xjtu-omics / msisensor-pro Goto Github PK

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

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!

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

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,

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

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.

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.

Looks like r missing here

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'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.

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

./msisensor_pro pro -h

.
.
-c coverage threshold for msi analysis, WXS: 20; WGS: 15, default=1
.
.
.

best,
json

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?

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,

Here is the distribution of the % Somatic sites from 132 paired tumor-normal msisensor runs (WGS):

Is there a cut-off that can tell me what is considered as low or high instability?

Thanks!

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

Add scripts for user to determine the cuf-off of MSI-H and MSS/MSI-L ?

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

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

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

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!

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
Process 1 case1 window: 66 done...:chr1:33148458-33649361
Process 1 case1 window: 67 done...:chr1:33651183-34151079
Process 1 case1 window: 68 done...:chr1:34153727-34654989
Process 1 case1 window: 69 done...:chr1:34659132-35161046
Process 1 case1 window: 70 done...:chr1:35159299-35659876
Process 1 case1 window: 71 done...:chr1:35660186-36160035
Process 1 case1 window: 72 done...:chr1:36160863-36659435
Process 1 case1 window: 73 done...:chr1:36661232-37162293
Process 1 case1 window: 74 done...:chr1:37162313-37663975
Process 1 case1 window: 75 done...:chr1:37663320-38145781
Process 1 case1 window: 76 done...:chr1:38163398-38662597
Process 1 case1 window: 77 done...:chr1:38663959-39164340
Process 1 case1 window: 78 done...:chr1:39165071-39665852
Process 1 case1 window: 79 done...:chr1:39666196-40163184
Process 1 case1 window: 80 done...:chr1:40167041-40661378
Process 1 case1 window: 81 done...:chr1:40667249-41168912
Process 1 case1 window: 82 done...:chr1:41167632-41667143
Process 1 case1 window: 83 done...:chr1:41675060-42176136
Process 1 case1 window: 84 done...:chr1:42176756-42677760
Process 1 case1 window: 85 done...:chr1:42681905-43181223
Process 1 case1 window: 86 done...:chr1:43183740-43679587
Process 1 case1 window: 87 done...:chr1:43684455-44186429
Process 1 case1 window: 88 done...:chr1:44185337-44686634
Process 1 case1 window: 89 done...:chr1:44685679-45187593
Process 1 case1 window: 90 done...:chr1:45188320-45689903
Process 1 case1 window: 91 done...:chr1:45688995-46187664
Process 1 case1 window: 92 done...:chr1:46189097-46682963
Process 1 case1 window: 93 done...:chr1:46692687-47192445
Process 1 case1 window: 94 done...:chr1:47198604-47695863
Process 1 case1 window: 95 done...:chr1:47699692-48201417
Process 1 case1 window: 96 done...:chr1:48202170-48700940
Process 1 case1 window: 97 done...:chr1:48706296-49207859
Process 1 case1 window: 98 done...:chr1:49206542-49708367
Process 1 case1 window: 99 done...:chr1:49713569-50214662
Process 1 case1 window: 100 done...:chr1:50215071-50715347
Process 1 case1 window: 101 done...:chr1:50716987-51218836
Process 1 case1 window: 102 done...:chr1:51217128-51718675
Process 1 case1 window: 103 done...:chr1:51718445-52215669
Process 1 case1 window: 104 done...:chr1:52218647-52719644
Process 1 case1 window: 105 done...:chr1:52718747-53217771
Process 1 case1 window: 106 done...:chr1:53222052-53723830
Process 1 case1 window: 107 done...:chr1:53722239-54222702
Process 1 case1 window: 108 done...:chr1:54223941-54725432
Process 1 case1 window: 109 done...:chr1:54726055-55227997
Process 1 case1 window: 110 done...:chr1:55227524-55729392
Process 1 case1 window: 111 done...:chr1:55727846-56229430
