostate tumor samples) and should we build only one baseline with 40 normal samples from patients with both cancer types?
Another question is: How to deal with cancer panel data? The difference is it only at the last step where we can provide a bed file indicating the targeted genes? Should we provide a file with microsatellite positions?
Thanks a lot!
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. If you use GRCh38.d1.vd1 , you can download the file on out github directly.
@PengJia6 Do you have this file somewhere in the project? I cannot seem to locate it. I assume the file referred to in the wiki is the ~7700 sites DMS from the paper? Thank you.
Hi,
MSISensor-pro did provide a large convenience to determine the MSI status for tumor-only samples by building the baseline of PON.
According to our knowledge, pathogenic mutations in MMR gene, such as MLH1, MSH2, MSH6, PMS2, can result in deficient MMR gene function, which will finally lead to MSI-H for these samples.
However, we found some samples harbor these pathogenic variants, but remained stable for most MSI sites.
Any suggestion to review/plot the peaks of these sites without wet experiments (since these data were fetched from public resource), so that we can understand the underlying mechanism behind?
Thanks,
Junfeng
./msisensor_pro pro -h
.
.
-c coverage threshold for msi analysis, WXS: 20; WGS: 15, default=1
.
.
.
best,
json
I tried to run the baseline command in parallel using this code:
msisensor-pro baseline -d msisensor-pro.list -i normal_configure.txt -o baseline -c 20 -b 4
But it returns this error:
failed to open output files to write !
malloc(): unsorted double linked list corrupted
Segmentation fault (core dumped)
There are 25 sample BAM files in the configure. I use MSIsensor-pro in Ubuntu 20 on a 6-core machine.
The command works fine without parallelisation:
msisensor-pro baseline -d msisensor-pro.list -i normal_configure.txt -o baseline -c 20
Thanks,
Shahryar
How to classify the MSI high and MSI low ? Looking forward to your reply.
Hello,
Can you guys provide a bit more info on what the outputs from the msisensor-pro msi
results mean? I see there are the prefix, _dis, _germline, __somatic files. It would be helpful if there is some explanation in the documentation? The File Formats page only had a few lines describing the outputs. I'm interested in knowing if the tumor samples I have can be classified into MSI-H or MSS. Is the MSI score the third column % in the prefix file? What is the # of somatic sites? is this the number of detected unstable sites (in somatic, and not found in germline)?
Also, do you need multiple normal samples as baseline, or is this only for tumor-only and you can run with just a single matched tumor-normal pair?
Bo
I notices the notes in the README file said "2. If you don't have normal samples to build baseline(baseline step for tumor only sample detection), you can download the microsatellites information with baseline on our github or use -i option in pro module to set a hard cutoff directly.". May I know the exact link to download the file? Thanks
In tumor/normal mode, how can I tell if a site is a deletion or insertion in the tumor? I can only see a difference
column. It might be more useful to report something like tumor content / normal content
rather than (AreaMax - AreaMin)/AreaMax
Hi, it would be nice to exclude secondary alignment by default. Thanks.
I am using pengjia1110/msisensor-pro to analyze a pair of Tumor and Normal bam files. The command is :
docker run --cpus=10 -i --rm pengjia1110/msisensor-pro msisensor-pro msi -b 10 -d ... -n .. -t .. -e .. -o ..
I only observed max ~150% cpu usage, and the speed is almost the same with -b 1, if not slower.
Is this normal? or if I did anything wrong? Thanks!
Add cram support
After downloading the binary using the wget
route, I get this error:
./msisensor-pro: error while loading shared libraries: libhts.so.3: cannot open shared object file: No such file or directory
The conda install route worked though
I have some normal sample sequence data (WGS), and some tumor sequence data (WES/WXS).
I'd like to use normal data (WGS) to build baseline, then use tumor data to evaluate MSI.
Could I use these data to run msisensor-pro?
Or the 2nd step use WES data, then 3rd step use WGS data?
Thank you for your answer😀.
For published studies, we usually don't have access to BAM. I am wondering if it is possible to calculate MSI scores just based on called mutations?
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