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chevron_left Peak calling Topologically domain calling Hybridization capture Chromatin loop detection Bioinformatics workflows Data integration Contact matrix normalization Contact matrix generation Quality control Read alignment chevron_right

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Juicer specifications


Unique identifier OMICS_13559
Name Juicer
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java, Perl, Shell (Bash)
Parallelization CUDA, Other
License MIT License
Computer skills Advanced
Stability Stable
GNU CoreUtils, BWA, CUDA, Java SDK, Apache Ant, OpenLava, LSF, SLURM, GridEngine
Maintained Yes


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  • person_outline Aiden E.L.

Publications for Juicer

Juicer citations


Computational Methods for Assessing Chromatin Hierarchy

Comput Struct Biotechnol J
PMCID: 5910504
PMID: 29686798
DOI: 10.1016/j.csbj.2018.02.003

[…] hardware is not required but could greatly accelerate the process. in a study comparing the performance of four different cluster systems that process 1.5 billion paired-end hi-c reads using juicer [,], the total required times varied from ~12,000 to ~600 h. this 20-fold increase in computing efficiency was achieved largely by incorporating general-purpose graphics processing units […]


Genomic basis of recombination suppression in the hybrid between Caenorhabditis briggsae and C. nigoni

Nucleic Acids Res
PMCID: 5814819
PMID: 29325078
DOI: 10.1093/nar/gkx1277

[…] those described earlier (), without size selection., for hi-c data analysis, a duplicate-free list of paired alignments of hi-c reads against a given contig assembled with slrs was generated using juicer pipeline (). the paired alignments were used as input for 3d de novo assembly (3d-dna) pipeline () with the following parameters: haploid model, three rounds of mis-join correction and six […]


Hybrid de novo genome assembly and centromere characterization of the gray mouse lemur (Microcebus murinus)

BMC Biol
PMCID: 5689209
PMID: 29145861
DOI: 10.1186/s12915-017-0439-6

[…] products via biotinylation and prepared for sequencing on the illumina platform. prior to deep sequencing, approximately 1 million reads were sequenced from each library and processed with the juicer pipeline [] in order to perform quality control assessments, such as calculating the percent of read pairs representing hi-c contacts as well as the frequency of the ligation motif., a total […]


Topological organization and dynamic regulation of human tRNA genes during macrophage differentiation

Genome Biol
PMCID: 5607496
PMID: 28931413
DOI: 10.1186/s13059-017-1310-3

[…] trna genes were within the specified distance. in situ hi-c contact domains were defined in thp-1 monocytes using the previously described arrowhead algorithm at 5-kb resolution with default juicer parameters []. in total, 12,272 contact domains were identified []. tdna domains were defined as any hi-c contact domain, profiled in thp-1 cells by high-throughput chromosome conformation […]


Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction

PMCID: 5615356
PMID: 28885554
DOI: 10.3390/genes8090223

[…] contact frequencies within tads []. topdom is another easy-to-implement pipeline to study cross-tissue tad conservation []. to tackle terabase-size data for those with less informatics experience, juicer is recommended as a one-click system for hi-c experiments, although essentially it needs much more parallel computing resources []., proper gene expression requires communication […]


Challenges for visualizing three‐dimensional data in genomic browsers

PMCID: 5638070
PMID: 28771695
DOI: 10.1002/1873-3468.12778

[…] pairs and associated scores (interaction counts). for example, such data can be visualized by the washu epigenome browser by reading in tab‐separated text similar to bedtools bedpe format . the juicer software can also read and analyze 3c‐based interaction data by converting experimental reads into a tool‐specific hi‐c format, which can then be visualized using the juicebox browser . […]

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Juicer institution(s)
The Center for Genome Architecture, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Department of Computer Science and Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA; Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA; Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Center for Theoretical Biological Physics, Rice University, Houston, TX, USA; Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA
Juicer funding source(s)
This work was supported by an NSF Graduate Research Fellowship (DGE0946799 and DGE1144152), an NIH New Innovator Award (OD008540-01), an NSF Physics Frontier Center (PHY-1427654, Center for Theoretical Biological Physics), an NHGRI CEGS (HG006193), NVIDIA, IBM, Google, a CPRIT Scholar Award (R1304), a McNair Medical Institute Scholar Award, and the President’s Early Career Award in Science and Engineering, and an NHGRI grant (HG003067).

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