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PlncRRO #

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6회 업데이트 됨.

  • 최초 작성자
  • 최근 업데이트

Structured data


Introduction #

PLncPRO (Plant Long Non-Coding RNA Prediction by Random Forest) is a tool to discover long non-coding RNAs (lncRNAs) in plants. This tools developed based on the classification principle using the random forest machine. This machine classifies the given transcripts in to coding or non-coding and non-coding groups.

Machine #

  • Random Forest

Features #

  • 71 dimensional features vector particular to sequence batch coding and non-coding
  • Framefinder (FFscore, ORF coverage)
  • BLASTX (Number of Hits, Significant Score, Total bit Score, Frame entropy)
  • Sequence Trimers and tetramers from the given sequence to optimize the codon bias

Usage #

python2.7 -p <Name> -i <Input Seq> -m <monocot.model> -o <OutPut> -d <BlastDatabase> -t <Cpus> -v

Output #

Predicted as neg: 83938: 0.592648553999
Predicted as pos: 57694: 0.407351446001

Reference #

  • PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea

Incoming Links #

Related Bioinformaticses #