Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome

Abstract

Recent sequencing technologies that allow massive parallel production of short reads are the method of choice for transcriptome analysis. Particularly, digital gene expression (DGE) technologies produce a large dynamic range of expression data by generating short tag signatures for each cell transcript. These tags can be mapped back to a reference genome to identify new transcribed regions that can be further covered by RNA-sequencing (RNA-Seq) reads. Here, we applied an integrated bioinformatics approach that combines DGE tags, RNA-Seq, tiling array expression data and species-comparison to explore new transcriptional regions and their specific biological features, particularly tissue expression or conservation. We analysed tags from a large DGE data set (designated as ‘TranscriRef’). We then annotated 750 000 tags that were uniquely mapped to the human genome according to Ensembl. We retained transcripts originating from both DNA strands and categorized tags corresponding to protein-coding genes, antisense, intronic- or intergenic-transcribed regions and computed their overlap with annotated non-coding transcripts. Using this bioinformatics approach, we identified ∼34 000 novel transcribed regions located outside the boundaries of known protein-coding genes. As demonstrated using sequencing data from human pluripotent stem cells for biological validation, the method could be easily applied for the selection of tissue-specific candidate transcripts. DigitagCT is available at http://cractools.gforge.inria.fr/softwares/digitagct.

The Landscape of long noncoding RNA classification

“…Serial analysis of gene expression (SAGE) : targets polyadenylated messages and generates a single internal (typically close to the 3′ end) tag per RNA molecule [145]. …”

The ‘TranSeq’ 3′‐end sequencing method for high‐throughput transcriptomics and gene space refinement in plant genomes

“…In this approach, early barcoding and multiplexing allow the preparation and sequencing of an extensive number of samples at once, hereby strongly reducing the sequencing cost per sample. Briefly, fragmented polyadenylated RNA molecules are used as a template for reverse transcription, resulting in a library that merely contains the 3′‐end of all polyadenylated RNA molecules, including mRNA transcripts as well as micro‐RNAs (miRNA) and other non‐coding RNAs [Philippe et al ., ; Figures and a, b and Method S1]. Consequently, this method is able to reveal the location of 3′‐ends of all polyadenylated transcripts in each sample.…”

Characterization of the genome and transcriptome of the blue tit Cyanistes caeruleus: polymorphisms, sex‐biased expression and selection signals

“…; Necsulea & Kaessmann ; Philippe et al . ). The Cufflinks‐predicted exons mapped to the genome were first merged across the three tissues within each individual using Cuffmerge and then intersected across groups of individuals using bedtools v 2.19.1 (Quinlan & Hall ) to reveal the following four groups: (i) exons common to all males for the comparison of RNA‐seq and DNA‐seq variants in the single reference male, (ii) exons common to all individuals, (iii) exons common to all females (but not found in all males) and (iv) exons common to all males (but not found in all females).…”

“…Parsimonious sets of transcripts for each tissue type were assembled using CUFFLINKS v2.1.1 (Trapnell et al 2010; Appendix S1, Supporting information). This step provides a complete picture of the transcriptome, because it takes the untranslated regions of genes into account, and considers differential splicing and poly-adenylated long noncoding RNA and miRNA precursors (Guttman et al 2009;Necsulea & Kaessmann 2014;Philippe et al 2014). The Cufflinks-predicted exons mapped to the genome were first merged across the three tissues within each individual using Cuffmerge and then intersected across groups of individuals using BEDTOOLS V2.19.1 (Quinlan & Hall 2010) to reveal the following four groups: (i) exons common to all males for the comparison of RNA-seq and DNA-seq variants in the single reference male, (ii) exons common to all individuals, (iii) exons common to all females (but not found in all males) and (iv) exons common to all males (but not found in all females).…”