Process 1 case1 window: 112 done...:chr1:56228960-56727950
Process 1 case1 window: 113 done...:chr1:56729283-57230516
Process 1 case1 window: 114 done...:chr1:57229320-57730894
Process 1 case1 window: 115 done...:chr1:57730804-58227396
Process 1 case1 window: 116 done...:chr1:58231476-58731564
Process 1 case1 window: 117 done...:chr1:58732141-59232685
Process 1 case1 window: 118 done...:chr1:59235289-59737103
Process 1 case1 window: 119 done...:chr1:59740938-60241261
Process 1 case1 window: 120 done...:chr1:60242181-60740376
Process 1 case1 window: 121 done...:chr1:60742967-61241970
Process 1 case1 window: 122 done...:chr1:61245604-61747094
Process 1 case1 window: 123 done...:chr1:61747246-62248304
Process 1 case1 window: 124 done...:chr1:62247547-62748717
Process 1 case1 window: 125 done...:chr1:62748386-63248686
Process 1 case1 window: 126 done...:chr1:63248645-63740404
Process 1 case1 window: 127 done...:chr1:63748843-64250377
Process 1 case1 window: 128 done...:chr1:64250179-64751307
Process 1 case1 window: 129 done...:chr1:64752523-65254352
Process 1 case1 window: 130 done...:chr1:65253467-65751490
Process 1 case1 window: 131 done...:chr1:65754368-66242725
Process 1 case1 window: 132 done...:chr1:66260354-66754153
Process 1 case1 window: 133 done...:chr1:66764640-67264863
Process 1 case1 window: 134 done...:chr1:67264873-67765701
Process 1 case1 window: 135 done...:chr1:67765106-68265698
Process 1 case1 window: 136 done...:chr1:68265929-68767107
Process 1 case1 window: 137 done...:chr1:68766601-69268048
Process 1 case1 window: 138 done...:chr1:69271065-69767035
Process 1 case1 window: 139 done...:chr1:69775716-70276961
Process 1 case1 window: 140 done...:chr1:70275849-70776948
Process 1 case1 window: 141 done...:chr1:70776057-71276647
Process 1 case1 window: 142 done...:chr1:71280161-71781040
Process 1 case1 window: 143 done...:chr1:71781991-72283938
Process 1 case1 window: 144 done...:chr1:72282126-72779024
Process 1 case1 window: 145 done...:chr1:72788757-73290523
Process 1 case1 window: 146 done...:chr1:73289640-73787984
Process 1 case1 window: 147 done...:chr1:73792193-74292155
Process 1 case1 window: 148 done...:chr1:74295273-74794251
Process 1 case1 window: 149 done...:chr1:74801548-75303112
Process 1 case1 window: 150 done...:chr1:75303074-75802710
Process 1 case1 window: 151 done...:chr1:75803499-76303053
Process 1 case1 window: 152 done...:chr1:76305668-76806706
Process 1 case1 window: 153 done...:chr1:76812057-77313611
Process 1 case1 window: 154 done...:chr1:77313411-77813443
Process 1 case1 window: 155 done...:chr1:77816955-78318862
Process 1 case1 window: 156 done...:chr1:78325227-78826991
Process 1 case1 window: 157 done...:chr1:78827189-79326604
Process 1 case1 window: 158 done...:chr1:79328481-79825567
Process 1 case1 window: 159 done...:chr1:79831891-80333561
Process 1 case1 window: 160 done...:chr1:80333298-80834033
Process 1 case1 window: 161 done...:chr1:80838272-81340037
Process 1 case1 window: 162 done...:chr1:81338382-81837817
Process 1 case1 window: 163 done...:chr1:81838742-82339968
Process 1 case1 window: 164 done...:chr1:82339477-82840784
Process 1 case1 window: 165 done...:chr1:82839812-83340019
Process 1 case1 window: 166 done...:chr1:83341360-83833252
Process 1 case1 window: 167 done...:chr1:83842881-84339432
Process 1 case1 window: 168 done...:chr1:84343078-84842998
Process 1 case1 window: 169 done...:chr1:84846360-85346334
Process 1 case1 window: 170 done...:chr1:85350060-85852024
Process 1 case1 window: 171 done...:chr1:85851102-86345224
Process 1 case1 window: 172 done...:chr1:86352417-86853400
Process 1 case1 window: 173 done...:chr1:86852679-87348460
Process 1 case1 window: 174 done...:chr1:87356223-87856447
Process 1 case1 window: 175 done...:chr1:87856983-88353795
Process 1 case1 window: 176 done...:chr1:88357038-88857804
Process 1 case1 window: 177 done...:chr1:88857867-89356438
Process 1 case1 window: 178 done...:chr1:89360079-89861786
Process 1 case1 window: 179 done...:chr1:89862533-90362174
Process 1 case1 window: 180 done...:chr1:90362624-90864423
Process 1 case1 window: 181 done...:chr1:90863076-91364524
