P L E K                                   
predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme
Features of 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).

Alignment-free

PLEK is a novel alignment-free tool.

Accurate

10-fold cross-validation on the human training datasets was performed. The accuracy of PLEK was 95.6%.

Balance between specificity and sensitivity
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PLEK achieved an optimal balance between high specificity and high sensitivity (0.946 and 0.942 on PacBio, 0.955 and 0.925 on 454).

Robust to indel
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Comparison of robustness towards indel sequencing errors. The x-axis is the indel numbers per 100 bases (indel sequencing error rates). Performance (accuracy) of CNCI declines significantly as the indel error rate increases.

Fast
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Several tools was tested on 1,000 human mRNA transcripts and 1,000 human lncRNA transcripts. PLEK took 128 seconds to process the data, which was approximately eightfold faster than CNCI (1,048 seconds), 244 times than CPC (31,247 seconds) and 1,421 times than PhyloCSF (181,925 seconds). Additionally, PLEK could be easily configured for multi-threading parallel computing, which will further save computation time. Thus, PLEK is especially suitable for classifying a large number of transcripts conducted by RNA-seq technologies.

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.