Segmentation

After a case sample has been normalized, it goes through a segmentation stage. There are multiple segmentation algorithms implemented in DRAGEN, including the following:

•

CBS (Circular Binary Segmentation)

•

SLM (Shifting Level Models)

The SLM algorithm has three variants, SLM, HSLM, and ASLM. HSLM (Heterogeneous SLM) is for use in exome analysis and handles target capture kits that are not equally spaced. ASLM (Adaptive SLM) includes additional sample-specific estimation of technical variability of depth of coverage (as opposed to changes in copy number), based on the median variance within fixed windows or a preliminary set of segments based on b-allele ratios, and can provide more robustness to "noisy" or "wavy" samples.

The default segmentation algorithm in use is SLM for germline whole genome processing, ASLM for somatic whole genome processing, and CBS for whole exome processing.

For the targeted sequencing workflows, you can also run with a --cnv-segmentation-bed. The option pre-defines the segments to estimate copy numbers for and skips the segmentation step of the workflow. See Targeted Segmentation (Segment BED)

Option

Description

--cnv-segmentation-mode

Specifies the segmentation algorithm to perform. The following values are available:

•

segment-bed

•

cbs

•

slm—The default for germline WGS analysis.

•

aslm—The default for somatic WGS analysis.

•

hslm—The default for targeted/WES analysis.

--cnv-merge-distance

Specifies the maximum number of base pairs between two segments that would allow them to be merged. The default value is 0 for WGS, which means the segments must be directly adjacent. For WES analysis this parameter is disabled by default due to the spacing of targeted intervals.

--cnv-merge-threshold

Specifies the maximum segment mean difference at which two adjacent segments should be merged. The segment mean is represented as a linear copy ratio value. The default is 0.2 for WGS and 0.4 for WES. To disable merging, set the value to 0.

Circular Binary Segmentation

Circular Binary Segmentation is implemented directly in DRAGEN and is based on A faster circular binary segmentation for the analysis of array CGH data with enhancements to improve sensitivity for NGS data.

The following options control Circular Binary Segmentation.

Option	Description
--c-alpha	Specifies the significance level for the test to accept change points. The default is 0.01.
--cnv-cbs-eta	Specifies the Type I error rate of the sequential boundary for early stopping when using the permutation method. The default is 0.05.
--cnv-cbs-kmax	Specifies maximum width of smaller segment for permutation. The default is 25.
--cnv-cbs-min-width	Specifies the minimum number of markers for a changed segment. The default is 2.
--cnv-cbs-nmin	Specifies the minimum length of data for maximum statistic approximation. The default is 200.
--cnv-cbs-nperm	Specifies the number of permutations used for p-value computation. The default is 10000.
--cnv-cbs-trim	Specifies the proportion of data to be trimmed for variance calculations. The default is 0.025.

Shifting Level Models Segmentation

The Shifting Level Models (SLM) segmentation mode follows from the R implementation of SLMSuite: a suite of algorithms for segmenting genomic profiles.

Option	Description
--cnv-slm-eta	Baseline probability that the mean process changes its value. The default is 4e-5.
--cnv-slm-fw	Minimum number of data points for a CNV to be emitted. The default is 0, which means segments with one design probe could in effect be emitted.
--cnv-slm-omega	Scaling parameter modulating relative weight between experimental/biological variance. The default is 0.3.
--cnv-slm-stepeta	Distance normalization parameter. The default is 10000. This option is only valid for HSLM.

Regardless of the segmentation method, initial segments are split across large gaps where depth data is unavailable, such as across centromeres.