Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network

1 INTRODUCTION . A copy number variant ( CNV ) is a genetic mutation where a stretch of DNA is completely lost or repeated more than once compared to a reference genome . CNV sizes range from 50 bases to 3 million bases or more with two major types : duplication if the DNA sequence is repeated and deletion if a DNA sequence is missing . CNVs are spread along a genome , which account for 4.8 % to 9.5 % of human genome ( Zarrei et al. , 2015 ) . They are known to influence many complex diseases including autism ( Sebat et al. , 2007 ) , bipolar disorder ( Green et al. , 2016 ) , schizophrenia ( Stefansson et al. , 2008 ) , and Alzheimer ’ disease ( Cuccaro et al. , 2017 ) as well as gene expression ( Chiang et al. , 2017 ) . As CNVs may overlap with a large portion of a genome , such as one gene or even multiple genes , their effect on disease may be substantially larger than that of single nucleotide variants , and hence CNVs often play important roles in the genetic mechanisms of diseases and complex traits . With the advent of next-generation DNA sequencing technologies over the past decade , the resolution and scale of CNV detection has been greatly improved as large-scale sequencing studies become feasible . However , CNVs are still challenging to detect from short-read next-generation DNA sequencing techniques due to the significantly varying sizes of CNVs and their similarity with common DNA sequencing artifacts . Many computational methods have been developed for the discovery of CNVs from short-read DNA sequencing data , but their performance is often unsatisfactory due to low accuracy and high computational cost ( Kosugi et al. , 2019 ) . The main reason for this is that previous methods ( Rausch et al. , 2012 ; Abyzov et al. , 2011 ) mainly rely on the statistical analysis of the signals from read alignments ( a process that maps reads from DNA sequencing data to the reference genome ) , which often fail to employ all useful features of DNA sequencing data and require significant computational resources . Therefore , there is a need for a novel , sophisticated computational tool to improve CNV detection with higher accuracy and efficiency . Here , we present CNV-Net , a new approach to identify CNVs from DNA sequencing data using a six-layer deep convolutional neural network ( CNN ) . CNV-Net first encodes the reads and their related information from DNA sequencing data such as the type of base , base coverage ( the number of reads covering a position ) , and base quality of each position into the RGB channels of images . Then it uses a deep CNN to predict these images as deletions , duplications , or false positives ( not a CNV ) breakpoints . Note that a CNV is defined by two breakpoints , that is , its start and end positions . Although it has been shown that CNNs such as DeepVariant ( Poplin et al. , 2018 ) and Clairvoyante ( Luo et al. , 2019 ) can accurately detect single nucleotide variants from similarly encoded images or tensors , few CNNs have been designed for the identification of CNVs . The key advantage of CNVNet over other methods is the application of deep learning , which allows learning features with higher complexity from DNA sequencing data . We train the network with the Genome in a Bottle Consortium NA12878 dataset ( Pendleton et al. , 2015 ) , and demonstrate CNV-Net performance on a high-quality benchmarking dataset acquired from a well-studied cell line , HG002 ( Zook et al. , 2020 ) . We also compare its performance to that of previous methods . 2 METHODS . CNV-Net first encodes sequencing information into pileup images and classifies each image as a deletion , duplication , or false positive ( not a CNV ) . In this section , we describe how we generate pileup images , that is , the input to CNV-Net , as well as the design of the CNN . 2.1 PILEUP IMAGE GENERATION . We convert the information of mapped reads from DNA sequencing data ( provided in BAM file format ) and reference genome information to 201×55 pixel image representation of 201-base wide regions . Training data pileup images contain a CNV breakpoint ( start or end position ) centered in the image , capturing the 100 positions left and right each CNV breakpoint . Using the RGB channels of an image , this snapshot is able to capture features of the reads at specific locations in the reference genome . As shown in Figure 1 , the first five rows of the image capture the reference genome across a region of 201 base pairs by encoding the R channel of each pixel with an individual base in that position : A ’ s as 250 , G ’ s as 180 , T ’ s as 100 , C ’ s as 30 , and N ’ s as 0 . Under the five rows representing the reference genome are another five rows that capture the read depth ( coverage ) at that position . The R and G channels are encoded with an adjusted read depth value between 0 and 255 . The following 45 rows capture the mapped reads from DNA sequencing . The R channel encodes for the nucleotide , which is the same as the reference genome , and the G channel encodes for the base quality found in the BAM file , which is adjusted to be between 0 and 255 . Lastly , the B channel encodes for strand directionality : 70 if positive and 240 if negative . Each 201×55 pixel pileup image , thus , captures coverage and reads specific to the 201-base region of the reference genome such that the sequencing data may be re-created from a series of consecutive pileup images . If there are more than 45 reads mapping to a specific region , those reads are discarded ; we instead compensate by capturing this information through the rows that encodes coverage . If no reads map to the region , the image is left blank below the first five rows for the reference genome . 