Dataset Information

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/.

SUBMITTER: Li A

PROVIDER: S-EPMC4177586 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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

Li Aimin A Zhang Junying J Zhou Zhongyin Z

BMC bioinformatics 20140919

<h4>Background</h4>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.<h4>Results</h4>We present an alignment-free tool called PLEK (predictor of long non-coding RNAs and messenger RNAs based on an improved ...[more]

PMID: 25239089

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Project description:Objective: Retinal neovascularization is a severe complication of many ocular diseases. To clarify the possible functions and therapeutic potential of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in retinal neovascularization, we assessed their expression profile in a mouse model of oxygen-induced retinopathy (OIR). Methods: Microarray analysis was performed to identify altered lncRNA and mRNA expressions between OIR and control mice. The microarray results were validated by qRT-PCR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to determine biological functions and signaling pathways of the altered or interacted mRNAs. A coding-non-coding gene co-expression (CNC) network was constructed to identify the interaction of lncRNAs and mRNAs. Results: We identified 198 up-regulated and 175 down-regulated lncRNAs (fold change≥2.0, P<0.05), respectively in OIR mice compared to control mice. We also identified 412 up-regulated and 127 down-regulated mRNAs (fold change≥2.0, P<0.05), respectively in OIR mice compared to control mice. GO and KEGG analyses suggested that altered mRNAs were enriched in immune system process, exopeptidase activity, ECM-receptor interaction and protein digestion and absorption. Four validated lncRNAs (ENSMUST00000165968, ENSMUST00000153785, ENSMUST00000134409, and ENSMUST00000154285) and the nearby coding gene pairs were analyzed. A CNC network profile based on those validated altered lncRNAs as well as 410 interacted mRNAs was composed of 509 connections. Moreover, the GO and KEGG analyses demonstrated that these interacted mRNAs mainly enriched in blood vessel development, angiogenesis, cell adhesion molecules and leukocyte transendothelial migration pathways. Conclusion: Our data highlight the utility of altered lncRNA and mRNA profiling in understanding the pathogenesis of ischemia-induced retinal neovascularization and further suggest that therapeutic potential of altered lncRNA for retinal neovascularization.

Dataset Information

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

Publications

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

Similar Datasets

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