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:  conclusion 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Background ::: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation. 
3. Abstract of Cited Paper (BIB003):  Motivation: A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean–variance relationships provides a flexible testing regimen that ‘borrows’ information across genes, while easily incorporating design effects and additional covariates. ::: ::: Results: We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean–variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. ::: ::: Availability: An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq ::: ::: Contact:yzhou@bios.unc.edu; fwright@bios.unc.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online. 
4. Abstract of Cited Paper (BIB004):  We discuss the identification of features that are associated with an outcome in RNA-Sequencing (RNA-Seq) and other sequencing-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or ‘sequencing depths’. Existing methods for this problem are based on Poisson or negative binomial models: they are useful but can be heavily influenced by ‘outliers’ in the data. We introduce a simple, nonparametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class or multiple-class outcomes. We compare our proposed method to Poisson and negative binomial-based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods. 
5. Abstract of Cited Paper (BIB005):  Next generation sequencing is quickly replacing microarrays as a technique to probe different molecular levels of the cell, such as DNA or RNA. The technology provides higher resolution, while reducing bias. RNA sequencing results in counts of RNA strands. This type of data imposes new statistical challenges. We present a novel, generic approach to model and analyze such data. Our approach aims at large flexibility of the likelihood (count) model and the regression model alike. Hence, a variety of count models is supported, such as the popular NB model, which accounts for overdispersion. In addition, complex, non-balanced designs and random effects are accommodated. Like some other methods, our method provides shrinkage of dispersion-related parameters. However, we extend it by enabling joint shrinkage of parameters, including those for which inference is desired. We argue that this is essential for Bayesian multiplicity correction. Shrinkage is effectuated by empirically estimating priors. We discuss several parametric (mixture) and non-parametric priors and develop procedures to estimate (parameters of) those. Inference is provided by means of local and Bayesian false discovery rates. We illustrate our method on several simulations and two data sets, also to compare it with other methods. Model- and data-based simulations show substantial improvements in the sensitivity at the given specificity. The data motivate the use of the ZI-NB as a powerful alternative to the NB, which results in higher detection rates for low-count data. Finally, compared with other methods, the results on small sample subsets are more reproducible when validated on their large sample complements, illustrating the importance of the type of shrinkage. 
6. Abstract of Cited Paper (BIB006):  RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice. 
7. Abstract of Cited Paper (BIB007):  A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Literature Review:
RNAseq data analysis is a relatively new and rapidly growing research area. The statistical model used for sequencing data has been evolving. The first proposed Poisson distribu tion has become obsolete because it fails to accommodate commonlyobserved overdispersion in RNAseq data. In a parametric framework, the negative binomial distribution is the most common assumption for modeling the marginal distribu tion due to the technical and biological variations. BIB002  BIB001  Other available methods that account for overdispersions include the generalized Poisson distribution,  negative binomial power distribution,  and betabinomial distribution,  BIB004 as well as nonparametric models BIB005  and Bayesian methods. BIB006 BIB003 Table 2 summarizes all the reviewed methods in this paper. For readers who are interested in the performance evalu ation and method comparison of the available methods, they can refer to the original paper as well as the body of litera ture on this issue. For instance, in the study by Seyednasrollah et al. BIB007 , DESeq has been recommended as one of the most robust methods and caution is advised when dealing with a small number of replicates regardless of which method is being used. Similarly, Soneson and Delorenzi 21 also advise caution when interpreting results drawn from a small number of rep licates and show that SAMseq surpasses many other reviewed methods. In the study by Rapaport et al. BIB008 , DESeq, edgeR, and baySeq, which all assume a negative binomial model, have bet ter specificity, sensitivity, and control of false positive errors than other nonnegative binomial models. As the technol ogy continues to improve and the empirical data accumulate, more compelling statistical modeling for RNAseq data can be expected.