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.

Project description:BackgroundToxoplasma gondii is an intracellular protozoan parasite that can infect a wide range of warm-blooded animals, including humans. It poses significant health risks, particularly in immunocompromised individuals and during pregnancy, leading to severe disease manifestations. The liver, being a crucial organ involved in immune response and metabolic regulation, plays a critical role in the host's defense against T. gondii infection.MethodsIn this study, we utilized RNA sequencing to investigate the expression profiles of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in the liver of mice infected with T. gondii. By employing this method, we obtained a comprehensive overview of the alterations in gene expression occurring in the liver during infection.ResultsBy comparing the infected groups to the control groups, we identified numerous differentially expressed lncRNAs DElncRNAs and DEmRNAs at two stages of infection. Specifically, at the acute infection stage, we found 628 DElncRNAs, and 6346 DEmRNAs. At the chronic infection stage, we identified 385 DElncRNAs and 2513 DEmRNAs. Furthermore, we identified 1959 commonly expressed DEmRNAs, including IL27, Nos2, and Cxcr2, across two infection stages. Enrichment and co-location analyses revealed pathways linked to immune and inflammatory responses during T. gondii infection. Notably, through co-location analysis, our analysis revealed several DElncRNAs, including Gm29156, Gm29157, and Gm28644, which are potentially implicated in the progression of liver inflammation induced by T. gondii. Additionally, functional enrichment analysis disclosed stage-specific characteristics of liver inflammation and immune response, alongside changes in metabolic regulation and immunosuppression pathways.ConclusionsOur findings provide valuable insights into the expression patterns of lncRNAs and mRNAs in the liver at different stages of T. gondii infection. We identified potential regulatory factors and pathways implicated in liver inflammation, thereby enhancing our understanding of the molecular mechanisms underlying liver inflammation and immune responses during T. gondii infection. These findings could contribute to the development of targeted therapeutic strategies for liver inflammation in the context of T. gondii infection.

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.

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