Detection Of Clinically Relevant Copy Number Variants From Short Read Sequencing Data For Genomic Diagnostics

Author : Fatemeh Zare
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Download or read book Developing Novel Copy Number Variation Detection Methods Using Emerging Sequencing Data written by Fatemeh Zare and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Copy number variations (CNVs) are an important type of structural variations that result in either gain or loss of chromosomal regions. CNVs include duplication (more than normal copy number) and deletion (fewer than normal copy number) of genomic regions. CNV has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease usceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Due to recent advances in sequencing technologies, next-generation sequencing (NGS) analysis has been widely applied to detecting CNVs. The key feature of NGS is that it generates huge amounts of data (usually at the scale of gigabytes), which requires to use efficient methods. The depth of coverage (DOC) approach is the most appropriate method to identify CNVs for NGS data. Both bulk and single-cell sequencing (SCS) generate read-depth (RD) data and have been used to detecting CNVs. The main di↵erence between single-cell sequencing and traditional bulk sequencing is that single-cell sequencing requires a further step to amplify a single-cell genome. Due to the small amount of DNA in one cell, an amplification step is performed before sequencing in SCS, and this step introduces further biases in the RD. On the other hand, the RD in current SCS data is still significantly lower than that from traditional bulk sequencing, even with the amplification stage. In other words, in SCS data, the signal-to-noise ratio is much lower than in data for the bulk sequencing. In general, the DOC-based tools for CNVs detection are divided into two major steps: 1) preprocessing, and 2) segmentation. In the preprocessing step, noise and biases are reduced from a read-count signal, and in the segmentation part, CNV segments are identified by merging the regions with similar read-count values. In this dissertation, first we propose methods for detecting CNVs from bulk sequencing data. For this purpose, first, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. Then we introduce a novel preprocessing pipeline to improve the detection accuracy of CNVs in heterogeneous next-generation sequencing data such as cancer whole-exome sequencing data. We employed several normalizations to reduce biases due to GC content, mappability, and tumor contamination. We also developed a novel efficient and effective smoothing approach based on the Taut String method to reduce noise and increase the detection power of the CNV detection methods. Also, we proposed a novel efficient segmentation algorithm that integrates information from partially mapped (soft-clipped) reads with read depth data for more precise CNV detection. The proposed method employs an efficient implementation of the solution to the change-point optimization problem, Taut String, to smooth the read depth data and to generate piecewise constant signals as CNV segments. Furthermore, we propose a novel segmentation algorithm based on the modified Taut String to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed segmentation method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method. Also, in this dissertation, we study CNV detection from whole genome single cell sequencing. Next-generation sequencing has been successfully adapted to the sequence of complete genomes at the single-cell level. Single-cell sequencing (SCS) is a useful tool to determine somatic genomic heterogeneity. Precise identification of CNVs may help to understand some of the genetic origins of cancer and to develop targeted drugs. General steps of a CNV detection from single-cell sequencing are GC correction, binning, removal of outlier bins, segmentation, removal of outlier cells. Single-cell sequencing requires a further step to amplify a single-cell genome. Amplification of genome will eventually add significant levels of noise and some genomic regions being amplified more than others and introduces biases to the RD. Also, single-cell DNA sequencing data often has high technical noises. Also, choosing an appropriate bin size is an important consideration, since the larger bin sizes have more reads and less noise. However, using larger bins resulting in lower resolution. The current tools designed for bulk sequencing are not optimized for single-cell data. Therefore, utilizing advanced novel segmentation, normalization, and de-noising techniques that are explicitly designed for SCS data is necessary. In this dissertation, we present a novel CNV detection algorithm based on the modified Taut String method to detect CNVs from SCS data. The proposed method, first, finds the optimal window size for counting reads from the whole genome SCS data using the AIC approach and then removes outlier from the read count signal. Then, using the modified Taut String algorithm, the method detects CNVs and identifies significant change points. Finally, it uses the hierarchical clustering of cells based on their CNV patterns and employs z-score to improve CNV detection across the cells. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of sensitivity and false discovery rate.