CORECLUST specifications

Information


Unique identifier OMICS_12897
Name CORECLUST
Alternative name COnservative REgulatory CLUster STructure
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input data CORECLUST needs a file with TF matrices, a file with training sequences and a file with search sequences, or only a file with regulatory region structure (model parameters) and a file with searching sequences.
Input format .cnt, .mtf, .fa
Operating system Unix/Linux
Programming languages Java
Computer skills Advanced
Stability Stable
Maintained Yes

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Versioning


No version available

Maintainer


  • person_outline Anna A. Nikulova

Publication for COnservative REgulatory CLUster STructure

CORECLUST citations

 (2)
library_books

Alignment free clustering of large data sets of unannotated protein conserved regions using minhashing

2018
BMC Bioinformatics
PMCID: 5838936
PMID: 29506470
DOI: 10.1186/s12859-018-2080-y

[…] proteins. In [, ] an approach for generation of the phylogenetic network of these organisms using clustering of their 13,571 proteins is presented. We have used their data set as input to the NADDA - coreClust two step pipeline to generate clusters of these proteins and then followed the method presented in [] for construction of the adjacency matrix of the organisms and creation of a phylogenetic […]

library_books

A New Algorithm for Identifying Cis Regulatory Modules Based on Hidden Markov Model

2017
Biomed Res Int
PMCID: 5405574
PMID: 28497059
DOI: 10.1155/2017/6274513

[…] ster [] and Cluster-Buster [], only define the distance constraint between motif sites but do not model any order between motif sites within a CRM. Subsequent methods, such as Stubb [], BayCis [] and CORECLUST [], define transitions between motif sites to infer the possible spatial arrangement of motif sites within a CRM. The difference between these methods is that Stubb defines the correlation b […]

CORECLUST institution(s)
Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia; Research and Training Center ‘‘Bioinformatics’’, Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia; Department of Oncology, Division of Biostatistics and Bioinformatics, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Laboratory of Bioinformatics, State Research Institute of Genetics and Selection of Industrial Microorganisms, Genetika, Moscow, Russia; Department of Computational Systems Biology, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia; Temporarily at Unite Mixte de Recherche CNRS (UMR 5800), Talence, France
CORECLUST funding source(s)
Programs 6 and 17, Russian Academy of Sciences; Russian Foundation of Basic Research [grant numbers 09-04-92742, 11-04-02016-a, 10-04-92663-IND_a and 11-04-02051-a]; State Contract of Russian Ministry of Education and Science [grant numbers 07.514.11.4007 and 07.514.11.4005]; Russian Academy of Science Presidium Program on Molecular and Cellular Biology; Johns Hopkins University Framework for the Future; Commonwealth Foundation and the SKCCC Center for Personalized Cancer Medicine

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