lncRScan-SVM statistics

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lncRScan-SVM specifications

Information


Unique identifier OMICS_14412
Name lncRScan-SVM
Alternative name lncRScan
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS
Programming languages Python
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.0.1
Stability Stable
Requirements
gffread, bigWigAverageOverBed, wigToBigWig, txCdsPredict, fetchChromSizes, BioPython, LIBSVM
Maintained Yes

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Versioning


No version available

Maintainer


  • person_outline Lei Sun

Publication for lncRScan-SVM

lncRScan-SVM citations

 (7)
library_books

Prediction of plant lncRNA by ensemble machine learning classifiers

2018
BMC Genomics
PMCID: 5930664
PMID: 29720103
DOI: 10.1186/s12864-018-4665-2

[…] s for predicting and identifying lncRNAs is a likely contributor to the lack of validated plant lncRNAs.Currently, many lncRNA prediction softwares that are available to researchers, such as PLEK [], lncRScan-SVM [], and COME [], use machine learning methods trained on data consisting of lncRNA transcripts yet to be empirically validated. Without empirical validation, many of these predicted lncRN […]

library_books

lncRNA screen: an interactive platform for computationally screening long non coding RNAs in large genomics datasets

2017
BMC Genomics
PMCID: 5458484
PMID: 28583068
DOI: 10.1186/s12864-017-3817-0

[…] hensive databases for known, annotated lncRNAs. iSeeRNA [], CPC [] and CPAT [] introduced machine learning-based approaches only focusing on assessment of the coding probability of potential lncRNAs. lncRScan [] and its new version lncRScan-SVM [] are pipelines which provide novel multi-exonic lncRNA only discovery from RNA sequencing (RNA-seq), lacking the ability to integrate other data types to […]

library_books

PlantRNA_Sniffer: A SVM Based Workflow to Predict Long Intergenic Non Coding RNAs in Plants

2017
Noncoding RNA
PMCID: 5831995
PMID: 29657283
DOI: 10.3390/ncrna3010011

[…] ay tend to classify novel PCTs into ncRNAs, if they have not been recorded in the protein databases. PSoL [], SnoReport [], RNAsnoop [], and SnoStrip [] are methods designed to classify small ncRNAs. LncRScan-SVM [], lncRNA-MFDL [], lncRNA-ID [], lncRNApred [], PLEK [], and CNCI [] are methods that use machine learning techniques in order to classify lncRNAs. In particular, ISeeRNA [] and linc-SF […]

library_books

Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

2016
Biomed Res Int
PMCID: 5153550
PMID: 28042575
DOI: 10.1155/2016/8496165

[…] LncRScan-SVM [] classifies the sequences mainly by evaluating the qualities of nucleotide sequences, codon sequence, and transcripts structure. The counts and average length of exon in one sequence ar […]

library_books

Use of RNA sequencing to evaluate rheumatic disease patients

2015
PMCID: 4488125
PMID: 26126608
DOI: 10.1186/s13075-015-0677-3

[…] own miRNAs), lncRNAdb (database of lncRNAs), ncRNAdb (database of non-coding regulatory RNAs) and others. Related third party analysis tools for this purpose include mirRanalyzer [], miRTools [], and lncRScan []. Similarly, for estimating the expression of diploid organisms at the haplotype, isoform and gene levels, specific tools are needed to be part of the RNA-seq pipeline, such as MMSEQ [].Vis […]

library_books

Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques

2015
Front Bioeng Biotechnol
PMCID: 4453482
PMID: 26090362
DOI: 10.3389/fbioe.2015.00077

[…] s so far (Guttman et al., ; Cabili et al., ; Pauli et al., ).The pipeline employed by Sun et al. (Sun et al., ) makes use of a software they have specifically developed to detect novel lncRNA, called lncRScan. The pipeline aims at tackling three of the major technical problems encountered in studying lncRNAs through RNA-seq: eliminating partial transcripts and artifacts in the assembled transcript […]

Citations

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lncRScan-SVM institution(s)
School of Information Engineering, Yangzhou University, Yangzhou, China; Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China; School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China; Department of Biological Sciences, XI'an Jiaotong-Liverpool University, Suzhou, China
lncRScan-SVM funding source(s)
This work was supported by National Natural Science Foundation of China (61301220, 61201408, 61401370), China Fundamental Research Funds for the Central Universities (2014QNA84, 2014QNB47), and Jiangsu Natural Science Foundation (BK20140403).

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