Process 1 case1 window: 182 done...:chr1:91363192-91862857
Process 1 case1 window: 183 done...:chr1:91864202-92362142
Process 1 case1 window: 184 done...:chr1:92364980-92866440
Process 1 case1 window: 185 done...:chr1:92865957-93365851
Process 1 case1 window: 186 done...:chr1:93366008-93863097
Process 1 case1 window: 187 done...:chr1:93866143-94364740
Process 1 case1 window: 188 done...:chr1:94372307-94873411
Process 1 case1 window: 189 done...:chr1:94877260-95379141
Process 1 case1 window: 190 done...:chr1:95377639-95879636
Process 1 case1 window: 191 done...:chr1:95877659-96379229
Process 1 case1 window: 192 done...:chr1:96385759-96887484
Process 1 case1 window: 193 done...:chr1:96891435-97393361
Process 1 case1 window: 194 done...:chr1:97392291-97893087
Process 1 case1 window: 195 done...:chr1:97892429-98392567
Process 1 case1 window: 196 done...:chr1:98394487-98894618
Process 1 case1 window: 197 done...:chr1:98895020-99392033
Process 1 case1 window: 198 done...:chr1:99405711-99906372
Process 1 case1 window: 199 done...:chr1:99907210-100407930
Process 1 case1 window: 200 done...:chr1:100409210-100910043
Process 1 case1 window: 201 done...:chr1:100911714-101413063
Process 1 case1 window: 202 done...:chr1:101415062-101916364
Process 1 case1 window: 203 done...:chr1:101916417-102417323
Process 1 case1 window: 204 done...:chr1:102416750-102915914
Process 1 case1 window: 205 done...:chr1:102922434-103422579
Process 1 case1 window: 206 done...:chr1:103422463-103917504
Process 1 case1 window: 207 done...:chr1:103929737-104431669
Process 1 case1 window: 208 done...:chr1:104433805-104926787
Process 1 case1 window: 209 done...:chr1:104943598-105445229
Process 1 case1 window: 210 done...:chr1:105445674-105940890
Process 1 case1 window: 211 done...:chr1:105946872-106446541
Process 1 case1 window: 212 done...:chr1:106446885-106945199
Process 1 case1 window: 213 done...:chr1:106949467-107450720
Process 1 case1 window: 214 done...:chr1:107450108-107949841
Process 1 case1 window: 215 done...:chr1:107950183-108450416
Process 1 case1 window: 216 done...:chr1:108451948-108953800
Process 1 case1 window: 217 done...:chr1:108953010-109454911
Process 1 case1 window: 218 done...:chr1:109455446-109956622
Process 1 case1 window: 219 done...:chr1:109958345-110460256
Process 1 case1 window: 220 done...:chr1:110458774-110960736
Process 1 case1 window: 221 done...:chr1:110959330-111458312
Process 1 case1 window: 222 done...:chr1:111459342-111961042
Process 1 case1 window: 223 done...:chr1:111964438-112464602
Process 1 case1 window: 224 done...:chr1:112464445-112966442
Process 1 case1 window: 225 done...:chr1:112964684-113461812
Process 1 case1 window: 226 done...:chr1:113468513-113967836
Process 1 case1 window: 227 done...:chr1:113968903-114467040
Process 1 case1 window: 228 done...:chr1:114469638-114971511
Process 1 case1 window: 229 done...:chr1:114974333-115475164
Process 1 case1 window: 230 done...:chr1:115476590-115977305
Process 1 case1 window: 231 done...:chr1:115978976-116480193
Process 1 case1 window: 232 done...:chr1:116479960-116974604
Process 1 case1 window: 233 done...:chr1:116982498-117483514
Process 1 case1 window: 234 done...:chr1:117487213-117983241
Process 1 case1 window: 235 done...:chr1:117994153-118494758
Process 1 case1 window: 236 done...:chr1:118496540-118997997
Process 1 case1 window: 237 done...:chr1:118998555-119494351
Process 1 case1 window: 238 done...:chr1:119503461-120004373
Process 1 case1 window: 239 done...:chr1:120003780-120503289
Process 1 case1 window: 240 done...:chr1:120507455-121008428
Process 1 case1 window: 241 done...:chr1:121013231-121513317
Process 1 case1 window: 242 done...:chr1:121514242-121925662
Process 1 case1 window: 243 done...:chr1:122101394-122230884
Process 1 case1 window: 244 done...:chr1:123434176-123757876
Process 1 case1 window: 245 done...:chr1:124123576-124260962
Process 1 case1 window: 246 done...:chr1:124838519-125147418
Process 1 case1 window: 247 done...:chr1:143276353-143775732
Process 1 case1 window: 248 done...:chr1:143778873-144278504
Process 1 case1 window: 249 done...:chr1:144279206-144780971
