Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts

Sun, Liang; Luo, Haitao; Bu, Dechao; Zhao, Guoguang; Yu, Kuntao; Zhang, Changhai; Liu, Yuanning; Chen, Runsheng; Zhao, Yi

doi:10.1093/nar/gkt646

Cited by 1,577 publications

(1,233 citation statements)

References 25 publications

Supporting

Mentioning

1,228

Contrasting

Unclassified

Order By: Relevance

“…It was identifi ed to be an lncRNA rather than a protein-coding transcript by CNCI software ( 18 ). We performed a codon substitution frequency analysis using PhyloCSF ( 19 ).…”

Section: Research Articlementioning

confidence: 99%

LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern

et al. 2016

View full text Add to dashboard Cite

Long noncoding RNAs (lncRNA) play a role in carcinogenesis. However, the function of lncRNAs in human gastric cancer remains largely unknown. In this study, we identifi ed a novel lncRNA, GClnc1, which was upregulated and associated with tumorigenesis, tumor size, metastasis, and poor prognosis in gastric cancer. GClnc1 affected gastric cancer cell proliferation, invasiveness, and metastasis in multiple gastric cancer models. Mechanistically, GClnc1 bound WDR5 (a key component of histone methyltransferase complex) and KAT2A histone acetyltransferase, acted as a modular scaffold of WDR5 and KAT2A complexes, coordinated their localization, specifi ed the histone modifi cation pattern on the target genes, including SOD2 , and consequently altered gastric cancer cell biology. Thus, GClnc1 is mechanistically, functionally, and clinically oncogenic in gastric cancer. Targeting GClnc1 and its pathway may be meaningful for treating patients with gastric cancer. SIGNIFICANCE:This report documents a novel lncRNA, GClnc1, which may act as a scaffold to recruit the WDR5 and KAT2A complex and modify the transcription of target genes. This study reveals that GClnc1 is an oncogenic lncRNA in human gastric cancer. Cancer Discov; 6(7); 784-801.

show abstract

“…It was identifi ed to be an lncRNA rather than a protein-coding transcript by CNCI software ( 18 ). We performed a codon substitution frequency analysis using PhyloCSF ( 19 ).…”

Section: Research Articlementioning

confidence: 99%

LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Different from linear discriminant functions, non-liner kernels have complex discriminant functions for complicated data examples. Usually, classical non-linear kernels designed for particular applications, including polynomial kernels [76], Gaussian kernels [79,80], spectrum kernels [81], weighted degree (WD) kernels [74], WD kernels with shifts [82], string kernels [83,84], Oligo kernels [85], convolutional kernels [86], and so forth, can be used for modeling more complex decision boundaries in predicting various signal sensors [72,74,87].…”

Section: Support Vector Machines and Kernel Methodsmentioning

confidence: 99%

A Comprehensive Review of Emerging Computational Methods for Gene Identification

2016

J Inf Process Syst

View full text Add to dashboard Cite

Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.

show abstract

“…Accumulating evidence showed that the protein-coding genes are accounted for only 50% of inal assembled transcriptome data. Mining inal non-redundant transcriptome data via long non-coding RNA identiication tools such as PLEK [90], lncRScan-SVM [91], FEELnc [92] or measuring protein coding potential of transcripts using various tools such as coding potential calculator (CPC) [93], coding potential assessment tool (CPAT) [94], coding-non-coding index (CNCI) [95] provides us more information about the transcriptome landscape of non-model organism.…”

Section: Transcriptomics Tells More: Focusing On Speciic Annotation Tmentioning

confidence: 99%

Transcriptome Analysis for Non-Model Organism: Current Status and Best-Practices

Eldem¹,

Zararsız²,

Taşçi³

et al. 2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

View full text Add to dashboard Cite

Since transcriptome analysis provides genome-wide sequence and gene expression information, transcript reconstruction using RNA-Seq sequence reads has become popular during recent years. For non-model organism, as distinct from the reference genome-based mapping, sequence reads are processed via de novo transcriptome assembly approaches to produce large numbers of contigs corresponding to coding or non-coding, but expressed, part of genome. In spite of immense potential of RNA-Seq-based methods, particularly in recovering full-length transcripts and spliced isoforms from short-reads, the accurate results can be only obtained by the procedures to be taken in a step-by-step manner. In this chapter, we aim to provide an overview of the state-of-the-art methods including (i) quality check and pre-processing of raw reads, (ii) the pros and cons of de novo transcriptome assemblers, (iii) generating non-redundant transcript data, (iv) current quality assessment tools for de novo transcriptome assemblies, (v) approaches for transcript abundance and diferential expression estimations and inally (vi) further mining of transcriptomic data for particular biological questions. Our intention is to provide an overview and practical guidance for choosing the appropriate approaches to best meet the needs of researchers in this area and also outline the strategies to improve on-going projects.

show abstract

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts

Cited by 1,577 publications

References 25 publications

LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern

LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern

A Comprehensive Review of Emerging Computational Methods for Gene Identification

Transcriptome Analysis for Non-Model Organism: Current Status and Best-Practices

Contact Info

Product

Resources

About