MosaicForecast is a machine learning method that leverages read-based phasing and read-level features to accurately detect mosaic SNVs (SNPs, small indels) from NGS data. It builds on existing algorithms to achieve a multifold increase in specificity.

References:

• Dou Y, Kwon M, Rodin RE, Cortés-Ciriano I, Doan R, Luquette LJ, Galor A, Bohrson C, Walsh CA, Park PJ. Accurate detection of mosaic variants in sequencing data without matched controls Nat Biotechnol. 2020 Mar;38(3):314-319. doi: 10.1038/s41587-019-0368-8. Epub 2020 Jan 6. PubMed |  Journal

• mosaicforecast Main Site:Main Site

• Module Name: mosaicforecast (see the modules page for more information)
• Mosaicforecast was installed as a container. There are 6 functions included in the packages, and please call them directly (without python or Rscript).

          Phase.py
  	ReadLevel_Features_extraction.py
  	Prediction.R
  	Train_RFmodel.R
  	PhasingRefine.R
  	MuTect2-PoN_filter.py
  	

• Environment variables set
  • $MOSAIC_TESTDATA #include demo and umap_mappability(bigWig file,k=24) from hg19
  • $MOSAIC_MODEL #include pre-trained model to predic genotypes, please choose the one match your sequence depths.

Allocate an interactive session and run the program.
Sample session (user input in bold):

[user@biowulf]$ sinteractive --cpus-per-task=2 --mem=2G
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 mosaicforecast
[user@cn3144 ~]$ Phase.py
Usage: python Phase.py bam_dir output_dir ref_fasta input_positions(file format:chr pos-1 pos ref alt sample, sep=\t) min_dp_inforSNPs(int) Umap_mappability(bigWig file,k=24) n_threads_parallel sequencing_file_format(bam/cram)

Note:
1. Name of bam files should be "sample.bam" under the bam_dir, and there should be corresponding index files.
2. There should be a fai file under the same dir of the fasta file (samtools faidx input.fa).
3. The "min_dp_inforSNPs" is the minimum depth of coverage of trustworthy neaby het SNPs.
4. Bam file is preferred than cram file, as the program would run much more slowly if using cram format.

[user@cn3144 ~]$ mkdir mosaicforecast_test && cd mosaicforecast
[user@cn3144 ~]$ cp -r ${MOSAIC_TESTDATA:-none}/* .
[user@cn3144 ~]$  Phase.py ./demo/ test_out \
                     /fdb/GATK_resource_bundle/b37-2.8/human_g1k_v37_decoy.fasta \
                     ./demo/test.input 20 k24.umap.wg.bw 2 bam
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

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

#!/bin/bash
#SBATCH --cpus-per-task=2
#SBATCH --mem=2G
#SBATCH --time=2:00:00
#SBATCH --partition=norm

set -e
module load mosaicforecast
cp -r ${MOSAIC_TESTDATA:-none}/* .
cp -r ${MOSAIC_MODEL:-none}/* .
Prediction.R demo/test.SNP.features models_trained/250xRFmodel_addRMSK_Refine.rds Refine test.SNP.predictions

Submit the job:

sbatch mosaicforecast.sh

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

       Prediction.R demo/test.SNP.features models_trained/250xRFmodel_addRMSK_Refine.rds Refine SNP.predictions   
       Prediction.R demo/test.DEL.features models_trained/deletions_250x.RF.rds Phase DEL.predictions

    

Submit this job using the swarm command.

swarm -f job.swarm [-g #] --module mosaicforecast