Process 1 case1 window: 250 done...:chr1:144781896-145282779
Process 1 case1 window: 251 done...:chr1:145283820-145784832
Process 1 case1 window: 252 done...:chr1:145786571-146286827
Process 1 case1 window: 253 done...:chr1:146287090-146788367
Process 1 case1 window: 254 done...:chr1:146787703-147289325
Process 1 case1 window: 255 done...:chr1:147292310-147793404
Process 1 case1 window: 256 done...:chr1:147795494-148294603
Process 1 case1 window: 257 done...:chr1:148298823-148800204
Process 1 case1 window: 258 done...:chr1:148798840-149300801
Process 1 case1 window: 259 done...:chr1:149300181-149799618
Process 1 case1 window: 260 done...:chr1:149802520-150302624
Process 1 case1 window: 261 done...:chr1:150303311-150803278
Process 1 case1 window: 262 done...:chr1:150804209-151305829
Process 1 case1 window: 263 done...:chr1:151306074-151807057
Process 1 case1 window: 264 done...:chr1:151809533-152302938
Process 1 case1 window: 265 done...:chr1:152313865-152808078
Process 1 case1 window: 266 done...:chr1:152813999-153313602
Process 1 case1 window: 267 done...:chr1:153323866-153825758
Process 1 case1 window: 268 done...:chr1:153825006-154323489
Process 1 case1 window: 269 done...:chr1:154327543-154829344
Process 1 case1 window: 270 done...:chr1:154833141-155334050
Process 1 case1 window: 271 done...:chr1:155333437-155834646
Process 1 case1 window: 272 done...:chr1:155834463-156335239
Process 1 case1 window: 273 done...:chr1:156335280-156831550
Process 1 case1 window: 274 done...:chr1:156837375-157337739
Process 1 case1 window: 275 done...:chr1:157338783-157838771
Process 1 case1 window: 276 done...:chr1:157844411-158346304
Process 1 case1 window: 277 done...:chr1:158344728-158846219
Process 1 case1 window: 278 done...:chr1:158845622-159346187
Process 1 case1 window: 279 done...:chr1:159347936-159843814
Process 1 case1 window: 280 done...:chr1:159851160-160351712
Process 1 case1 window: 281 done...:chr1:160357908-160859084
Process 1 case1 window: 282 done...:chr1:160858743-161360096
Process 1 case1 window: 283 done...:chr1:161359028-161855871
Process 1 case1 window: 284 done...:chr1:161859183-162358097
Process 1 case1 window: 285 done...:chr1:162360277-162861562
Process 1 case1 window: 286 done...:chr1:162860401-163360848
Process 1 case1 window: 287 done...:chr1:163362405-163860524
Process 1 case1 window: 288 done...:chr1:163864212-164366096
Process 1 case1 window: 289 done...:chr1:164364463-164859447
Process 1 case1 window: 290 done...:chr1:164869191-165366472
Process 1 case1 window: 291 done...:chr1:165369333-165871201
Process 1 case1 window: 292 done...:chr1:165870729-166372135
Process 1 case1 window: 293 done...:chr1:166371113-166872521
Process 1 case1 window: 294 done...:chr1:166871673-167368794
Process 1 case1 window: 295 done...:chr1:167371929-167873367
Process 1 case1 window: 296 done...:chr1:167872074-168369528
Process 1 case1 window: 297 done...:chr1:168372960-168873474
Process 1 case1 window: 298 done...:chr1:168873415-169375202
Process 1 case1 window: 299 done...:chr1:169374321-169871755
Process 1 case1 window: 300 done...:chr1:169875095-170376942
Process 1 case1 window: 301 done...:chr1:170375324-170876476
Process 1 case1 window: 302 done...:chr1:170878059-171372882
Process 1 case1 window: 303 done...:chr1:171378618-171879837
Process 1 case1 window: 304 done...:chr1:171879905-172378553
Process 1 case1 window: 305 done...:chr1:172379981-172878619
Process 1 case1 window: 306 done...:chr1:172887107-173386849
Process 1 case1 window: 307 done...:chr1:173389737-173885153
Process 1 case1 window: 308 done...:chr1:173897461-174397975
Process 1 case1 window: 309 done...:chr1:174402208-174898978
Process 1 case1 window: 310 done...:chr1:174902955-175403361
Process 1 case1 window: 311 done...:chr1:175404592-175904423
Process 1 case1 window: 312 done...:chr1:175905268-176406304
Process 1 case1 window: 313 done...:chr1:176408749-176909132
Process 1 case1 window: 314 done...:chr1:176921556-177416698
Process 1 case1 window: 315 done...:chr1:177424449-177920812
Process 1 case1 window: 316 done...:chr1:177930985-178431907
Process 1 case1 window: 317 done...:chr1:178432917-178934788
