Table 1.
Some of the popular data pre-treatment and segmentation methods in DoC-based approaches are listed.
Table 2.
Some of the popular annotation and visualization methods for CNVs are listed.
Fig 1.
Flowchart of the iCopyDAV pipeline (input from user: ‘green’, computational steps: ‘Yellow’, Output: ‘Blue’).
Table 3.
Structural and functional annotations provided by the annotate module in iCopyDAV.
Fig 2.
(a) F-score plots, and (b) Recall & Precision plots as a function of sequencing depth. In (b) Recall and precision values are depicted by ‘closed’ and ‘open’ symbols respectively. Performance of TVM (‘circle’) and CBS (‘triangle’) is shown for the two CNV sets: small (≤ 1Kb) shown as dashed lines and large (> 1Kb) as solid lines. Error bars represent standard deviation in each group. Bin size = 50 bp.
Fig 3.
Box plots representing breakpoint error in CNV detection as a function of sequencing coverage using (a) TVM and (b) CBS segmentation approaches in simulated data. Bin size = 50 bp.
Fig 4.
Performance of TVM (‘circle’) and CBS (‘triangle’) approaches in predicting copy gain (‘closed’ symbol) and copy loss (‘open’ symbol) events in simulated data is shown.
Dashed line corresponds to detection of small CNVs (≤ 1 Kb) and solid line for large CNVs (> 1 Kb). Bin size = 50 bp.
Fig 5.
Performance (F-score) of iCopyDAV with three other DoC-based tools (using default parameters for data pre-treatment and segmentation approaches) shown as a function of sequencing coverage.
Error bars represent standard deviation in each group.
Fig 6.
Size distribution of copy gain (solid) and copy loss (striped) events shown for various combinations of data pre-treatment and segmentation approaches in low sequence coverage data (6×) for Chr 1 of NA12878 sample.
Bin size = 300 bp.
Fig 7.
Performance of iCopyDAV is shown for low sequence coverage data (6×) of Chr 1 of NA12878 for mappability threshold values (a) 0.5 and (b) 0.8. Recall and precision values for various combinations of GC bias correction and segmentation algorithms are computed with respect to the six studies reported in DGV. Bin size = 300 bp.
Fig 8.
Size distribution of copy gain (solid) and copy loss (striped) events shown for various combinations of data pre-treatments and segmentation approaches in high sequence coverage data (35×) for Chr 1 of NA12878 sample.
Bin size = 300 bp.
Fig 9.
Performance of iCopyDAV is shown on high sequence coverage data (35×) of Chr 1 of NA12878 sample for mappability threshold values (a) Mth = 0.5 and (b) Mth = 0.8. Recall and precision values for various combinations of GC bias correction and segmentation algorithms are computed independently for the six studies reported in DGV. Bin size = 300 bp.
Table 4.
Performance of various combinations of GC bias correction and segmentation approaches in the detection of CNVs of different size (small/large) and type (copy gain/loss) are summarized for high sequence coverage data (Mth = 0.8).
Table 5.
Comparison of CNVs detected in iCopyDAV (combined approach, median + (CBS +TVM)) with ReadDepth, Control-FREEC and CNVnator (Mth = 0.8, window size 300 bp).
Fig 10.
Performance of iCopyDAV with three DoC-based methods, ReadDepth, Control-FREEC, and CNVnator (using default parameters) is shown for high sequence coverage data (35×) of Chr 1 of NA12878 sample.
Fig 11.
The location of CNVs, gain in ‘red’ and loss in ‘blue’ is shown (a) along Chr 1 of NA12878 sample, and (b) at the locus 1q21.1 spanning NBPF gene-family, which is rich in segmental duplications.
Table 6.
Comparative analysis of various parameters affecting variant calling in TVM and CBS segmentation approaches.