2.2 DESIGN OF CONVOLUTIONAL NEURAL NETWORK . As presented in Figure 2 , CNV-Net consists of four convolutional layers and two fully connected layers with a total of 5,110,979 parameters . Each of the four convolutional layers uses 3×3 kernels and the RELU activation function . Each convolutional layer is followed by batch normalization , average pooling , and 0.4 dropout . Arrays are flattened in between the four convolutional layers and two fully connected layers followed by softmax output to predictions for CNV breakpoint presence in the 201-base window : duplication , deletion , or false positive ( not a CNV ) . In training CNV-net , we use a standard Adam optimizer ( Kingma & Ba , 2014 ) with a sparse categorical cross-entropy loss function . We use a cyclical learning rate initialized at 1e-5 and maximal 1e-2 . To reduce overfitting , we train the model until the accuracy can not be improved in 15 epochs . The 201-base regions are centered around the CNV breakpoints , that is , the starts and ends of CNVs in the training set capturing the 100 positions directly before and after each CNV breakpoint . False positive images include random 201-base regions outside of CNVs . From the NA12878 dataset , a total of 52,243 pileup images are labeled as duplications , deletions , and false positives from the training set randomly split into 70 % , 20 % , 10 % categories for training set , validation set , and test set for the network , respectively . 3 RESULTS . In this study , we use two standard structural variation benchmarking datasets : NA12878 and HG002 . Both these benchmarking datasets are mapped to the GRCh37 reference genome . NA12878 is a well-studied genome provided by the Genome in a Bottle Consortium . It is whole-genome sequenced with 50× coverage and each read in the sequencing data is 101 bases long . From the NA12878 image dataset ( Pendleton et al. , 2015 ) , we generate images for 22,936 duplications , 18,614 deletions , and 10,693 false positives ( not CNVs ) breakpoints . The HG002 dataset ( Zook et al. , 2020 ) is another robust benchmarking dataset derived by the Genome in a Bottle Consortium , which is sequenced with 60× coverage . Reads in the HG002 dataset are all 148 bases long . As we only consider CNVs passing the quality control filter , this dataset contains 174 duplications , 10,668 deletions , and 5,205 false positives breakpoints post-filtering . We train the convolutional neural network on the NA12878 dataset and test the fitted model on the HG002 dataset . We also compare CNV-Net with popular existing tools for CNV detection , including BreakDancer ( Chen et al. , 2009 ) , CNVnator ( Abyzov et al. , 2011 ) , Delly ( Rausch et al. , 2012 ) , Lumpy ( Layer et al. , 2014 ) , and Manta ( Chen et al. , 2016 ) . It is important to note that CNVNet makes predictions using the pileup images of CNVs where their start and end positions are given , and hence we assume that candidate CNV positions are given as input to CNV-Net . However , other methods find the candidate CNV positions using the information on how reads are mapped to the reference genome and detect CNVs on those candidate CNV positions . The NA12878 dataset is preprocessed and converted into RGB images as described in the Methods section . We split the CNVs in NA12878 into 70 % , 20 % , 10 % for training set , validation set , and test set , respectively . We train the CNV-Net with the training set and report its performance metrics on the test set . As shown in Table 1 , CNV-Net identifies duplications with F1-score of 0.7833 , deletions with F1-score of 0.7473 , and false positives with F1-score of 0.7124 . This result shows that CNV-Net achieves high accuracy in the test set of the NA12878 dataset . Next , we evaluate our fitted CNV-Net on the HG002 dataset and compare it with five existing tools for CNV detection . This training scheme represents the real data analysis as we will train CNVNet with CNVs from known genomes and testing it on different genomes . As demonstrated in Table 2 , CNV-Net achieves the highest F1-score in CNV detection in the HG002 dataset among all six methods . It also has the highest recall and the second highest precision . This result shows that CNV-Net is more accurate in CNV detection than current methods . We then compare the time cost of applying different methods to the HG002 dataset . CNV-Net is significantly faster than other methods ( Table 3 ) . That is because a fitted convolutional neural network is efficient in making predictions , however , it may be time-consuming during model training . Run time is effectively front-loaded into model training instead of a concurrent statistical analysis . Another reason for this can be that CNV-Net makes predictions on candidate CNVs so it does not need to spend time searching for candidate CNVs in DNA sequencing data as if other methods do . 4 CONCLUSION . CNV-Net encodes DNA sequencing information into RGB images and applies a deep convolutional neural network to learn complex features from DNA sequencing data to discover CNVs . We demonstrate that CNV-Net can detect CNVs breakpoints from DNA sequencing data with significantly higher accuracy and efficiency than existing tools . To the best of our knowledge , CNV-Net is the first tool to use a CNN to detect CNVs from DNA sequencing data and achieve high performance on the standard benchmarks . It changes the problem of CNV detection from a process of statistical modeling requiring professional expertise into a process of optimizing a general deep learning model . Besides , although we benchmark CNV-Net using only human samples in this study , it can also be applied to DNA sequencing data of other species . One limitation of CNV-Net is its need to know candidate CNV positions , potential start and end positions of CNVs , before applying the CNN . This is because CNV-Net makes predictions on pileup images generated at the start and end positions of candidate CNVs . In our analysis , we used candidate CNV positions in human genomes , which were previously identified by the Genome in a Bottle Consortium using a suite of other algorithms . In genome sequencing datasets where CNVs have not been analyzed , these candidate CNV positions may not be available . One solution to this problem may be using candidate CNV positions detected by other CNV detection algorithms , especially algorithms with high recall , as CNV-Net can remove false positive CNVs from those CNV positions . Additionally , we may also develop a sensitive candidate CNV detection algorithm , which may have low precision but high recall . Our future research direction involves identifying candidate CNV positions from genome sequencing datasets and incorporating them with CNV-Net ; nevertheless , CNV-Net demonstrates potential as a valuable and effective tool for CNV detection .

Summary: They authors describe a method to detect structural variation from aligned sequencing reads in a genome browser view. Their model encodes this genome browser view into an RGB image and applies a deep convolutional neural network to classify variant type (or no variant). They make use of curated variant annotations to train and test their model.