MatrixREDUCE statistics

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Citations per year

Number of citations per year for the bioinformatics software tool MatrixREDUCE

Tool usage distribution map

This map represents all the scientific publications referring to MatrixREDUCE per scientific context
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MatrixREDUCE specifications


Unique identifier OMICS_13319
Name MatrixREDUCE
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Computer skills Advanced
Stability Stable
Source code URL
Maintained Yes


No version available


  • person_outline Harmen J. Bussemaker

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Publications for MatrixREDUCE

MatrixREDUCE citations


RNA protein binding motifs mining with a new hybrid deep learning based cross domain knowledge integration approach

BMC Bioinformatics
PMCID: 5331642
PMID: 28245811
DOI: 10.1186/s12859-017-1561-8

[…] sing the advanced computational methods [–].At the very beginning of the methodology development of this field, predictors are mainly constructed by only using the sequence information. For instance, MatrixREDUCE simply fits a statistical mechanical model to infer the sequence-specific binding sites for transcription factors from sequences []. DRIMust discovers motifs by integrating the minimum hy […]


Integrating Epigenomics into the Understanding of Biomedical Insight

Bioinform Biol Insights
PMCID: 5138066
PMID: 27980397
DOI: 10.4137/BBI.S38427

[…] and functional characterization. Previously developed tools for DNA and protein motif discoveries can be implemented to the RNA datasets and performed well, which include HOMER, MEME, cERMIT, GLAM2, MatrixREDUCE, and RNAcontext. Although there are tools for the ncRNA secondary structure prediction and functional annotation, such as GraphProt, CapR, and LncRNA2Function, there are still significant […]


Modeling protein–DNA binding via high throughput in vitro technologies

Brief Funct Genomics
PMCID: 5439287
PMID: 27497616
DOI: 10.1093/bfgp/elw030

[…] tion Maximization (MEME []), Gibbs sampling (AlignACE []), efficient enumeration (Amadeus []) and neural networks (ANN-Spec []). Methods that use weights or a ranked list of genes include DRIM [] and MatrixREDUCE []. A survey of motif finding tools can be found in [, ].The problem of inferring a motif from high-throughput in vitro data can be naively solved by methods developed for motif finding i […]


Comprehensive genome wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation

BMC Genomics
PMCID: 4474539
PMID: 26099425
DOI: 10.1186/1471-2164-16-S7-S12

[…] ability. If the concentration of protein is very low then the Fermi-Dirac function can be approximated by a Maxwell-Boltzmann protein binding function []PS≈exp(-E∙S). In earlier works, BayesPI [] and MatrixREDUCE [] had used Femi-Dirac form and Maxwell-Boltzmann function to model genome-wide in vivo protein-DNA interaction experiments, respectively, by combining measured TF occupancy data and PBEM […]


Building accurate sequence to affinity models from high throughput in vitro protein DNA binding data using FeatureREDUCE

PMCID: 4758951
PMID: 26701911
DOI: 10.7554/eLife.06397.030

[…] f PBM data, the PSAM parameters are inferred by performing a nonlinear fit of a sequence-based model that predicts the signal intensity for each probe. The first implementations of this idea were the MatrixREDUCE (; ) and PREGO () algorithms; a more recent extension is BEEML-PBM ().Whether dependencies between nucleotide positions can be accurately estimated from PBM data and used to refine models […]


Modeling DNA affinity landscape through two round support vector regression with weighted degree kernels

BMC Syst Biol
PMCID: 4305984
PMID: 25605483
DOI: 10.1186/1752-0509-8-S5-S5

[…] (k-mers) [,]. MDscan [], for example, combined the word enumeration and position-specific weight matrix updating to iteratively approximate maximum a posteriori scoring function. Foat et al. proposed MatrixREDUCE [], a statistical mechanics method that took high-throughput measurements of binding affinity as inputs and performed a least-squares fit to estimate the position specific affinity matrix […]

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MatrixREDUCE institution(s)
Department of Biological Sciences, Columbia University, New York, NY, USA; Department of Biology, University of Missouri, St. Louis, MO, USA; Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, USA; Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA
MatrixREDUCE funding source(s)
National Institutes of Health Grants GM008798, LM007276, GM63759 and HG003008

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