Process 1 case1 window: 318 done...:chr1:178936115-179438069
Process 1 case1 window: 319 done...:chr1:179437160-179938557
Process 1 case1 window: 320 done...:chr1:179937771-180436415
Process 1 case1 window: 321 done...:chr1:180438589-180935755
Process 1 case1 window: 322 done...:chr1:180939667-181440333
Process 1 case1 window: 323 done...:chr1:181439983-181939336
Process 1 case1 window: 324 done...:chr1:181942667-182444007
Process 1 case1 window: 325 done...:chr1:182442778-182944453
Process 1 case1 window: 326 done...:chr1:182943121-183443170
Process 1 case1 window: 327 done...:chr1:183446027-183944436
Process 1 case1 window: 328 done...:chr1:183946030-184447851
Process 1 case1 window: 329 done...:chr1:184448051-184949122
Process 1 case1 window: 330 done...:chr1:184948360-185447769
Process 1 case1 window: 331 done...:chr1:185453490-185945162
Process 1 case1 window: 332 done...:chr1:185953769-186455168
Process 1 case1 window: 333 done...:chr1:186454797-186956734
Process 1 case1 window: 334 done...:chr1:186954900-187453757
Process 1 case1 window: 335 done...:chr1:187456955-187956627
Process 1 case1 window: 336 done...:chr1:187962076-188455997
Process 1 case1 window: 337 done...:chr1:188463401-188951998
Process 1 case1 window: 338 done...:chr1:188965123-189466363
Process 1 case1 window: 339 done...:chr1:189465369-189966294
Process 1 case1 window: 340 done...:chr1:189966446-190467139
Process 1 case1 window: 341 done...:chr1:190467863-190967696
Process 1 case1 window: 342 done...:chr1:190969564-191463345
Process 1 case1 window: 343 done...:chr1:191473072-191969747
Process 1 case1 window: 344 done...:chr1:191973382-192473496
Process 1 case1 window: 345 done...:chr1:192473745-192974601
Process 1 case1 window: 346 done...:chr1:192974527-193475989
Process 1 case1 window: 347 done...:chr1:193483188-193982724
Process 1 case1 window: 348 done...:chr1:193984143-194484291
Process 1 case1 window: 349 done...:chr1:194484958-194982466
Process 1 case1 window: 350 done...:chr1:194985562-195487076
Process 1 case1 window: 351 done...:chr1:195495817-195995491
Process 1 case1 window: 352 done...:chr1:196005910-196504167
Process 1 case1 window: 353 done...:chr1:196507189-197009006
Process 1 case1 window: 354 done...:chr1:197008097-197508149
Process 1 case1 window: 355 done...:chr1:197508626-198007425
Process 1 case1 window: 356 done...:chr1:198010621-198510288
Process 1 case1 window: 357 done...:chr1:198512161-199012822
Process 1 case1 window: 358 done...:chr1:199017987-199511942
Process 1 case1 window: 359 done...:chr1:199521409-200022395
Process 1 case1 window: 360 done...:chr1:200022509-200524274
Process 1 case1 window: 361 done...:chr1:200522583-201021808
Process 1 case1 window: 362 done...:chr1:201025282-201524912
Process 1 case1 window: 363 done...:chr1:201525336-202027167
Process 1 case1 window: 364 done...:chr1:202025614-202527049
Process 1 case1 window: 365 done...:chr1:202527724-203029640
Process 1 case1 window: 366 done...:chr1:203030052-203529067
Process 1 case1 window: 367 done...:chr1:203531786-204033666
Process 1 case1 window: 368 done...:chr1:204032364-204532833
Process 1 case1 window: 369 done...:chr1:204532782-205032840
Process 1 case1 window: 370 done...:chr1:205034345-205533937
Process 1 case1 window: 371 done...:chr1:205534865-206030125
Process 1 case1 window: 372 done...:chr1:206035555-206536743
Process 1 case1 window: 373 done...:chr1:206542221-207044019
Process 1 case1 window: 374 done...:chr1:207044597-207544613
Process 1 case1 window: 375 done...:chr1:207544757-208046557
Process 1 case1 window: 376 done...:chr1:208045546-208546686
Process 1 case1 window: 377 done...:chr1:208561543-209062464
Process 1 case1 window: 378 done...:chr1:209063595-209565589
Process 1 case1 window: 379 done...:chr1:209567506-210066541
Process 1 case1 window: 380 done...:chr1:210069910-210570373
Process 1 case1 window: 381 done...:chr1:210576073-211075299
Process 1 case1 window: 382 done...:chr1:211076941-211572948
Process 1 case1 window: 383 done...:chr1:211581690-212079025
Process 1 case1 window: 384 done...:chr1:212083206-212581327
Process 1 case1 window: 385 done...:chr1:212584793-213085166
