Manta is a packaged used to discover structural variants and indels from next generation sequencing data. It is optimized for rapid clinical analysis, calling structural variants, medium-sized indels and large insertions. Manta makes use of split read and paired end information and includes scoring models optimized for germline analysis of diploid genomes and tumor-normal genome comparisons. Major use cases (as listed in the manta manual):

• Joint analysis of small sets of diploid individuals (where 'small' means family-scale -- roughly 10 or fewer samples)
• Subtractive analysis of a matched tumor/normal sample pair
• Analysis of an individual tumor sample

There is also experimental RNA-Seq support.

References:

• Chen et al. Manta: Rapid detection of structural variants and indels for clinical sequencing applications. 2015: http://dx.doi.org/10.1101/024232 BioRxiv

• Manta Main Site

• Module Name: manta (see the modules page for more information)
• Multithreaded/singlethreaded/MPI...
• environment variables set
  • $MANTA_TEST_DATA
• Example files in $MANTA_TEST_DATA

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive -c 10 --mem 10g
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load manta

[user@cn3144 ~]$ configManta.py \
  --normalBam=${MANTA_TEST_DATA}/HCC1954.NORMAL.30x.compare.COST16011_region.bam \
  --tumorBam=${MANTA_TEST_DATA}/G15512.HCC1954.1.COST16011_region.bam \
  --referenceFasta=${MANTA_TEST_DATA}/Homo_sapiens_assembly19.COST16011_region.fa \
  --region=8:107652000-107655000 \
  --region=11:94974000-94989000 \
  --candidateBins=4 --exome --runDir=./test

[user@cn3144 ~]$ tree test
test
|-- [user   4.0K]  results
|   |-- [user   4.0K]  stats
|   `-- [user   4.0K]  variants
|-- [user   7.0K]  runWorkflow.py
|-- [user   3.0K]  runWorkflow.py.config.pickle
`-- [user   4.0K]  workspace

[user@cn3144 ~]$ test/runWorkflow.py -m local -j 10 -g 10

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

The workflow is executed by running the generated runWorkflow.py script. In our case, this is wrapped into a slurm batch script

Create a batch input file (e.g. manta.sh). For example:

#!/bin/bash
module load manta || exit 1
test/runWorkflow.py -m local -j $SLURM_CPUS_PER_TASK -g $((SLURM_MEM_PER_NODE / 1024))

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=4 --mem=10g manta.sh

Create a swarmfile (e.g. manta.swarm). For example:

normal1_vs_tumor1/runWorkflow.py -m local -j $SLURM_CPUS_PER_TASK -g $((SLURM_MEM_PER_NODE / 1024))
normal2_vs_tumor2/runWorkflow.py -m local -j $SLURM_CPUS_PER_TASK -g $((SLURM_MEM_PER_NODE / 1024))
normal3_vs_tumor3/runWorkflow.py -m local -j $SLURM_CPUS_PER_TASK -g $((SLURM_MEM_PER_NODE / 1024))

Submit this job using the swarm command.

swarm -f manta.swarm -g 10 -t 10 --module manta