In short, Cufflinks and similar exon-focused algorithms are not suited to distinguish between the sorts of small and closely spaced regulatory elements that GRO-sequencing reveals. For example, Cufflinks is optimized for paired-end reads, expects uniform density for a given transcript (whereas GRO-seq can reveal pausing and other biologically relevant deviations from uniformity) and aims to accommodate large gaps (introns) in reads that result from splicing rather than from transcriptional breaks. New transcripts can be identified using software such as Cufflinks ( 8), but these rely on assumptions optimized for spliced RNA. Existing analysis of GRO-seq data relies largely on adaptations of RNA-sequencing analysis techniques, with expression levels calculated from tag counts over gene bodies, promoters or other explicitly defined genomic regions ( 1–4, 7). Units of transcription must be inferred de novo from the short read output of GRO-sequencing experiments. To take full advantage of this novel data, regions beyond existing annotations must be considered. Unlike ChIP-seq, peaks are not the primary unit of output, and, unlike RNA-seq, nascent transcripts can be anywhere, so relying on previously annotated regions such as NCBI Reference Sequence (RefSeq) ( 6) or microRNA genes is insufficient. This new sequencing method presents a new algorithmic challenge, as the peak-calling and exonic RNA identification techniques developed for other sequencing methods do not address the particular output of GRO-seq. The data collected, however, are both immense and unique each experiment yields tens of millions of strand-specific short RNA reads across the entire genome. GRO-seq thus offers unprecedented insight into the generation of a vast repertoire of non-coding transcripts that are of potential functional significance. The capture of nascent transcripts in each of these conditions reveals a variety of RNA species beyond the standard set derived from genes encoding proteins and microRNAs, including enhancer RNA (eRNA), long intergenic RNA (lincRNA) ( 2) and promoter-associated RNA ( 1, 5). This sequencing technique is now being used to inspect the nature of transcriptional regulation in a number of experimental conditions ( 1–4). GRO-seq captures a point-in-time snapshot of active transcription genome-wide and returns data on the position, length and orientation of nascent transcripts. Global run-on sequencing (GRO-seq) ( 1) is a recent addition to the series of sequencing-based methods that holds particular promise for understanding real-time transcriptional behavior. High-throughput sequencing has opened up a new window into transcriptional biology and the complex regulatory networks that define RNA and DNA interactions. Vespucci thereby provides a robust system for defining, storing and analyzing diverse classes of primary RNA transcripts that are of increasing biological interest. In addition, Vespucci generates assemblies for un-annotated non-coding RNAs such as those transcribed from enhancer-like elements. In doing so, we demonstrate that Vespucci expands existing annotations for mRNAs and lincRNAs by defining the primary transcript beyond the polyadenylation site. We use this method to analyze GRO-sequencing data from primary mouse macrophages and derive novel quantitative insights into the extent and characteristics of non-coding transcription in mammalian cells. Here, we present a novel algorithm for de novo transcript identification from GRO-sequencing data, along with a system that determines transcript regions, stores them in a relational database and associates them with known reference annotations. However, GRO-sequencing presents new algorithmic challenges, as existing analysis platforms for ChIP-seq and RNA-seq do not address the unique problem of identifying transcriptional units de novo from short reads located all across the genome. Global run-on sequencing (GRO-seq) is a recent addition to the series of high-throughput sequencing methods that enables new insights into transcriptional dynamics within a cell.
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