Pipeline of stLFR(Single Tube Long Fragment Reads) Resequencing data analysis.
Tools of stLFR(Single Tube Long Fragment Reads) data analysis.
stLFR FAQs is directed to bgi-MGITech_Bioinfor@genomics.cn.
Download source code package from https://github.com/MGI-tech-bioinformatics/stLFR_V1.3.
V1.3, 17/08/2020
1. fix some bugs in result representation
2. introduce smoove for SV detection
3. publish on Docker only
V1.2, 14/07/2020
https://github.com/MGI-tech-bioinformatics/stLFR_V1.2
1. Install docker follow the official website
https://www.docker.com/
2. Then do the following for the workflow:
docker pull rjunhua/stlfr_reseq:v1.3
3. Download and unzip the database
From BGI Cloud Drive:
https://pan.genomics.cn/ucdisk/s/Jvmuii
Or from OneDrive:
https://bgitech-my.sharepoint.com/:f:/g/personal/raojunhua_genomics_cn/EsOCbGxJj7BAuGUkcu5-RZMBYgAMQ-5lWhwAdZrvS4aPNA?e=Oq5q6h
Notes:
1. Please make sure that you run the docker container with at least 45GB memory and 30 CPU.
2. The input is sample list and output directory which descripted below (Main progarm arguments).
3. The log files and temp results will not be removed now, so the memory must be double-checked.
1. Please set the following variables on your machine:
(a) $DB_LOCAL: directory on your local machine that has the database files.
(b) $DATA_LOCAL: directory on your local machine that has the sequence data and "samplelist" file.
"samplelist" must follow the format descripted bellow,
and the *PATH* in "samplelist" must be absolute dicrtory of $DATA_LOCAL.
(c) $RESULT_LOCAL: directory for result.
2. Run the command:
docker run -d -P \
--name $STLFRNAME \
-v $DB_LOCAL:/stLFR/db \
-v $DATA_LOCAL:$DATA_LOCAL \
-v $RESULT_LOCAL:$RESULT_LOCAL \
rjunhua/stlfr_reseq:v1.3 \
/bin/bash \
/stLFR/bin/stLFR_SGE \
$DATA_LOCAL/samplelist \
$RESULT_LOCAL
3. After report is generated:
docker rm $STLFRNAME
two demo stLFR libraries are provided for testing, and every library consists two lanes:
1. T0001-2:
http://ftp.cngb.org/pub/CNSA/data1/CNP0000387/CNS0057111/
2. T0001-4:
http://ftp.cngb.org/pub/CNSA/data1/CNP0000387/CNS0094773/
Name of input file. This is required.
Three columns are required:
1. name : unique sample ID in this analysis
2. path : fastq path(s) for this sample split with colon(:)
3. barcode : sample-barcode for each path split with colon(:), 0 means all used
eg:
SAM1 /DATA/slide1/L01 1-4,5,7-9
SAM2 /DATA/slide1/L02:/DATA/slide2/L02 1-5:6-9
After all analysis processes ending, you will get these files below:
1. HTML report: *_cn.html, *_en.html
2. raw data summary: *.fastqtable.xls
3. stLFR barcode summary: *.fragtable.xls
4. alignment summary: *.aligntable.xls
5. variant summary: *.varianttable.xls
6. haplotype phasing summary: *.haplotype.xls, *.haplotype.pdf
7. evaluation summary (NA12878): *.evaluation.xls
8. quality distrubution in cleanfq: *.Cleanfq.qual.png
9. base distribution in cleanfq: *.Cleanfq.base.png
10. depth distribution in alignment: *.Sequencing.depth.pdf
11. accumulated depth distribution: *.Sequencing.depth.accumulation.pdf
12. insert size distrubition: *.Insertsize.metrics.txt, *.Insertsize.pdf
13. GC bias distrubution: *.GCbias.metrics.txt, *.GCbias.pdf
14. Fragment coverage figure: *.frag_cov.pdf
15. Fragment length distribution figure: *.fraglen_distribution_min5000.pdf
16. Fragment per barcode distribution figure: *.frag_per_barcode.pdf
17. variant CIRCOS: *.circos.svg, *.circos.png, *.legend_circos.pdf
Meanwhile, the following shows the directory structure when the process is executed:
|-- 01.filter // for align
| |__ SAMPLE
| |__ SAMPLE.clean_1.fq.gz
| |__ SAMPLE.clean_2.fq.gz
|-- 02.align // for phase, cnv and sv
| |__ SAMPLE
| |__ SAMPLE.sortdup.bqsr.bam
| |__ SAMPLE.sortdup.bqsr.bam.bai
| |__ SAMPLE.sortdup.bqsr.bam.HaplotypeCaller.vcf.gz
| |__ SAMPLE.sortdup.bqsr.bam.HaplotypeCaller.vcf.gz.tbi
|-- 03.phase // for cnv
| |__ SAMPLE
| |__ phasesplit
| |__ hapblock_SAMPLE_CHROMOSOM
|-- 04.cnv
| |__ SAMPLE
| |__ SAMPLE.CNV.result.xls
|-- 05.sv
| |__ SAMPLE
| |__ SAMPLE.SVresult.xls
|__ file
|__ SAMPLE
|-- alignment
| |__ SAMPLE.sortdup.bqsr.bam
| |__ SAMPLE.sor
|-- CNV
| |__ SAMPLE.CNV.result.xls
|-- haplotype
| |__ SAMPLE.hapblock
|-- sequence
| |__ SAMPLE.clean_1.fq.gz
| |__ SAMPLE.clean_2.fq.gz
|-- SV
| |__ SAMPLE.SV.result.xls
|__ variant
|__ SAMPLE.sortdup.bqsr.bam.HaplotypeCaller.vcf.gz
We provide stLFR_ReSeq workflow by Docker since stLFR_V1.3.
If you like to use clean FASTQ file to run other pipelines or bioinformatics softwares(e.g. de novo assembly or metagenomics), you could get the FASTQ files from subdirectory(named after sample name) in the 01.filter directory of result, such as:
|-- 01.filter |__ SAMPLE |__ SAMPLE.clean_1.fq.gz |__ SAMPLE.clean_2.fq.gz
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