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Comparison between the strategies showed that replicate number profiles derived from the Nimblegen array platform and from CopywriteR had been highly related, with all main aberrations detected with both strategies. Similarly, the correlation between LC-WGS and CopywriteR was highest, albeit that these approaches fashioned sub-clusters based on the utilized segmentation method.

Thus, along with our earlier analyses, these results present that CopywriteR outperforms different exonic learn-primarily based approaches. While evaluating the onTarget and CopywriteR approaches on a dataset of six melanoma PDX (T98-T103) with matched germline samples, we noticed two phenomena that could clarify the higher performance of CopywriteR. Thus, whereas onTarget copy number profiles show differences in technical noise relying on the bait efficiency, CopywriteR does not endure from this limitation.

Copy number info was extracted using CopywriteR and instance areas of the resulting copy quantity profiles are offered, with segmentation values (CBS) depicted in purple. As a result of the enrichment of genomic areas containing such binding sites is relatively poor, we hypothesized that enough off-goal reads could be out there for CopywriteR to derive correct copy number profiles.

In sum, CopywriteR outperforms other exonic learn-primarily based strategies, potentially resulting from both the presence of a high number of outliers in on-goal information and the poor performance of traditional segmentation algorithms on all these data. Others have already shown that LC-WGS can be utilized to generate copy quantity profiles with out the need for a reference 10 , 22 , 30 , 31 CopywriteR mimics LC-WGS in that it makes use of uniformly distributed sequence reads.