Process 1 case1 window: 386 done...:chr1:213084921-213584820
Process 1 case1 window: 387 done...:chr1:213587102-214085601
Process 1 case1 window: 388 done...:chr1:214087371-214585474
Process 1 case1 window: 389 done...:chr1:214588746-215089575
Process 1 case1 window: 390 done...:chr1:215088825-215588133
Process 1 case1 window: 391 done...:chr1:215590266-216091431
Process 1 case1 window: 392 done...:chr1:216091970-216593503
Process 1 case1 window: 393 done...:chr1:216592741-217094466
Process 1 case1 window: 394 done...:chr1:217092783-217592310
Process 1 case1 window: 395 done...:chr1:217594507-218093742
Process 1 case1 window: 396 done...:chr1:218101586-218603409
Process 1 case1 window: 397 done...:chr1:218602159-219101473
Process 1 case1 window: 398 done...:chr1:219105272-219605242
Process 1 case1 window: 399 done...:chr1:219606738-220107630
Process 1 case1 window: 400 done...:chr1:220107065-220606481
Process 1 case1 window: 401 done...:chr1:220611921-221113483
Process 1 case1 window: 402 done...:chr1:221114572-221616265
Process 1 case1 window: 403 done...:chr1:221618136-222119442
Process 1 case1 window: 404 done...:chr1:222119737-222609427
Process 1 case1 window: 405 done...:chr1:222620813-223120978
Process 1 case1 window: 406 done...:chr1:223123447-223624998
Process 1 case1 window: 407 done...:chr1:223623713-224124442
Process 1 case1 window: 408 done...:chr1:224124113-224617327
Process 1 case1 window: 409 done...:chr1:224624396-225124776
Process 1 case1 window: 410 done...:chr1:225129815-225631237
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!!!

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。

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?

Hey,

I needed to install libcurl4-openssl-dev on ubuntu 20.04. You might want to add it to your readme.

Thanks

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

Add cram support

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😀.

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 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!

I ran MSIsensor-pro with paired samples successfully and I found some sample has a MSI score great than 1. Actually, the MSI score is the percent of somatic sites, which can not be 1 or great than 1. So, I am confused with the result. I hope you can give me some suggestions.

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 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

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 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

How to classify the MSI high and MSI low ? Looking forward to your reply.

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?

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.

Hi, it would be nice to exclude secondary alignment by default. Thanks.

Hi,

I have the Apple M1 chip (if it helps) and tried the following ways to install the tool:

1. Using binary I get 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

2. Using conda I got 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.

3. So, I used a different conda command that I found using a Google search but then there are some dependencies that I am not sure how to install:

➜  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)

4. None of the above worked easily, so finally I used docker to install and run 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

5. I installed the missing libraries, and was finally able to install using conda, but now it is giving me 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

Hi,

Could you kindly tell me how to use custom MS file to run msisensor-pro?

Thanks very much,

Kimdo

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

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

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!

Does anyone know how to download the Baseline files?

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

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?

Feature request: please can you support cram files?

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!