Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

Recent advances in RNA sequence analysis

The latest high-throughput DNA sequencing technology can now be applied on a large scale to capture the complete set of mRNA transcripts in a cell, using a technique called RNA-seq. Although RNA-seq is only 2 years old, it has rapidly swept through the field of genomics, and it is now being used to analyze the transcriptomes of organisms ranging from bacteria to primates. The depth of sequencing allows researchers to quantify the level of expression of genes, to discover alternative isoforms in eukaryotic species, and even to characterize the operon structure of bacterial genomes.

Comprehensive comparative analysis of strand-specific RNA sequencing methods

Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but no consensus exists as to how to choose between them. Here we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library-construction protocols, including both published and our own methods. We found marked differences in strand specificity, library complexity, evenness and continuity of coverage, agreement with known annotations and accuracy for expression profiling. Weighing each method's performance and ease, we identified the dUTP second-strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.

Cloud-scale RNA-sequencing differential expression analysis with Myrna

As sequencing throughput approaches dozens of gigabases per day, there is a growing need for efficient software for analysis of transcriptome sequencing (RNA-Seq) data. Myrna is a cloud-computing pipeline for calculating differential gene expression in large RNA-Seq datasets. We apply Myrna to the analysis of publicly available data sets and assess the goodness of fit of standard statistical models. Myrna is available from http://bowtie-bio.sf.net/myrna.

Annotating conserved and novel features of primate transcriptomes using sequencing

Recent high-throughput sequencing of chimpanzee brain and liver transcriptomes published in Genome Biology reveals multiple transcripts lost in the human genome and highlights the incompleteness of primate genome annotations.

See research article: http://genomebiology.com/2010/11/7/R78

Global regulation of alternative splicing during myogenic differentiation

Recent genome-wide analyses have elucidated the extent of alternative splicing (AS) in mammals, often focusing on comparisons of splice isoforms between differentiated tissues. However, regulated splicing changes are likely to be important in biological transitions such as cellular differentiation, or response to environmental stimuli. To assess the extent and significance of AS in myogenesis, we used splicing-sensitive microarray analysis of differentiating C2C12 myoblasts. We identified 95 AS events that undergo robust splicing transitions during C2C12 differentiation. More than half of the splicing transitions are conserved during differentiation of avian myoblasts, suggesting the products and timing of transitions are functionally significant. The majority of splicing transitions during C2C12 differentiation fall into four temporal patterns and were dependent on the myogenic program, suggesting that they are integral components of myogenic differentiation. Computational analyses revealed enrichment of many sequence motifs within the upstream and downstream intronic regions near the alternatively spliced regions corresponding to binding sites of splicing regulators. Western analyses demonstrated that several splicing regulators undergo dynamic changes in nuclear abundance during differentiation. These findings show that within a developmental context, AS is a highly regulated and conserved process, suggesting a major role for AS regulation in myogenic differentiation.

Tissue-specific transcript annotation and expression profiling with complementary next-generation sequencing technologies

Next-generation sequencing is excellently suited to evaluate the abundance of mRNAs to study gene expression. Here we compare two alternative technologies, cap analysis of gene expression (CAGE) and serial analysis of gene expression (SAGE), for the same RNA samples. Along with quantifying gene expression levels, CAGE can be used to identify tissue-specific transcription start sites, while SAGE monitors 3′-end usage. We used both methods to get more insight into the transcriptional control of myogenesis, studying differential gene expression in differentiated and proliferating C2C12 myoblast cells with statistical evaluation of reproducibility and differential gene expression. Both CAGE and SAGE provided highly reproducible data (Pearson's correlations >0.92 among biological triplicates). With both methods we found around 10 000 genes expressed at levels 2 transcripts per million (0.3 copies per cell), with an overlap of 86%. We identified 4304 and 3846 genes differentially expressed between proliferating and differentiated C2C12 cells by CAGE and SAGE, respectively, with an overlap of 2144. We identified 196 novel regulatory regions with preferential use in proliferating or differentiated cells. Next-generation sequencing of CAGE and SAGE libraries provides consistent expression levels and can enrich current genome annotations with tissue-specific promoters and alternative 3′-UTR usage.

Next-generation sequencing techniques for eukaryotic microorganisms: sequencing-based solutions to biological problems

Over the past 5 years, large-scale sequencing has been revolutionized by the development of several so-called next-generation sequencing (NGS) technologies. These have drastically increased the number of bases obtained per sequencing run while at the same time decreasing the costs per base. Compared to Sanger sequencing, NGS technologies yield shorter read lengths; however, despite this drawback, they have greatly facilitated genome sequencing, first for prokaryotic genomes and within the last year also for eukaryotic ones. This advance was possible due to a concomitant development of software that allows the de novo assembly of draft genomes from large numbers of short reads. In addition, NGS can be used for metagenomics studies as well as for the detection of sequence variations within individual genomes, e.g., single-nucleotide polymorphisms (SNPs), insertions/deletions (indels), or structural variants. Furthermore, NGS technologies have quickly been adopted for other high-throughput studies that were previously performed mostly by hybridization-based methods like microarrays. This includes the use of NGS for transcriptomics (RNA-seq) or the genome-wide analysis of DNA/protein interactions (ChIP-seq). This review provides an overview of NGS technologies that are currently available and the bioinformatics analyses that are necessary to obtain information from the flood of sequencing data as well as applications of NGS to address biological questions in eukaryotic microorganisms.

Design and evaluation of genome-wide libraries for RNA interference screens

RNA interference (RNAi) screens have enabled the systematic analysis of many biological processes in cultured cells and whole organisms. The success of such screens and the interpretation of the data depend on the stringent design of RNAi libraries. We describe and validate NEXT-RNAi, a software for the automated design and evaluation of RNAi sequences on a genome-wide scale. NEXT-RNAi is implemented as open-source software and is accessible at http://www.nextrnai.org/.