P L E K                                   
predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme
PLEK
We present a tool called PLEK (predictor of long non-coding RNAs and messager RNAs based on k-mer scheme), which uses an improved computational pipeline based on k-mer and support vector machine (SVM) to distinguish long non-coding RNAs (lncRNAs) from messager RNAs (mRNAs).

PLEK.py -- used to differentiate lncRNAs from mRNAs.

PLEKModelling.py -- used to build your own classifier with your training data (mRNA/lncRNA transcripts).

Highlights
Users can used the default settings to run PLEK on vertebrate datasets.
Users can use the model we built on maize datasets to predict on other plants.
Users can build their own models to predict on non-vertebrates and others.
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Citation

Aimin Li,Junying Zhang and Zhongyin Zhou: PLEK: A tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme.  BMC bioinformatics 2014, 15:311.

Cited by (Google Scholar)



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Quick Contact

Aimin Li
LiAiminMail@gmail.com
Junying Zhang
jyzhang@mail.xidian.edu.cn
About this website
This website provides guide and files related to PLEK.
Special Thanks
miRBond
ALDB
PigSPS
LibSVM
RefSeq
GENCODE
Ensembl