Chapters authored
Impact of Gene Annotation on RNA-seq Data Analysis
RNA-seq has become increasingly popular in transcriptome profiling. One of the major challenges in RNA-seq data analysis is the accurate mapping of junction reads to their genomic origins. To detect splicing sites in short reads, many RNA-seq aligners use reference transcriptome to inform placement of junction reads. However, no systematic evaluation has been performed to assess or quantify the benefits of incorporating reference transcriptome in mapping RNA-seq reads. Meanwhile, there exist multiple human genome annotation databases, including RefGene (RefSeq Gene), Ensembl, and the UCSC annotation database. The impact of the choice of an annotation on estimating gene expression remains insufficiently investigated.
Part of the book: Next Generation Sequencing
Bioinformatics for RNA‐Seq Data Analysis
While RNA sequencing (RNA‐seq) has become increasingly popular for transcriptome profiling, the analysis of the massive amount of data generated by large‐scale RNA‐seq still remains a challenge. RNA‐seq data analyses typically consist of (1) accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events; (2) quantifying expression levels of genes, transcripts, and exons; (3) differential analysis of gene expression among different biological conditions; and (4) biological interpretation of differentially expressed genes. Despite the fact that multiple algorithms pertinent to basic analyses have been developed, there are still a variety of unresolved questions. In this chapter, we review the main tools and algorithms currently available for RNA‐seq data analyses, and our goal is to help RNA‐seq data analysts to make an informed choice of tools in practical RNA‐seq data analysis. In the meantime, RNA‐seq is evolving rapidly, and newer sequencing technologies are briefly introduced, including stranded RNA‐seq, targeted RNA‐seq, and single‐cell RNA‐seq.
Part of the book: Bioinformatics
Cloud Computing for Next-Generation Sequencing Data Analysis
High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.
Part of the book: Cloud Computing