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RankMotif++ specifications

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Unique identifier OMICS_13673
Name RankMotif++
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C++
Computer skills Advanced
Stability No
Maintained No

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Publication for RankMotif++

RankMotif++ citations

 (8)
library_books

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

2014
BMC Syst Biol
PMCID: 4305984
PMID: 25605483
DOI: 10.1186/1752-0509-8-S5-S5

[…] nding affinity as inputs and performed a least-squares fit to estimate the position specific affinity matrix that contained the relative energy contribution of each nucleotide at different positions. RankMotif++ [] learned PWM motif models by maximum likelihood estimation of a probabilistic model for binding preferences.Recent advances in high-throughput measurements of binding affinity and machin […]

library_books

A comparative analysis of transcription factor binding models learned from PBM, HT SELEX and ChIP data

2014
Nucleic Acids Res
PMCID: 4005680
PMID: 24500199
DOI: 10.1093/nar/gku117

[…] same TF by one technology, we assigned to each sequence the highest score obtained by such a model. We used five algorithms to generate PWMs from PBM experiments: Amadeus-PBM (), Seed-and-Wobble (), RankMotif++ (), BEEML-PBM () and RAP (). The performance of the models generated by each algorithm was reported in (). For each paired experiment, these models were learned on one array and tested on […]

library_books

Design of shortest double stranded DNA sequences covering all k mers with applications to protein binding microarrays and synthetic enhancers

2013
Bioinformatics
PMCID: 3694677
PMID: 23813011
DOI: 10.1093/bioinformatics/btt230

[…] ns that have a significant contribution (; ). A recent study from the Taipale Laboratory using HT-Selex showed that many TFs have longer motifs that are not covered well by an all 10mer array (). The RankMotif++ algorithm for PBM data also generates motifs of length in most cases (). Covering all k-mers for a greater value of k will lead to improved understanding of TF binding.As the probes are d […]

library_books

Deciphering the Transcriptional Regulatory Network of Flocculation in Schizosaccharomyces pombe

2012
PLoS Genet
PMCID: 3516552
PMID: 23236291
DOI: 10.1371/journal.pgen.1003104

[…] The transcription factor binding specificities were determined by RankMotif++ and MEME . S. pombe promoter sequences 1000 bp upstream of the translational start site were used for these motif-finding algorithms. For MEME, promoter sequences of genes with various lo […]

library_books

Assessment of Algorithms for Inferring Positional Weight Matrix Motifs of Transcription Factor Binding Sites Using Protein Binding Microarray Data

2012
PLoS One
PMCID: 3460961
PMID: 23029415
DOI: 10.1371/journal.pone.0046145

[…] ls , since we choose to focus on simpler, more compact models.In this paper we present a systematic comparison of four algorithms for identifying TFBS motifs from PBM profiles: Seed-and-Wobble (SW) , RankMotif++ (RM) , BEEML-PBM (BE) and the algorithm Amadeus-PBM (AM) introduced here (see ). In 2005, a systematic comparison of computational methods for motif discovery in promoters clarified some […]

library_books

PePPER: a webserver for prediction of prokaryote promoter elements and regulons

2012
BMC Genomics
PMCID: 3472324
PMID: 22747501
DOI: 10.1186/1471-2164-13-299

[…] e existence of protein-DNA interaction, TFBS discovery algorithms have been developed to uncover conserved regions that might act as TFBSs (MEME [], ARCS-Motif [], GLAM2 [], W-AlignACE [], GIMSAN [], RankMotif++ [], GAME [], and Tmod []). This so-called motif mining is based on a collection of genes having a certain correlation. Gene-to-gene correlations can be derived e.g., from transcriptome dat […]


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RankMotif++ institution(s)
Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada; Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Canada; Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
RankMotif++ funding source(s)
This work was supported by the Natural Sciences and Engineering Research Council of Canada.

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