BACKGROUND The starting material for RNA sequencing (RNA-seq) studies is usually total RNA or polyA+ RNA. Both forms of RNA represent heterogeneous pools of RNA molecules at different levels of maturation and processing. Such heterogeneity, in addition to the biases associated with polyA+ purification steps, may influence the analysis, sensitivity and the interpretation of RNA-seq data. We hypo...

BACKGROUND The past decade has seen an abundance of transcriptional profiling studies of preclinical models of persistent pain, predominantly employing microarray technology. In this study we directly compare exon microarrays to RNA-seq and investigate the ability of both platforms to detect differentially expressed genes following nerve injury using the L5 spinal nerve transection model of neu...

Macrophages undergo programmatic changes with age, leading to altered cytokine polarization and immune dysfunction, shifting these critical cells from protective sentinels disease promoters. The molecular mechanisms underlying macrophage inflammaging are poorly understood. Using an unbiased RNA sequencing (RNA-seq) approach, we identified Mir146b as a microRNA whose expression progressively uni...

RNA-Seq has become a widely used method to study transcriptomes, and it is now possible to perform RNA-Seq on almost any sample. Nevertheless, samples obtained from small cell populations are particularly challenging, as biases associated with low amounts of input RNA can have strong and detrimental effects on downstream analyses. Here we compare different methods to normalize RNA-Seq data obta...

The next generation sequencing technology (RNA-seq) provides absolute quantification of gene expression using counts of read. Transcriptome studies are switching to rely on RNA-seq rather than microarrays since RNA-seq has higher sensitivity and dynamic range, with lower technical variation and thus higher precision than microarrays. Limited work has been done on expression analysis of longitud...

The significance of single-cell transcription resides not only in the cumulative expression strength of the cell population but also in its heterogeneity. We propose a new model that improves the detection of changes in the transcriptional heterogeneity pattern of RNA-Seq data using two heterogeneity parameters: ‘burst proportion’ and ‘burst magnitude’, whose changes are validated using RNA-FIS...

Global transcriptomic analysis via RNA-seq is often hampered by the high abundance of ribosomal (r)RNA in bacterial cells. To remove rRNA and enrich coding sequences, subtractive hybridization procedures have become the approach of choice prior to RNA-seq, with their efficiency varying in a manner dependent on sample type and composition. Yet, despite an increasing number of RNA-seq studies, co...

Single-cell sampling with RNA-seq analysis plays an important role in reference laboratory; cytogenomic diagnosis for specimens on glass-slides or rare cells in circulating blood for tumor and genetic diseases; measurement of sensitivity and specificity in tumor-tissue genomic analysis with mixed-cells; mechanism analysis of differentiation and proliferation of cancer stem cell for academic pur...

Next-generation sequencing has been widely used for the genome-wide profiling of histone modifications, transcription factor binding and gene expression through chromatin immunoprecipitated DNA sequencing (ChIP-seq) and cDNA sequencing (RNA-seq). Here, we describe a versatile library construction method that can be applied to both ChIP-seq and RNA-seq on the widely used Illumina platforms. Stan...

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...