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. 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. Source text: Allison et al., 2014.
discriminative Regulatory-Element detection from…
discriminative Regulatory-Element detection from GRO-seq
dREG
A sensitive machine learning method that uses support vector regression to identify…
A sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment. dREG allows TREs to be assayed together with gene expression levels and other transcriptional…
Fast Read Stitcher
Fast Read Stitcher
FStitch
A fast and simple algorithm to detect nascent RNA transcription in global nuclear run-on…
A fast and simple algorithm to detect nascent RNA transcription in global nuclear run-on sequencing (GRO-seq). FStitch takes advantage of two popular machine-learning techniques, a hidden Markov model (HMM) and logistic regression to robustly…
groHMM
groHMM
An analytical pipeline for annotating primary transcripts using global nuclear run-on…
An analytical pipeline for annotating primary transcripts using global nuclear run-on sequencing (GRO-seq) data. groHMM automates many common next-generation sequencing data analysis tasks. Advanced features include a general HMM implementation…
Vespucci
Vespucci
An algorithm for de novo transcript identification from GRO-sequencing data, along with a…
An 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. Vespucci provides a…