PlncRRO
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최초 작성자
moorthy@insilicogen.com
- 최근 업데이트
Structured data
- Category
- Analysis
Table of Contents
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 prediction.py -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
