This application identifies pseudogenes in Whole-Exome and Whole-Genome Next Generation Sequencing data.
docker run --rm \
-v file.bam:/input/file.bam \
-v file.bai:/input/file.bai \
-v hg38.fa:/reference/reference.fa \
-v /path:/output \
zgmwum/psedogenesearcher \
--only-soft-clipped \
--threads 12 \
--standard-genome hg38 \
--bam /input/file.bam \
--output-json /output/out.json \
--output-vcf /output/out.vcf \
--reference /reference/reference.fa
Standard usage may look like this
java -jar PseudogeneSearcher-0.0.1-SNAPSHOT.jar \
--reference /reference/hg38.fasta \
--bam /input/file.bam \
--only-soft-clipped \
--threads 12 \
--output /output/1.output \
--output-json /tmp/1.json \
--output-vcf /tmp/1.vcf \
--standard-genome hg38
Usage:
Options:
--bam
Input bam file
--genes
Genes description from RefSeq UCSC hgTables
--help
--omit-duplicated-reads
Omit duplicates
Default: false
--only-soft-clipped
Use data only from soft clipped reads, omit these having deletion but no soft clipped ends
Default: false
--output
Ouput file, give - for stdout
* --output-json
Ouput file for json
* --output-vcf
Ouput file for vcf
--position
Only selected position, in format chr9:39898200-39909240
--pseudogenes
Pseudogenes gtf description
--reference
Reference fastq
--sample-name
Sample name
Default: name
--standard-genome
Value: hg19 or hg38, if given program uses it's own databases
--threads
Number of threads
Default: 1
--two-pass-run
Read bam file twice, cannot be used with stdin as bam input
Default: false
Due to the way this software is used, not all arguments are fully tested. Please check the Testing section belof to find the supported way of running PseudogeneSearcher.
Program will create both .vcf and .json file. However, I encourage to use the .json file and to make postprocessing of the file to remove false positives according to adjust the precision and recall due to specific needs.
In our analysis we are using Apache Spark for postprocessing.
spark.read.json("input.json")
.filter($"pass"===true) // take only 'passed' findings
.filter($"fracIntronesCoveredIS">0.8) // take only these retropseudogenes where fraction of intrones with support of removal by the insert size is greater than 80%
.filter($"fracIntronesCoveredClip">0.8) // take only these retropseudogenes where fraction of intrones with support of removal by the soft clipped reads aligning to the exon-exon junction is greater than 80%
.withColumn("introns", size($"intronDetails"))
.filter($"introns" > 2) // take only these retropseudogenes that have more than two intrones
.write.json("filtered.json")
If you have a population of samples you can do population analysis to find unique retropseudogenes or samples with unusual amount of restropseudogenes by executing the code below. To raise the precision of population analysis the expectation of number of reads being higher that 2 for each intron was added. Please adjust it according to your specific needs.
spark.read.json("/aDirectoryWithJsons/")
.filter($"pass"===true)
.filter($"fracIntronesCoveredIS">0.8)
.filter($"fracIntronesCoveredClip">0.8)
.withColumn("introns", size($"intronDetails"))
.filter($"introns" > 2)
.withColumn("introne", explode($"intronDetails"))
.filter($"introne.readsSupportingRemovalByIS" > 2)
.filter($"introne.readsSupportingRemovalByAlignment" > 2)
.groupBy($"sampleName",$"motherTranscript.gene" as "gene")
.agg(collect_set($"pass"))
.groupBy("sampleName")
.agg(collect_set($"gene") as "gene")
.sort(size($"gene"))
.coalesce(1)
.write.json("sampleToPseudogenes.json")
spark.read.json("/aDirectoryWithJsons/")
.filter($"pass"===true)
.filter($"fracIntronesCoveredIS">0.8)
.filter($"fracIntronesCoveredClip">0.8)
.withColumn("introns", size($"intronDetails"))
.filter($"introns" > 2)
.withColumn("introne", explode($"intronDetails"))
.filter($"introne.readsSupportingRemovalByIS" > 2)
.filter($"introne.readsSupportingRemovalByAlignment" > 2)
.groupBy($"sampleName",$"motherTranscript.gene" as "gene")
.agg(collect_set($"pass"))
.groupBy("gene")
.agg(collect_set($"sampleName") as "sampleName")
.sort(size($"sampleName"))
.coalesce(1)
.write.json("pseudogeneToSamples.json")
Check test-docker.sh and test-shell.sh scripts. They download the reference sequence, input data and runs PseudogeneSearcher respectively using Docker or using Java.
If your sample is so big, that you get into memory issues:
Recommended solution is to use the --position argument to process only a part of the huge input bam in a sigle run. For example: --position chr2:1-242194529 to test chr2.
To reate an executable jar run:
mvn clean compile package
The jar file will be created in the target directory.
To create and push a docker image run:
mvn compile jib:build