CONSERTING: integrating copy-number analysis with structural-variation detection.
Xiang ChenPankaj GuptaJianmin WangJoy NakitandweKathryn RobertsJames D DaltonMatthew ParkerSamir PatelLinda HolmfeldtDebbie PayneJohn EastonJing MaMichael RuschGang WuAman PatelSuzanne J BakerMichael A DyerSheila ShurtleffStephen EspyStanley PoundsJames R DowningDavid W EllisonCharles G MullighanJinghui ZhangPublished in: Nature methods (2015)
We developed Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING), an algorithm for detecting somatic copy-number alteration (CNA) using whole-genome sequencing (WGS) data. CONSERTING performs iterative analysis of segmentation on the basis of changes in read depth and the detection of localized structural variations, with high accuracy and sensitivity. Analysis of 43 cancer genomes from both pediatric and adult patients revealed novel oncogenic CNAs, complex rearrangements and subclonal CNAs missed by alternative approaches.
Keyphrases
- copy number
- mitochondrial dna
- deep learning
- genome wide
- convolutional neural network
- loop mediated isothermal amplification
- dna methylation
- papillary thyroid
- real time pcr
- label free
- machine learning
- electronic health record
- single cell
- gene expression
- big data
- squamous cell carcinoma
- artificial intelligence
- squamous cell
- magnetic resonance
- image quality
- young adults
- neural network
- quantum dots