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 Predicting Drivers in Cancer 
1. Abstract of Cited Paper (BIB001):  Next-generation DNA sequencing technologies are enabling genome-wide measurements of somatic mutations in large numbers of cancer patients. A major challenge in interpretation of this data is to distinguish functional driver mutations that are important for cancer development from random, passenger mutations. A common approach to identify driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb the necessary pathways. However, the current understanding of the somatic mutational process of cancer [3,5,6] places two additional constraints on the expected patterns of somatic mutations in a cancer pathway. First, an important cancer pathway should be perturbed in a large number of patients. Thus we expect that with genome-wide measurements of somatic mutations a driver pathway will exhibit high coverage, where most patients will have a mutation in some gene in the pathway. Second, since driver mutations are relatively rare and typically a single driver mutation is sufficient to perturb a pathway, a reasonable assumption is that most patients have a single driver mutation in a pathway. Thus, the genes in a driver pathway exhibit a pattern of mutually exclusive driver mutations, where driver mutations are observed in exactly one gene in the pathway in each patient. There are numerous examples of sets of mutually exclusive mutations [5,6].

Literature Review:
Exomes. Cancer is a disease driven by genetic variations and copy number alterations. These genetic events can be classified into two classes, "driver" and "passenger" mutations. Driver mutations are the key mutation that drive the development of cancer and provide a survival advantage, whereas passenger mutations are "by-stander" alterations that happen to be altered in the primary cells but do not provide a survival advantage. As the cancer exomes tend to have high mutational burdens, identifying the "driver" mutations from the "passenger" mutations is one of the key analyses in cancer research. Several tools have been developed to find driver mutations including but not limited to CHASM BIB001 , Dendrix BIB002 , and MutSigCV  . CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) uses random forest as the machine learning approach to distinguish the difference between driver and passenger mutations in cancer BIB001 . CHASM was trained on the curated driver mutations obtained from the COSMIC database ("positive examples") and synthetic passenger mutations generated according to the background of base substitution frequencies observed in specific tumor types ("negative examples"). CHASM can achieve high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations when tested in real tumor samples. This method has been one of the popular driver detection prediction tools for cancer researchers and has been applied in various cancer genomic studies. Another common driver mutation tool is MutSigCV developed to resolve the problem of extensive false-positive findings that overshadow true driver mutations  . As the size of cancer genomes sequenced has increased implausible genes (such as TTN) have been falsely reported as being related to cancer when in fact their large size just makes the probability they would be mutated by chance increase  . MutSigCV takes into account patient-specific mutation frequency and spectrum as well as gene-specific background mutation rates, expression level, and replication time. By pooling all of this available data into one tool, MutSigCV has become a standard tool used for driver mutation identification in cancer studies. De novo Driver Exclusivity (Dendrix) is a novel computational tool to determine de novo driver pathways (gene sets) from somatic mutations in patient data BIB002 . The main goal of the Dendrix algorithm is to find gene sets with high coverage and high exclusivity properties from the somatic data. The high coverage property assumes most patients have at least one driver mutation in the gene set, whereas the high exclusivity property assumes that these driver mutations are rarely mutated together in the same patient. Two algorithms were developed in Dendrix, one based on a greedy algorithm and one based on the Markov Chain Monte Carlo (MCMC) algorithm, to measure sets of genes that exhibit both criteria. When Dendrix was applied to the TCGA data, the algorithms identified groups of genes that were mutated in large subsets of patients and these mutations were mutually exclusive. This tool provides an opportunity to analyze WES data to identify driver pathways in cancer genomic studies.