Variant Calling

Getting a VCF file of the mutations in your BAM files, and a reference genome. The network models are specified in the folder path (`*.pb`) and we get the best results if the sequencing data-type in the neural network model is the same as the samples we want to call. Mixing models & sample types will keep the accuracy from its potential, for example, calling Nanopore data with a HiSeq model, calling plant genomes with a model trained on human genomes, or calling cell-free tumor DNA on a genotyping model. This is Magnolia's superpower, get high accuracy by using the correct model for your work.


sudo magnolia call --bam=<file.bam> --ref=<file.fa> --label=<label> --folder=<path> --output=<file.vcf> --verbose --display

Required Parameters

--folder= path to the network model file (.pb)
--bam= name of bam file. The file should meet format specifications, and a great checkpoint is if it works with common tools like GATK or samtools.
--ref= name of reference fasta (fa) file. The reference should be the same version as the bam being called (example hg19).
--label= the labels here should match the labels found in the model. For example AF (allele frequency) creates a VCF with AF=1.0, AF=0.5, for hetero/homozygous, SVTYPE for complex mutations, or somatic/germline for tumors.
--output= VCF file name with all variants found by the model.

Optional Parameters

--region= the limit the vcf input to, all by default (`<chr>:<from>-<to>`) exp:--region=chr20:70000-6000000.
--type= the type of variant to look for, `X` by default for mismatch. Uses CIGAR strings:

M = alignment match
I = insertion to the reference
N = skipped region from the reference
D = deletion from the reference
S = soft clipping
H = hard clipping
P = padding
X = sequence mismatch


--batch= the mini-batch size, `64` by default, but might need adjustment depending on available memory.
--device= on which device to run the training, `cudnn` by default, using GPU acceleration if available (`cudnn`,`cuda`,`cpu`).
--display get visual output during sample selection (if using ssh, use -X option).
--verbose print output on terminal.

Example for INDELs & SNP called together: --type=XID


Load

Files only need to go through the Load process if they are going to be used to train a new model, not for variant calling. This process will take known truths, in as VCF & BAM, parsing the INFO field in the VCF based on the --label parameter. Images of these true locations are generated from the BAM file & each class is stored in its own path within the specified --folder directory.

sudo magnolia load --vcf=<file.vcf> --bam=<file.bam> --ref=<file.fa> --label=<label> --folder=<path(relative)>

Required Parameters

--folder= path where the images generated from truth vcf & bam files will be stored.
--vcf= vcf file containing known mutations to train.
--bam= bam files for the associated truth vcf above.
--ref= reference genome file, should be same reference version used to create the above bam file.
--label= these can be any string within the INFO field of the truth vcf.

Optional Parameters

--region= the limit the vcf input to, all by default (`<chr>:<from>-<to>`) example:--region=chr20:70000-6000000.
--count= indicate the number of truth points to generate, `10K` by default, but `~30K` will help avoid overfitting during training.
--display get visual output during sample selection (if using ssh, use -X option).
--verbose print output on terminal.

VCF format specifications

It is best to use truth VCFs that use the official format specification for the INFO field. This means for example, heterozygous/homozygous mutations are defined by the AF (allele frequency). Structural mutations are specified by the SVTYPE where DEL, INS, DUP, etc define deletions, insertions, duplications. These are however just guidelines, and your truth samples may contain different nomenclature. It can also be helpful to create a none category to generate true negatives.