The breakpoints achieved across the genomic region 1240000bp–1280000bp.

<p>The start bin and end bin of the genomic region having copy number changes, has a high value of △.</p

The overlapped percentage of base pairs of CNV regions detected by the aforesaid algorithms.

<p>The first column represents the CNVs detected by CNV-CH in the region 26200000bp–30000000bp of chromosome 20. The second column represents the corresponding percentage of overlap with the regions, reported in the Database of Genomic Variants (DGV), in the same chromosomal section. For a particular row, each entry shows the percentage of overlap of CNV-CH with DGV, EWT, cnMOPS, CNV-TV and CMDs respectively.</p><p>The overlapped percentage of base pairs of CNV regions detected by the aforesaid algorithms.</p

CNV-CH: A Convex Hull Based Segmentation Approach to Detect Copy Number Variations (CNV) Using Next-Generation Sequencing Data

<div><p>Copy number variation (CNV) is a form of structural alteration in the mammalian DNA sequence, which are associated with many complex neurological diseases as well as cancer. The development of next generation sequencing (NGS) technology provides us a new dimension towards detection of genomic locations with copy number variations. Here we develop an algorithm for detecting CNVs, which is based on depth of coverage data generated by NGS technology. In this work, we have used a novel way to represent the read count data as a two dimensional geometrical point. A key aspect of detecting the regions with CNVs, is to devise a proper segmentation algorithm that will distinguish the genomic locations having a significant difference in read count data. We have designed a new segmentation approach in this context, using convex hull algorithm on the geometrical representation of read count data. To our knowledge, most algorithms have used a single distribution model of read count data, but here in our approach, we have considered the read count data to follow two different distribution models independently, which adds to the robustness of detection of CNVs. In addition, our algorithm calls CNVs based on the multiple sample analysis approach resulting in a low false discovery rate with high precision.</p></div

The overall precision and sensitivity of CNV-CH, EWT, cnMOPS, CMDs and CNV-TV on the basis of their performance on simulated data set.

<p>The black bar shows the precision value in [0, 1] and the gray bar shows the sensitivity value in [0, 1].</p

Results obtained by CNV-CH for the execution instance 1 of real data.

<p>The first column represents the samples taken into consideration for the execution instance 1. The second column of the table shows the total number of variants detected by CNV-CH, and the third column shows the number of detected variants that has been validated.</p><p>Results obtained by CNV-CH for the execution instance 1 of real data.</p

An instance of GC-corrected read count data, corresponding to the genomic region 1240000bp–1280000bp, of 2 test samples, as represented in (a) and (b).

<p>The copy number variation as duplication, was introduced in the genomic segment 1257695bp–1263695bp as represented in (a). The other sample has no variation in this region, as represented in (b).</p

The overall precision and sensitivity of CNV-CH, EWT, cnMOPS, CMDs and CNV-TV for the sample NA19238.

<p>The black bar shows the precision value in [0, 1] and the gray bar shows the sensitivity value in [0, 1].</p

Summary of the CNVs identified in the region 26200000bp–30000000bp, and their validation with Database of Genomic Variants (DGV).

<p>The first two columns of the table show the genomic coordinates (start and end) of the CNVs that were detected by our algorithm. The third and fourth columns of the table show the overlap region, i.e., the genomic coordinates of each of our detected CNVs that has overlapped with the genomic coordinates of the CNVs listed in DGV.</p><p>Summary of the CNVs identified in the region 26200000bp–30000000bp, and their validation with Database of Genomic Variants (DGV).</p

The overall precision and sensitivity of CNV-CH, EWT, cnMOPS, CMDs and CNV-TV, on the basis of their performance on the real data set considered in our work.

<p>The black bar shows the precision value in [0, 1] and the gray bar shows the sensitivity value in [0, 1].</p

The box plot based analysis of detected length of variations, obtained from simulation conducted at each coverage level (C) (2<i>X</i>, 10<i>X</i>, 15<i>X</i> or 20<i>X</i>), with all possible copy number variations (Z) (0, 1, 3, 4 or 5) and at a fixed single-copy variant length of 1Kbp, 3Kbp and 6Kbp as depicted in (a), (b) and (c) respectively, with 10 samples.

<p>The box plot based analysis of detected length of variations, obtained from simulation conducted at each coverage level (C) (2<i>X</i>, 10<i>X</i>, 15<i>X</i> or 20<i>X</i>), with all possible copy number variations (Z) (0, 1, 3, 4 or 5) and at a fixed single-copy variant length of 1Kbp, 3Kbp and 6Kbp as depicted in (a), (b) and (c) respectively, with 10 samples.</p