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: Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software 
Chapter Title:  35 
1. Abstract of Cited Paper (BIB001):  For any generalized linear model, the Pearson goodness of fit statistic is the score test statistic for testing the current model against the saturated model. The relationship between the Pearson statistic and the residual deviance is therefore the relationship between the score test and the likelihood ratio test statistics, and this clarifies the role of the Pearson statistic in generalized linear models. The result is extended to cases in which there are multiple reponse observations for the same combination of explanatory variables. 
2. Abstract of Cited Paper (BIB002):  MOTIVATION ::: Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. ::: ::: ::: RESULTS ::: We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. ::: ::: ::: AVAILABILITY ::: An R package can be accessed from http://bioinf.wehi.edu.au/resources/ 
3. Abstract of Cited Paper (BIB003):  High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package. 
4. Abstract of Cited Paper (BIB004):  Summary: Next-generation sequencing platforms for measuring digital expression such as RNASeq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far, few exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as two replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes. Availability and implementation: The package is freely available under the MIT license from CRAN

Literature Review:
Negative binomial. Overview. A class of models based on the negative binomial distribution assumption has been developed in order to accommodate the overdispersion among biological replicate data. BIB004  BIB003  Robinson and Smyth BIB003 used the conditional maximum likelihood (CML) to estimate the dispersion param eter-a measure of the excessive variance that a Poisson model does not incorporate-when assuming a common dispersion parameter across genes. They compared the CML method with alternative estimation methods based on pseudolikelihood, quasi likelihood, and conditional inference. BIB002  BIB001 In a followup paper,  they also extended the model to allow for genespecific dispersion parameters and proposed to estimate the dispersion parameters by maximizing a weighted conditional likelihood with empiri cal Bayesian approximation. Details of their method, edgeR, can be found in Robinson and Smyth. 33,  edgeRun is based on the same model as edgeR but it uses an unconditional exact test to achieve more power while paying the price of computational time. BIB005 Anders and Huber 8 proposed a method called DESeq also under the negative binomial assumption. They advocated the use of a robust estimate of normalization factors for the esti mation of dispersion parameter and a local regression to obtain smooth function for each group on the graphs of expected pro portions vs sample variances. DESeq2 was developed in the study by Love et al.  as a successor of DESeq. It employs a number of new modeling features, such as the use of a shrunken fold change and a shrunken dispersion estimation method, to further improve the model performance. Di and others  proposed a method, NBPSeq, using a negative binomial power distribu tion instead of a regular negative binomial distribution. They and φ is common across genes while α helps to accommodate the overdispersion. φ and α are estimated by maximizing condi tional loglikelihood,  conditional on the total gene counts for each gene g. An exact test modified for negative binomial power distribution is used for hypothesis testing. More details can be found in the study by Di et al.  Modeling. The model setup for negative binomial is to assume y gi ∼ negative binomial (µ gi , φ g ). The dispersion para meter, φ g , accounts for the sampletosample variability, which is usually assumed to be common across samples. There are various estima tion methods for this model assumption. More specifically, the negative binomial probability mass function is written as where Hypothesis test ing is set up as H 0 : no difference either between the expected normalized expression of gene g in groups or between the pro portion of reads that are gene g in groups.