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:  WES Analysis Pipelines 
1. Abstract of Cited Paper (BIB001):  OBJECTIVE ::: Currently, there is a disconnect between finding a patient's relevant molecular profile and predicting actionable therapeutics. Here we develop and implement the Integrating Molecular Profiles with Actionable Therapeutics (IMPACT) analysis pipeline, linking variants detected from whole-exome sequencing (WES) to actionable therapeutics. ::: ::: ::: METHODS AND MATERIALS ::: The IMPACT pipeline contains 4 analytical modules: detecting somatic variants, calling copy number alterations, predicting drugs against deleterious variants, and analyzing tumor heterogeneity. We tested the IMPACT pipeline on whole-exome sequencing data in The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples with known EGFR mutations. We also used IMPACT to analyze melanoma patient tumor samples before treatment, after BRAF-inhibitor treatment, and after BRAF- and MEK-inhibitor treatment. ::: ::: ::: RESULTS ::: IMPACT Food and Drug Administration (FDA) correctly identified known EGFR mutations in the TCGA lung adenocarcinoma samples. IMPACT linked these EGFR mutations to the appropriate FDA-approved EGFR inhibitors. For the melanoma patient samples, we identified NRAS p.Q61K as an acquired resistance mutation to BRAF-inhibitor treatment. We also identified CDKN2A deletion as a novel acquired resistance mutation to BRAFi/MEKi inhibition. The IMPACT analysis pipeline predicts these somatic variants to actionable therapeutics. We observed the clonal dynamic in the tumor samples after various treatments. We showed that IMPACT not only helped in successful prioritization of clinically relevant variants but also linked these variations to possible targeted therapies. ::: ::: ::: CONCLUSION ::: IMPACT provides a new bioinformatics strategy to delineate candidate somatic variants and actionable therapies. This approach can be applied to other patient tumor samples to discover effective drug targets for personalized medicine.IMPACT is publicly available at http://tanlab.ucdenver.edu/IMPACT. 
2. Abstract of Cited Paper (BIB002):  Next-generation sequencing technologies are fueling a wave of new diagnostic tests. Progress on a key set of nine research challenge areas will help generate the knowledge required to advance effectively these diagnostics to the clinic.

Literature Review:
WES data analysis pipelines integrate computational tools and methods described in the previous sections in a single analysis workflow. Here, we review three recent sequencing pipelines SeqMule BIB001 , Fastq2vcf  , and IMPACT BIB002 that assimilate some of the tools described in previous sections. SeqMule stands out in part due to the use of five alignment tools (BWA, Bowtie 1 and 2, SOAP2, and SNAP) and five different variant calling algorithms (GATK, SAMtools, VarScan2, FreeBayes, and SOAPsnp) BIB001 . SeqMule contains at least one feature that performs Pre-VCF analyses to generate a filtered VCF file. SeqMule also generates an accompanying HTML-based report and images to show an overview of every step in the pipeline. Fastq2vcf also performs the Pre-VCF analyses using BWA as an alignment tool and variant calling by GATK, UnifiedGenotyper, HaplotypeCaller, SAMtools, and SNVer resulting in a filtered VCF after implementation of ANNOVAR and VEP  . Fastq2vcf can be used in a single or parallel computing environment on variety of sequencing data. Both SeqMule and Fastq2vcf pipelines focus on taking raw sequencing data and converting it into a filtered VCF file. IMPACT (Integrating Molecular Profiles with ACtionable Therapeutics) WES data analysis pipeline was developed to take this analysis a step further by linking a filtered VCF to actionable therapeutics BIB002 . The IMPACT pipeline contains four analytical modules: detecting somatic variants; calling copy number alterations; predicting drugs against the deleterious variants; and tumor heterogeneity analysis. IMPACT has been applied to longitudinal samples obtained from a melanoma patient and identified novel acquired resistance mutations to treatment. IMPACT analysis revealed loss of CDKN2A as a novel resistance mechanism to the combination of dabrafenib and trametinib treatment and predicted potential drugs for further pharmacological and biological studies BIB002 . To compare the strengths and weaknesses between these three WES pipelines, SeqMule allows the use of different alignment algorithms in its pipeline whereas IMPACT and Fastq2vcf only utilize BWA as the sequencing alignment algorithm. SAMtools is the common tool used by IMPACT, Fastq2vcf, and SeqMule to call variants. In addition, Fastq2vcf and SeqMule employ GATK and other variant calling algorithms for variants detection. Fastq2vcf and IMPACT both annotate the variants with ANNOVAR. Fastq2vcf also utilizes VEP and IMPACT utilizes SIFT and PolyPhen-2 as the primary variants prediction methods. For Post-VCF analysis, IMPACT pipeline has more options as compared to SeqMule and Fastq2vcf. In particular, IMPACT performs copy number analysis, tumor heterogeneity, and linking of actionable therapeutics to the molecular profiles. However, IMPACT is only designed to be performed on tumor samples while SeqMule and Fastq2vcf are designed for any WES dataset. Therefore, it is advisable for the users to consider the analytic needs to select the appropriate WES data analysis pipeline for their research. As recently discussed by Altman et al., part of the U.S. Precision Medicine Initiative (PMI) includes being able to define a gold standard of pipelines and tools for specific sequencing studies to enable a new era of medicine BIB003 . Automated pipelines such as these will accelerate the analysis and interpretation of WES data. Future development of data analysis pipeline will be needed to incorporate newer and wider tools tailored for specific research questions.