Evaluate RNAseq DEG software

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Genome Biology 2013, 14:R95  doi:10.1186/gb-2013-14-9-r95

 

Differential expression analysis using qRT-PCR validated gene set. (a) ROC analysis was performed using a qRT-PCR log₂ expression change threshold of 0.5. The results show a slight advantage for DESeq and edgeR in detection accuracy. (b) At increasing log₂ expression ratios (incremented by 0.1), representing a more stringent cutoff for differential expression, the performances of the Cuffdiff and limma methods gradually reduce whereas PoissonSeq performance increases. AUC, area under the curve.

Rapaport et al. Genome Biology 2013 14:R95   doi:10.1186/gb-2013-14-9-r95

DB for RNA-seq datasets

Question: What Databases Are Available For Rna-Seq Datasets?
https://www.biostars.org/p/46059/

 

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

1. Carsten A. Raabe¹,*,
2. Thean-Hock Tang²,
3. Juergen Brosius¹ and
4. Timofey S. Rozhdestvensky¹,*

+ Author Affiliations

1. ¹Institute of Experimental Pathology (ZMBE), University of Muenster, Von-Esmarch-Strasse 56, 48149 Muenster, Germany and ²Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, 13200 Penang, Malaysia

1. ↵*To whom correspondence should be addressed. Tel: +49 251 8358607; Fax: +49 251 8352134; Email: rozhdest@uni-muenster.de
2. ↵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.