Differential RNA sequencing data analysis software tools
Differential RNA-sequencing (dRNA-seq) is a high-throughput technique that is used to distinguish primary and processed transcripts. It examines architecture of bacterial operons and provides global transcription start site (TSS) maps and other transcriptome information. dRNA-seq software tools are used for automated de novo TSS annotation from dRNA-seq data that respects the statistics of dRNA-seq libraries and for identification of transcribed and untranscribed genomic segments.
Predicts bacterial transcription start sites (TSSs) from dRNA-seq data. TSSAR is a tool for automated de novo TSS annotation that respects the statistics of dRNA-seq libraries. It is built on a RESTful Client/Server architecture that allows for rapid screening and processing of Next-Generation Sequencing (NGS) data. The RESTful architecture of this tool provides additional extensibility, rendering implementation of new functionality such as promoter or operon characterization straightforward.
Permits the computational evaluation of RNA-Seq data. READemption is an automated RNA-Seq processing with the initial purpose to handle differential RNA-Seq (dRNA-Seq) data for the determination of transcriptional start sites (TSSs) from bacterial, archaeal and eukaryotic species as well as for RNA virus genomes. READemption generates several output files that can be examined with common office suites, graphic programs and genome browser.
Allows the identification of transcriptional units based on dRNA-seq data. RNAseg is an algorithm able to distinguish between transcribed and un-transcribed genomic segments. This tool is used for the mapping of 5’ and 3’ transcript boundaries based on dRNA-seq data. It can help in whole-transcriptome characterization and in the identification of operon structures and 5’- and 3’- Untranslated Transcribed Regions (UTRs).
Enables the automatic inference of transcription start sites (TSSs) from dRNA-seq data. TSSer allows the discovery of TSSs that may not be expected or easily evaluated such as those of antisense transcripts, alternative TSSs and TSSs corresponding to novel genes. This method can provide an initial set of high-confidence TSSs that can be used to train more complex models of transcription regulation, which could be used to iteratively identify additional TSSs, that may be supported by a small number of reads.