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

Dynamic Changes in the MicroRNA Expression Profile Reveal Multiple Regulatory Mechanisms in the Spinal Nerve Ligation Model of Neuropathic Pain

Neuropathic pain resulting from nerve lesions or dysfunction represents one of the most challenging neurological diseases to treat. A better understanding of the molecular mechanisms responsible for causing these maladaptive responses can help develop novel therapeutic strategies and biomarkers for neuropathic pain. We performed a miRNA expression profiling study of dorsal root ganglion (DRG) tissue from rats four weeks post spinal nerve ligation (SNL), a model of neuropathic pain. TaqMan low density arrays identified 63 miRNAs whose level of expression was significantly altered following SNL surgery. Of these, 59 were downregulated and the ipsilateral L4 DRG, not the injured L5 DRG, showed the most significant downregulation suggesting that miRNA changes in the uninjured afferents may underlie the development and maintenance of neuropathic pain. TargetScan was used to predict mRNA targets for these miRNAs and it was found that the transcripts with multiple predicted target sites belong to neurologically important pathways. By employing different bioinformatic approaches we identified neurite remodeling as a significantly regulated biological pathway, and some of these predictions were confirmed by siRNA knockdown for genes that regulate neurite growth in differentiated Neuro2A cells. In vitro validation for predicted target sites in the 3′-UTR of voltage-gated sodium channel Scn11a, alpha 2/delta1 subunit of voltage-dependent Ca-channel, and purinergic receptor P2rx ligand-gated ion channel 4 using luciferase reporter assays showed that identified miRNAs modulated gene expression significantly. Our results suggest the potential for miRNAs to play a direct role in neuropathic pain

Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion

Abstract To allow efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNA) are generally removed from total RNA either by positive polyA+ selection or by rRNA depletion (negative selection) before sequencing. Comparisons between the two methods have been carried out by various groups, but the assessments have relied largely on non-clinical samples. In this study, we evaluated these two RNA sequencing approaches using human blood and colon tissue samples. Our analyses showed that rRNA depletion captured more unique transcriptome features, whereas polyA+ selection outperformed rRNA depletion with higher exonic coverage and better accuracy of gene quantification. For blood- and colon-derived RNAs, we found that 220% and 50% more reads, respectively, would have to be sequenced to achieve the same level of exonic coverage in the rRNA depletion method compared with the polyA+ selection method. Therefore, in most cases we strongly recommend polyA+ selection over rRNA depletion for gene quantification in clinical RNA sequencing. Our evaluation revealed that a small number of lncRNAs and small RNAs made up a large fraction of the reads in the rRNA depletion RNA sequencing data. Thus, we recommend that these RNAs are specifically depleted to improve the sequencing depth of the remaining RNAs

Npas4, a novel helix-loop-helix PAS domain protein, is regulated in response to cerebral ischemia

Basic helix-loop-helix PAS domain proteins form a growing family of transcription factors. These proteins are involved in the process of adaptation to cellular stresses and environmental factors such as a change in oxygen concentration. We describe the identification and characterization of a recently cloned PAS domain protein termed Npas4 in ischemic rat brain. Using gene expression profiling following middle cerebral artery occlusion, we showed that the Npas4 mRNA is differentially expressed in ischemic tissue. The full-length gene was cloned from rat brain and its spatial and temporal expression characterized with in situ hybridization and Northern blotting. The Npas4 mRNA is specifically expressed in the brain and is highly up-regulated in ischemic tissues following both focal and global cerebral ischemic insults. Immunohistochemistry revealed a strong expression in the limbic system and thalamus, as well as in layers 3 and 5 in the cortex of the unchallenged brain. When overexpressed in HEK 293 cells, Npas4 appears as a protein of similar to 100 kDa. In brain samples, however, in addition to the 100 kDa band a specific 200 kDa immunoreactive band was also detected. Ischemic challenge lead to a decrease in the 200 kDa form and a simultaneous increase in the 100 kDa immunoreactivity. This could indicate a novel regulatory mechanism for activation and/or deactivation of this protein in response to ischemic brain injury

Additional file 1:Table S1. of Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap

Reports the related metrics for all eight RNA-seq samples, including library sizes, the mapping summaries, and the counting summaries. Tables S2. and S3. Tabulate the overlapping summaries of Gencode V19 annotation database at both the gene and the nucleotide base levels, respectively. Figures S1. and S2. Show all-against-all scatter plots of gene expression profile among RNA-seq samples sequenced by stranded and non-stranded protocols, respectively. Figure S3. Explains why the expression level for GAPDH (a well-known housekeeping gene) is underestimated in non-stranded RNA-seq. Script 1. Contains the R script to estimate the gene overlap in Gencode Release 19. (PDF 429 kb