BlockClust statistics

info info

Citations per year

Number of citations per year for the bioinformatics software tool BlockClust
info

Tool usage distribution map

info info

Associated diseases

info

Popular tool citations

chevron_left Bioinformatics workflows chevron_right
Want to access the full stats & trends on this tool?

BlockClust specifications

Information


Unique identifier OMICS_04641
Name BlockClust
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

Download


galaxy.png

Versioning


No version available

Publication for BlockClust

BlockClust citations

 (3)
library_books

The discovery potential of RNA processing profiles

2017
Nucleic Acids Res
PMCID: 5814818
PMID: 29155959
DOI: 10.1093/nar/gkx1115

[…] We assessed the accuracy of SeRPeNT by performing a comparison against BlockClust (), an unsupervised method that predicts known sncRNA families from sncRNA-seq data. We evaluated the accuracy to detect known miRNAs, tRNAs and snoRNAs from the GENCODE annotation () using […]

library_books

A Review on Recent Computational Methods for Predicting Noncoding RNAs

2017
Biomed Res Int
PMCID: 5434267
PMID: 28553651
DOI: 10.1155/2017/9139504

[…] ray [] is used to scan the long and macro non-protein-coding RNAs related to cell-cycle, p53, and STAT3 pathways. DGE is used for discovering novel polyA+noncoding transcripts within human genome []. BlockClust [] tries to predict the ncRNA modified after its transcription by combining the sequence and secondary structure information with a graph-kernel SVM, whose novel thinking lies in a new stra […]

library_books

Emerging applications of read profiles towards the functional annotation of the genome

2015
Front Genet
PMCID: 4437211
PMID: 26042150
DOI: 10.3389/fgene.2015.00188

[…] eepBlockAlign, it is based on a graph-kernel method trained on different read profile features such as minimum read length and entropy. Due to the nature of its supervised training, the prediction of BlockClust is limited to known ncRNA classes, whose read profiles have been used for training the computational model. Furthermore, primarily due to low number of snoRNAs in the input dataset, all the […]


Want to access the full list of citations?
BlockClust institution(s)
Bioinformatics Group, Department of Computer Science, University of Freiburg, Munich Leukemia Laboratory (MLL), Munich, Centre for Biological Signalling Studies (BIOSS), Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany; Centre for Non-coding RNA in Technology and Health, Bagsvaerd, Denmark

BlockClust reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review BlockClust