title: PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme.
abstract: BACKGROUND
  High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, particularly the identification of long non-coding RNAs (lncRNAs) from de novo sequencing data. This requires tools that are not restricted by prior gene annotations, genomic sequences and high-quality sequencing.
  
  
  RESULTS
  We present an alignment-free tool called PLEK (predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme), which uses a computational pipeline based on an improved k-mer scheme and a support vector machine (SVM) algorithm to distinguish lncRNAs from messenger RNAs (mRNAs), in the absence of genomic sequences or annotations. The performance of PLEK was evaluated on well-annotated mRNA and lncRNA transcripts. 10-fold cross-validation tests on human RefSeq mRNAs and GENCODE lncRNAs indicated that our tool could achieve accuracy of up to 95.6%. We demonstrated the utility of PLEK on transcripts from other vertebrates using the model built from human datasets. PLEK attained >90% accuracy on most of these datasets. PLEK also performed well using a simulated dataset and two real de novo assembled transcriptome datasets (sequenced by PacBio and 454 platforms) with relatively high indel sequencing errors. In addition, PLEK is approximately eightfold faster than a newly developed alignment-free tool, named Coding-Non-Coding Index (CNCI), and 244 times faster than the most popular alignment-based tool, Coding Potential Calculator (CPC), in a single-threading running manner.
  
  
  CONCLUSIONS
  PLEK is an efficient alignment-free computational tool to distinguish lncRNAs from mRNAs in RNA-seq transcriptomes of species lacking reference genomes. PLEK is especially suitable for PacBio or 454 sequencing data and large-scale transcriptome data. Its open-source software can be freely downloaded from https://sourceforge.net/projects/plek/files/.
authors.forename: Aimin
authors.initials: A
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, PR China. jyzhang@mail.xidian.edu.cn.
authors.forename: Junying
authors.initials: J
authors.lastname: Zhang
authors.forename: Zhongyin
authors.initials: Z
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D051379:Mice; D058977:Molecular Sequence Annotation; D011506:Proteins / Q000235:genetics; D062085:RNA, Long Noncoding / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics; D017423:Sequence Analysis, RNA; D012984:Software; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D062085:RNA, Long Noncoding; D012333:RNA, Messenger