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.
Highlights
High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, the identification of long non-coding RNAs from de novo sequencing data
Different usage frequencies of k-mer strings To verify the difference between Messenger RNA (mRNA) and Long non-coding RNA (lncRNA) in k-mer string usage, we calculated the calibrated usage frequencies of all the 1,364 k-mer patterns in the positive training dataset (22,389 protein-coding transcripts) and negative training dataset (22,389 long non-coding transcripts)
The results showed that the accuracies of PLEK and Coding Potential Calculator (CPC) were not affected by a small amount of indels, whereas the accuracy of Coding-Non-Coding Index (CNCI) decreased significantly with increasing indel error rates, indicating that PLEK is a robust tool for distinguishing protein-coding and non-coding transcripts with homopolymer-associated indel sequencing errors
Summary
High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, 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. CNCI performs better than previous tools for poorly annotated species or those without whole-genome sequence information, but it may misclassify transcripts when there are insertion or deletion (indel) sequencing errors among them. Such errors are common in the current Roche (454) and Pacific Biosciences (PacBio) sequencing platforms [18-21]
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