TIPR

TIPR is a sequence-based machine learning model for identifying transcription start sites (TSSs) with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize. TIPR predicts not only the locations of TSSs but also the expected spatial initiation pattern each TSS will form along the chromosome, which has the potential to improve gene annotations and our understanding of the regulation of transcription initiation. The model is available online and has a high nucleotide resolution, locating TSSs within 10 nucleotides or less on average.

Topic

Data mining;Machine learning;Transcription factors and regulatory sites;DNA;Transcriptomics

Detail

  • Operation: Transcriptional regulatory element prediction

  • Software interface: Command-line user interface

  • Language: Shell;Perl;Python

  • License: Not stated

  • Cost: Free

  • Version name: -

  • Credit: The National Institutes of Health, Oregon State University.

  • Input: FASTA

  • Output: -

  • Contact: megrawm@science.oregonstate.edu

  • Collection: -

  • Maturity: -

Publications

  • TIPR: transcription initiation pattern recognition on a genome scale.
  • Morton T, et al. TIPR: transcription initiation pattern recognition on a genome scale. TIPR: transcription initiation pattern recognition on a genome scale. 2015; 31:3725-32. doi: 10.1093/bioinformatics/btv464
  • https://doi.org/10.1093/bioinformatics/btv464
  • PMID: 26254489
  • PMC: PMC4804766

Download and documentation

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