TAAPP: Tiling array analysis pipeline for prokaryotes

Ranjit Kumar, Shane C. Burgess, Mark L. Lawrence, Bindu Nanduri

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


High-density tiling arrays provide closer view of transcription than regular microarrays and can also be used for annotating functional elements in genomes. The identified transcripts usually have a complex overlapping architecture when compared to the existing genome annotation. Therefore, there is a need for customized tiling array data analysis tools. Since most of the initial tiling arrays were conducted in eukaryotes, data analysis methods are well suited for eukaryotic genomes. For using whole-genome tiling arrays to identify previously unknown transcriptional elements like small RNA and antisense RNA in prokaryotes, existing data analysis tools need to be tailored for prokaryotic genome architecture. Furthermore, automation of such custom data analysis workflow is necessary for biologists to apply this powerful platform for knowledge discovery. Here we describe TAAPP, a web-based package that consists of two modules for prokaryotic tiling array data analysis. The transcript generation module works on normalized data to generate transcriptionally active regions (TARs). The feature extraction and annotation module then maps TARs to existing genome annotation. This module further categorizes the transcription profile into potential novel non-coding RNA, antisense RNA, gene expression and operon structures. The implemented workflow is microarray platform independent and is presented as a web-based service. The web interface is freely available for acedemic use at http://lims.lsbi.mafes.msstate.edu/TAAPP-HTML/.

Original languageEnglish (US)
Pages (from-to)56-62
Number of pages7
JournalGenomics, Proteomics and Bioinformatics
Issue number1-2
StatePublished - Apr 2011


  • Operon
  • Prokaryotes
  • Small RNA
  • Tiling arrays
  • Transcriptomics

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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