9/03/2014
Methods to study splicing from RNAseq
The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. The authors provide an overview of the methods available to study splicing from short RNA-Seq data, which could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. They also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.
Biases in RNA deep sequencing data
Biases in small RNA deep sequencing data
+ Author Affiliations
- ↵*To whom correspondence should be addressed. Tel: +49 251 8358607; Fax: +49 251 8352134; Email: rozhdest@uni-muenster.de
- ↵Correspondence may also be addressed to Carsten A. Raabe. Tel: +49 251 8358615; Fax: +49 251 8352134; Email: raabec@uni-muenstser.de
High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, the authors review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.
Methods to get 5' UTR regions (5′-Anchored Reads)
Transcription Start Site Evolution in Drosophila
Bradley J. Main*,1, Andrew D. Smith1, Hyosik Jang1 and Sergey V. Nuzhdin1+ Author Affiliations
1Section of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California
↵*Corresponding author: E-mail: bmain@usc.edu.
New Approaches
Several molecular techniques can be used to locate TSS including cap analysis for gene expression (CAGE) (Shiraki et al. 2003) and several updated versions (Ni et al. 2010; Plessy et al. 2010; Kanamori-Katayama et al. 2011), 5′-rapid amplification of cDNA ends (RACE) (Harvey and Darlison 1991), robust analysis of 5′-transcript ends (Gowda et al. 2007), and FLcDNA assays (Suzuki et al. 1997). The original CAGE protocol involves the concatenation of short 5′-sequence tags (14–20 bp), followed by traditional Sanger sequencing (Shiraki et al. 2003). More recently, CAGE and similar 5′-targeting methods have been adapted to high-throughput sequencing (Sandelin et al. 2007; Ni et al. 2010; Plessy et al. 2010; Kanamori-Katayama et al. 2011). One major difference between the available methods is the approach used to target 5′-ends of full length transcripts. For example, some methods rely on the removal of the 5′-cap structure with tobacco acid pyrophosphatase (TAP), others use the 5′-cap structure to perform template switching, and 5′-caps can also be biotinyled and isolated with streptavidin beads. We wanted a simple and straightforward approach without specific limitations, such as short reads (tags) (Harbers and Carninci 2005; Kodzius et al. 2006; Ni et al. 2010) and single-end reads (Kodzius et al. 2006), which hinder mapping, and added sampling bias from a required semisuppressive polymerase chain reaction (PCR) step (Plessy et al. 2010; Salimullah et al. 2011). Thus, we generated 5′-anchored reads using a concise TAP-based protocol that employs standard Illumina adapters and barcode indexes and is free of the aforementioned drawbacks. We extracted total RNA from whole body, adult female flies from each Drosophila species. We purified mRNA using oligo-dT Dynabeads (Invitrogen) and ligated an RNA adapter oligo to the 5′-end of each mRNA molecule. We chemically fragmented the ligated mRNA using RNA fragmentation reagent (Ambion) and generated single-stranded cDNA with reverse transcriptase and random hexamers, followed by RNAse H treatment. We added a primer complementary to the 5′-ligated adapter sequence and performed one primer extension step at 72 °C with Taq polymerase to yield double-stranded fragments of all 5′-ends (fig. 2). This primer has a 5′-amine group to prevent concatenation and subsequent ligation. Taq adds an A-overhang in a template-independent fashion (Clark 1988), thus we can bypass the typical blunt-end repair and cleanup step and immediately ligate standard Illumina adapters in a strand-specific orientation. Standard Illumina indexing barcodes were then added during PCR enrichment of each sample. We sequenced the 5′-enriched fragments on an Illumina Genome Analyzer II (see supplementary file, Supplementary Material online, for a detailed protocol).
Pulling teeth from history
… teeth have proven to be an excellent source of high‐quality ancient DNA, yielding insights into the evolution of the human diet, disease and immunity since the onset of agriculture…
9/02/2014
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