infoQuant Copy Number software delivers a state-of-the-art algorithm for copy number change detection to your PC. Reliability of gain/loss detection methods becomes extremely important with the increase in data volume. And it has become practically impossible to make copy calls by hand with current high-resolution platforms. Therefore, we have put a sizeable effort behind the novel Robust Binary Segmentation approach to copy number detection built into our products oneClickCGH and CGH Fusion.
Our segmentation approach is built around the framework of the Circular Binary Segmentation. Our particular implementation benefits from the robustness of the bootstrap-based estimation of data distribution. At the same time we employ a two-stage “detection with tracking” algorithm to achieve real-time speed even for ultra-high resolution data.
Robust Binary Segmentation algorithm provides users of high-resolution oligo arrays with an ability to detect both large anomalies and small CNVs with equal reliability. To accommodate needs of different types of users, we also provide a set of controls that facilitates filtering of detected anomalies by their size and level. Additional level of control comes from software's ability to filter out CNV regions present in the set of HapMap reference samples. infoQuant software can further streamline reporting of chromosomal anomalies in a Cytogenetic laboratory by restricting detection to a list of pre-defined regions of interest.
Our customers have been using this detection module on both array CGH and SNP array platforms with great success. Users of SNP platforms can benefit from the use of Copy Number detection in conjunction with infoQuant's LOH detection results and allele-specific visualizations. Joint detection of chromosomal Copy Number events and copy-neutral anomalies makes infoQuant packages irreplaceable for SNP users.
At the same time we realize that users of lower-resolution arrays, like BAC arrays, may need a different approach to copy number detection. That’s why we also allow our users to opt for a simpler algorithm widely accepted for BAC array data: log-ratio thresholding.