diChIPMunk protocols

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diChIPMunk specifications

Information


Unique identifier OMICS_00481
Name diChIPMunk
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A DNA sequence
Output data DiChIPMunk converts all DNA sequences from mono to dinucleotide alphabet. Each dinucleotide is constructed from two single neighboring nucleotide letters.
Operating system Unix/Linux
Programming languages Java
Computer skills Advanced
Version 4.3
Stability Stable
Maintained Yes

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Documentation


Maintainer


  • person_outline Ivan Kulakovskiy <>

Publication for diChIPMunk

diChIPMunk in pipeline

2016
PMCID: 5009728
PMID: 27137890
DOI: 10.1093/nar/gkw371

[…] done using macs2 () (https://github.com/taoliu/macs) against preimmune and input control data. peak sets obtained with the preimmune control were smaller and were utilized for motif discovery with dichipmunk (,). resulting dinucleotide position weight matrices were used for motif finding in peaks obtained with the input control. to detect peaks with strong motif occurrences, we estimated […]


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diChIPMunk in publications

 (5)
PMCID: 5438342
PMID: 28526842
DOI: 10.1038/s41598-017-02110-7

[…] 25 binding peaks (kwwgttacat) was consistent with the previously characterized repeat sequence of the ompr-binding site (tttgttacat) (fig. ). in addition, we used two other methods, chipmunk and dichipmunk, to identify dna sequence motifs of ompr–. the identified sequence motifs were similar to that from the meme software tool in that these two methods generated the previously known repeat […]

PMCID: 5333389
PMID: 28249564
DOI: 10.1186/s12859-017-1495-1

[…] have been shown to outperform simpler motif models like the pwm model [–]. examples for highly popular tools that model intra-motif dependencies are dimont [], meme-chip [], deepbind [], and dichipmunk []., in contrast, the second group of de-novo motif discovery approaches known as phylogenetic footprinting incorporates orthologous sequences of at least two phylogenetically related […]

PMCID: 4618835
PMID: 26267829
DOI: 10.1016/j.stemcr.2015.07.004

[…] available chip data and existing binding motif models (positional weight matrices [pwms]) for oct4, sox2, and nanog were collected from available sources (; ; ). the chipmunk for classic pwms () and dichipmunk () programs were used for de novo motif discovery. raw published chip data were processed, and receiver operating characteristic (roc) curves were constructed as described earlier (). […]

PMCID: 4234207
PMID: 24472686
DOI: 10.1186/1471-2164-15-80

[…] four computational models of two fundamental classes: pattern matching based on existing training set of experimentally confirmed tfbss (opwm and sitega) and de novo motif discovery (chipmunk and dichipmunk). to properly select prediction thresholds for the models, we experimentally evaluated affinity of 64 predicted foxa bss using emsa that allows safely distinguishing sequences able to bind […]

PMCID: 3834837
PMID: 24057214
DOI: 10.1093/nar/gkt831

[…] order 1 than for motif order 0 for at least one combination of training and test data sets in supplementary figure s5–s13, and we compare the dependencies detected by dimont to those detected by dichipmunk () in section 6 of the supplementary material., new high-throughput techniques including chip-seq, chip-exo and pbms have greatly increased the quality and amount of data […]


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diChIPMunk institution(s)
Laboratory of Bioinformatics and Systems Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia; Department of Computational Systems Biology, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia; Laboratory of Molecular Genetics Systems, Institute of Cytology and Genetics of the Siberian Division of Russian, Academy of Sciences, Novosibirsk, Russia; Faculty of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia; Laboratory of Regulation of Gene Expression, Institute of Cytology and Genetics of the Siberian Division of Russian Academy of Sciences, Novosibirsk, Russia; Yandex Data Analysis School, Data Analysis Department, Moscow Institute of Physics and Technology, Moscow, Russia; State Research Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russia; Moscow Institute of Physics and Technology, Moscow, Russia
diChIPMunk funding source(s)
This work was supported by a Dynasty Foundation Fellowship; Russian Foundation for Basic Research [12-04-32082] and [12-04-01736]; Presidium of the Russian Academy of Sciences program in Cellular and Molecular Biology; Presidium of the Russian Academy of Sciences Fundamental Research Subprogram “Gene pools dynamics and conservation”; Russian Federation Government Contract 8088 under program “Scientific and pedagogical personnel of innovative Russia”.

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