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:  VCF Annotation Methods. 
1. Abstract of Cited Paper (BIB001):  We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in... 
2. Abstract of Cited Paper (BIB002):  This paper describes a new program SnpSift for filtering differential DNA sequence variants between two or more experimental genomes after genotoxic chemical exposure. Here, we illustrate how SnpSift can be used to identify candidate phenotype-relevant variants including single nucleotide polymorphisms (SNPs), multiple nucleotide polymorphisms (MNPs), insertions and deletions (InDels) in mutant strains isolated from genome-wide chemical mutagenesis of Drosophila melanogaster. First, the genomes of two independently-isolated mutant fly strains that are allelic for a novel recessive male-sterile locus generated by genotoxic chemical exposure were sequenced using the Illumina next-generation DNA sequencer to obtain 20- to 29-fold coverage of the euchromatic sequences. The sequencing reads were processed and variants were called using standard bioinformatic tools. Next, SnpEff was used to annotate all sequence variants and their potential mutational effects on associated genes. Then, SnpSift was used to filter and select differential variants that potentially disrupt a common gene in the two allelic mutant strains. The potential causative DNA lesions were partially validated by capillary sequencing of PCR-amplified DNA in the genetic interval as defined by meiotic mapping and deletions that remove defined regions of the chromosome. Of the five candidate genes located in the genetic interval, the Pka-like gene CG12069 was found to carry a separate premature stop codon mutation in each of the two allelic mutants whereas the other 4 candidate genes within the interval have wild-type sequences. The Pka-like gene is therefore a strong candidate gene for the male-sterile locus. These results demonstrate that combining SnpEff and SnpSift can expedite the identification of candidate phenotype-causative mutations in chemically-mutagenized Drosophila strains. This technique can also be used to characterize the variety of mutations generated by genotoxic chemicals. 
3. Abstract of Cited Paper (BIB003):  Summary: The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment. ::: ::: Availability and Implementation: VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org. ::: ::: Contact: lukas.habegger@yale.edu or mark.gerstein@yale.edu ::: ::: Supplementary Information: Supplementary data are available at Bioinformatics online. 
4. Abstract of Cited Paper (BIB004):  Detection of somatic point substitutions is a key step in characterizing the cancer genome. However, existing methods typically miss low-allelic-fraction mutations that occur in only a subset of the sequenced cells owing to either tumor heterogeneity or contamination by normal cells. Here we present MuTect, a method that applies a Bayesian classifier to detect somatic mutations with very low allele fractions, requiring only a few supporting reads, followed by carefully tuned filters that ensure high specificity. We also describe benchmarking approaches that use real, rather than simulated, sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.

Literature Review:
Once the variants are detected and called, the next step is to annotate these variants. The two most popular VCF annotation tools are ANNOVAR BIB001 and MuTect BIB005 which is part of the GATK pipeline. ANNOVAR was developed in 2010 with the aim to rapidly annotate millions of variants with ease and remains one of the popular variant annotation methods to date BIB001 . ANNOVAR can use gene, region, or filter-based annotation to access over 20 public databases for variants annotation. MuTect is another method that uses Bayesian classifiers for detecting and annotating variants BIB001 BIB005 . MuTect has been widely used in cancer genomics research, especially in The Cancer Genome Atlas projects. Other VCF annotation tools are SnpEff BIB002 and SnpSift BIB003 . SnpEff can perform annotation for multiple variants and SnpSift allows rapid detection of significant variants from the VCF files BIB003 . The Variant Annotation Tool (VAT) distinguishes itself from other annotation tools in one aspect by adding cloud computing capabilities BIB004 . VAT annotation occurs at the transcript level to determine whether all or only a subset of the transcript isoforms of a gene is affected. VAT is dynamic in that it also annotates Multiple Nucleotide Polymorphisms (MNPs) and can be used on more than just the human species.