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Image Search Results
Journal: Bioinformatics
Article Title: Multi-factor data normalization enables the detection of copy number aberrations in amplicon sequencing data
doi: 10.1093/bioinformatics/btu436
Figure Lengend Snippet: Comparison of RC normalization and accuracy of CNV calls achieved by ONCOCNV, ADTEx and NextGENe for samples from dataset A. ( A ) Correlation between NRCs and log ratio values of array CGH. ( B ) Prediction accuracy. Accuracy = (#True predictions)/(#All predictions), where each prediction corresponds to a gene copy number status (gain, neutral or loss). ( C ) True-positive rate. ( D ) False-positive rate
Article Snippet: We decided to compare our CNA calling method in ultra-deep targeted sequencing data with two WES-specific tools: ADTEx ( Amarasinghe et al. , 2013 ), a method based on hidden Markov models (HMMs) for CNA calling in targeted (exome) sequencing data, and
Techniques: Comparison
Journal: Bioinformatics
Article Title: Multi-factor data normalization enables the detection of copy number aberrations in amplicon sequencing data
doi: 10.1093/bioinformatics/btu436
Figure Lengend Snippet: Example of CNVs called by ONCOCNV, ADTEx and NextGENe (Sample A1). ( A) Array CGH profile for sample A1, segmented using cghseg: purple (loss), orange (gain). x -axis corresponds to probe indexes. ( B) Copy number profile calculated by ONCOCNV. x -axis corresponds to amplicon indexes. ( C) Agreement between CNVs predicted from array CGHs and amplicon sequencing. Each vertical bar denotes a gene copy number status: white (neutral), purple (loss) and orange (gain)
Article Snippet: We decided to compare our CNA calling method in ultra-deep targeted sequencing data with two WES-specific tools: ADTEx ( Amarasinghe et al. , 2013 ), a method based on hidden Markov models (HMMs) for CNA calling in targeted (exome) sequencing data, and
Techniques: Amplification, Sequencing