PLEKv2 Files

PLEK2: a novel method for predicting lncRNA and mRNA based on sequence intrinsic features and Coding-Net model (Upgraded version of PLEK)
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PLEK: predictor of long non-coding RNAs and mRNAs based on k-mer scheme (PLEK download | SourceForge.net)
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INSTALLATION
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LncRNA participates in many important regulatory activities of organisms. Its biological structure is similar to messenger RNA (mRNA), in order to distinguish between lncRNA and mRNA (messenger RNA) transcripts more quickly and accurately, we upgraded the alignment-free PLEK to PLEK2.


Requirements
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+ [Linux]
+ [Python version > = 3.8.5] (http://www.python.org/)
install regex package
$ pip install regex
install keras==2.4.3 package
$ pip install keras==2.4.3
install pandas package
$ pip install pandas
install tensorflow==2.4.1 package
$ pip install tensorflow==2.4.1
install bio version >= 1.3.2 package
$ pip install bio
install numpy package
$ pip install numpy==1.19.2
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Steps:
1. Download PLEK2_model_v3.tar.gz from https://sourceforge.net/projects/plek2/files/ and decompress it.
$ tar zvxf PLEK2_model_v3
2. Compile PLEK2_model_v3
$ cd PLEK2_model_v3
3. decompress Coding_Net_kmer6_orf.h5.bz2 model
$ bunzip2 Coding_Net_kmer6_orf.h5.bz2 
4.  decompress Coding_Net_kmer6_orf_Arabidopsis.h5.bz2 model
$ bunzip2 Coding_Net_kmer6_orf_Arabidopsis.h5.bz2


USAGE
Python PLEK2.py -i fasta_file -m model(ve: vertebrate , pl: plant)
#-fasta        The name of a fasta file, its sequences are to be predicted.
   
Examples:
$ python PLEK2.py -i PLEK2_test.fa -m ve

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Haotian Zhou, Master
School of Computer Science and Engineering,
Xi'an University of Technology,
5 South Jinhua Road,
Xi'an, Shaanxi 710048, P.R China

Aimin Li, PhD
School of Computer Science and Engineering,
Xi'an University of Technology,
5 South Jinhua Road,
Xi'an, Shaanxi 710048, P.R China

2350837044@qq.com 

liaiminmail AT gmail.com
emanlee815 AT 163.com

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Citation:

Aimin Li*, Haotian Zhou, Siqi Xiong*, Junhuai Li, Saurav Mallik, Rong Fei, Yajun Liu, Hongfang Zhou, Xiaofan Wang, Xinhong Hei, Lei Wang. PLEKv2: predicting lncRNAs and mRNAs based on intrinsic sequence features and the coding-net model. BMC Genomics 2024, 25(1):756. https://doi.org/10.1186/s12864-024-10662-y

Aimin Li, Junying Zhang*, 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(1): 311~314.  https://doi.org/10.1186/1471-2105-15-311