Short Tandem Repeat (STR) genotyper
|Author||Haibao Tang (tanghaibao)|
|Smriti Ramakrishnan (smr18)|
|License||See included LICENSE|
Process a list of TRED (trinucleotide repeats disease) loci, and infer the most likely genotype.
Make sure your Python version >= 2.7 (tested in ubuntu, Python 3 not yet supported):
pip install --user -U git+git://github.com/humanlongevity/tredparse.git
For accessing BAMs that are located on S3, please refer to docker/tredparse.dockerfile for installation of SAMTOOLS/pysam with S3 support.
Or, you can simply build and use the docker image:
docker pull humanlongevity/tredparse docker run -v `pwd`:`pwd` -w `pwd` humanlongevity/tredparse \ tred.py --tred HD test.bam
First specify the input bam paths and sample keys in a CSV file, like tests/samples.csv. This file is comma separated:
#SampleKey,BAM,TRED t001,tests/t001.bam,HD t002,tests/t002.bam,DM1
If third column is omitted, then all 30 TREDs are scanned. For example:
#SampleKey,BAM t001,tests/t001.bam t002,tests/t002.bam
Please also note that the BAM path can start with http:// or s3://, provided that the corresponding BAM index can be found.
Run tred.py on sample CSV file and generate TSV file with the genotype:
tred.py tests/samples.csv --workdir work
Highlight the potential risk individuals:
tredreport.py work/*.json --tsv work.tsv
The inferred “at-risk” individuals show up in results:
[DM1] - Myotonic dystrophy 1 rep=CAG inherit=AD cutoff=50 n_risk=1 n_carrier=0 loc=chr19:45770205-45770264 SampleKey inferredGender Calls DM1.FR DM1.PR DM1.RR DM1.PP t002 Unknown 5|62 5|24 ...|1;39|1;40|1;42|1;43|1;46|2 49|3;50|8 1 [HD] - Huntington disease rep=CAG inherit=AD cutoff=40 n_risk=1 n_carrier=0 loc=chr4:3074877-3074933 SampleKey inferredGender Calls HD.FR HD.PR HD.RR HD.PP t001 Unknown 15|41 15|4 ...|1;21|1;24|2;29|1;34|1;41|1 1
One particular individual t001 appears to have 15/41 call (one allele at 15 CAGs and the other at 41 CAGs) at Huntington disease locus (HD). Since the risk cutoff is 40, we have inferred it to be at-risk.
A .report.txt file will also be generated that contains a summary of number of people affected by over-expanded TREDs as well as population allele frequency.
To better understand the uncertainties in the prediction, we can plot the likelihood surface based on the model. Using the same example as above at the Huntington disease case, we can run a command on the JSON output, with option --tred HD to specify the locus.
tredplot.py likelihood work/t001.json --tred HD
This generates the following plot:
The server/client allows tredparse to be run as a service, also showing the detailed debug information for the detailed computation.
Install meteor if you don’t have it yet.
curl https://install.meteor.com/ | sh
Then build the docker image to run the command, then run the server.
cd docker make build cd ../server meteor npm install meteor
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