PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme

Abstract

BackgroundHigh-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.ResultsWe 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.ConclusionsPLEK 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/.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-311) contains supplementary material, which is available to authorized users.

Genome-Wide Analysis of mRNAs and lncRNAs of Intramuscular Fat Related to Lipid Metabolism in Two Pig Breeds

“…SNP detection was conducted with the SAMtools pileup program [30] [32], CNCI [33], CPC [34] and Pfam [35] were applied to assess the protein-coding potential of the above-obtained transcripts and the intersection of the results from these software tools was taken as the final result. In order to identify known lncRNAs, BLASTN tool was used to align lncRNA candidates to ALDB (A DomesticAnimal Long Noncoding RNA Database) [36], a database with a focus on the domestic-animal lncRNAs, with the settings of identity=100%, mismatch=0, E-value<1e-10 and gap_opening=0.…”

De novo assembly and annotation of the salivary gland transcriptome of Rhipicephalus appendiculatus male and female ticks during blood feeding

“…Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping was used for pathway analysis and KEGG Automatic Annotation Server (KAAS) to assign I. scapularis KEGG orthology (KO) identifiers to the transcripts (Moriya et al, 2007). The protein-coding potential of the transcripts were determined by the Coding Potential Calculator (CPC) package (Kong et al, 2007), Coding-Potential Assessment Tool (CPAT v1.2.2) package (Wang et al, 2013) and Predictor of lncRNAs and mRNAs based on k-mer scheme (PLEK v1.2) package (Li et al, 2014).…”

Detection of Piwi-interacting RNAs based on sequence features

“…K-mer features were found to be somewhat robust to indel sequencing errors (Li et al, 2014); however, the proposed K-mer wildcards must be more robust than K-mer since it introduces wildcards, allowing for additional non-indel nuclear change sequencing errors. This indicates that the proposed features are robust.…”