You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data 
Chapter Title:  Computational Tools for Estimating Copy Number Alteration. 
1. Abstract of Cited Paper (BIB001):  Cancer results from somatic alterations in key genes, including point mutations, copy-number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to segment equal copy numbers from massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of approximately 14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over twofold better precision for localizing breakpoints (typically, to within approximately 1 kilobase). 
2. Abstract of Cited Paper (BIB002):  Motivation: The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection. ::: ::: Results: We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We apply our method to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design. ::: ::: Availability: CRAN package ‘ExomeCNV’. ::: ::: Contact:fsathira@fas.harvard.edu; snelson@ucla.edu ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online. 
3. Abstract of Cited Paper (BIB003):  Motivation: High-throughput techniques facilitate the simultaneous measurement of DNA copy number at hundreds of thousands of sites on a genome. Older techniques allow measurement only of total copy number, the sum of the copy number contributions from the two parental chromosomes. Newer single nucleotide polymorphism (SNP) techniques can in addition enable quantifying parent-specific copy number (PSCN). The raw data from such experiments are two-dimensional, but are unphased. Consequently, inference based on them necessitates development of new analytic methods. ::: ::: Methods: We have adapted and enhanced the circular binary segmentation (CBS) algorithm for this purpose with focus on paired test and reference samples. The essence of paired parent-specific CBS (Paired PSCBS) is to utilize the original CBS algorithm to identify regions of equal total copy number and then to further segment these regions where there have been changes in PSCN. For the final set of regions, calls are made of equal parental copy number and loss of heterozygosity (LOH). PSCN estimates are computed both before and after calling. ::: ::: Results: The methodology is evaluated by simulation and on glioblastoma data. In the simulation, PSCBS compares favorably to established methods. On the glioblastoma data, PSCBS identifies interesting genomic regions, such as copy-neutral LOH. ::: ::: Availability: The Paired PSCBS method is implemented in an open-source R package named PSCBS, available on CRAN ( http://cran.r-project.org/). ::: ::: Contact: olshena@biostat.ucsf.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online. 
4. Abstract of Cited Paper (BIB004):  MOTIVATION ::: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology. ::: ::: ::: RESULTS ::: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using samples from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes. 
5. Abstract of Cited Paper (BIB005):  Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor-normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer. 
6. Abstract of Cited Paper (BIB006):  AbstractWe developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/. 
7. Abstract of Cited Paper (BIB007):  BackgroundUsing whole exome sequencing to predict aberrations in tumours is a cost effective alternative to whole genome sequencing, however is predominantly used for variant detection and infrequently utilised for detection of somatic copy number variation.ResultsWe propose a new method to infer copy number and genotypes using whole exome data from paired tumour/normal samples. Our algorithm uses two Hidden Markov Models to predict copy number and genotypes and computationally resolves polyploidy/aneuploidy, normal cell contamination and signal baseline shift. Our method makes explicit detection on chromosome arm level events, which are commonly found in tumour samples. The methods are combined into a package named ADTEx (Aberration Detection in Tumour Exome). We applied our algorithm to a cohort of 17 in-house generated and 18 TCGA paired ovarian cancer/normal exomes and evaluated the performance by comparing against the copy number variations and genotypes predicted using Affymetrix SNP 6.0 data of the same samples. Further, we carried out a comparison study to show that ADTEx outperformed its competitors in terms of precision and F-measure.ConclusionsOur proposed method, ADTEx, uses both depth of coverage ratios and B allele frequencies calculated from whole exome sequencing data, to predict copy number variations along with their genotypes. ADTEx is implemented as a user friendly software package using Python and R statistical language. Source code and sample data are freely available under GNU license (GPLv3) at http://adtex.sourceforge.net/. 
8. Abstract of Cited Paper (BIB008):  Background ::: Molecular mechanisms associated with frequent relapse of diffuse large B-cell lymphoma (DLBCL) are poorly defined. It is especially unclear how primary tumor clonal heterogeneity contributes to relapse. Here, we explore unique features of B-cell lymphomas - VDJ recombination and somatic hypermutation - to address this question. 
9. Abstract of Cited Paper (BIB009):  SUMMARY ::: Whole-exome sequencing (WES) has extensively been used in cancer genome studies; however, the use of WES data in the study of loss of heterozygosity or more generally allelic imbalance (AI) has so far been very limited, which highlights the need for user-friendly and flexible software that can handle low-quality datasets. We have developed a statistical approach, ExomeAI, for the detection of recurrent AI events using WES datasets, specifically where matched normal samples are not available. ::: ::: ::: AVAILABILITY ::: ExomeAI is a web-based application, publicly available at: http://genomequebec.mcgill.ca/exomeai. ::: ::: ::: CONTACT ::: JavadNadaf@gmail.com or somayyeh.fahiminiya@mcgill.ca ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online. 
10. Abstract of Cited Paper (BIB010):  Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.

Literature Review:
One active research area in WES data analysis is the development of computational methods for estimating copy number alterations (CNAs). Many tools have been developed for estimating CNAs from WES data based on paired normaltumor samples such as CNV-seq BIB001 , SegSeq BIB002 , ADTEx BIB008 , CONTRA BIB005 , EXCAVATOR BIB007 , ExomeCNV BIB003 , Control-FREEC (control-FREE Copy number caller)  , and CNVseeqer BIB009 . For example, VarScan2 BIB006 is a computational tool that can estimate somatic mutations and CNAs from paired normal-tumor samples. VarScan2 utilizes International Journal of Genomics a normal sample to find Somatic CNAs (SCNAs) by first comparing Q20 read depths between normal and tumor samples and normalizes them based on the amount of input data for each sample BIB006 . Copy number alteration is inferred from the log 2 of the ratio of tumor depth to normal depth for each region BIB006 . Lastly, the circular segmentation (CBS) algorithm BIB004 is utilized to merge adjacent segments to call a set of SCNAs. These SCNAs could be further classified as large-scale (>25% of chromosome arm) or focal (<25%) events in the WES data BIB011 . Recently, ExomeAI was developed to detect Allelic Imbalance (AI) from WES data BIB010 . Utilizing heterozygous sites, ExomeAI finds deviations from the expected 1 : 1 ratio between an A-and B-allele in multiple tumor samples without a normal comparison. Absolute deviation of B-allele frequency from .05 is calculated and similar to VarScan2; the CBS algorithm is applied to each chromosomal arm BIB004 . In order to reduce the number of false positives, a database was created with 500 (and counting) normal samples to filter out known AIs. This represents a novel tool to analyze WES for the detection of recurrent AI events without matched normal samples. A systematic evaluation of somatic copy number estimation tools for WES data has been recently published BIB011 . In this study, six computational tools for CNAs detection (ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV, and VarScan2) were evaluated using WES data from three TCGA datasets. Using a SNP array as the reference, this study found that these algorithms gave highly variable results. The authors found that ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity when compared to the reference set. The study showed that the current CNA detection tools for WES data still have limitations and called for more robust algorithms for this challenging task.