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novoBreak specifications


Unique identifier OMICS_13803
Name novoBreak
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C, Perl
Computer skills Advanced
Version 1.1.3
Stability Stable
Maintained Yes


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  • person_outline Ken Chen <>

Publication for novoBreak

novoBreak in publications

PMCID: 5931083
PMID: 29718005
DOI: 10.1038/sdata.2018.79

[…] as key results of the in-depth analyses of the sequencing data of the 3k rg., in this data descriptor, we present the rice sv data obtained by calling against the nipponbare reference genome using novobreak because it had the lowest false positive rate when compared with results from several tools such as breakdancer and delly. this sv data set contains a total number of 93,683 svs (deletions, […]

PMCID: 5826927
PMID: 29483503
DOI: 10.1038/s41467-018-03273-1

[…] (e.g., barnacle and dissect, which are rna-seq analysis tools that can also be used to analyze genomic data). there are also several tools that employ a combination of these (e.g., pindel, delly, novobreak, and gasvpro), which, unfortunately, only consider uniquely mapped reads and cannot identify alterations in repetitive dna. in fact, no available tool, even those designed to identify gene […]

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novoBreak institution(s)
Department of Bioinformatics and Computational Biology, The University of Texas Maryland Anderson Cancer Center, Houston, TX, USA; Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; Department of Experimental Radiation Oncology, The University of Texas Maryland Anderson Cancer Center, Houston, TX, USA; McDonnell Genome Institute, Washington University, St. Louis, MI, USA; Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
novoBreak funding source(s)
This work was supported by the National Institutes of Health (grants n°R01 CA172652 and U41 HG007497), the National Cancer Institute Cancer Center Support (grant n°P30 CA016672), the Andrew Sabin Family Foundation and a training fellowship from the Computational Cancer Biology Training Program of the Gulf Coast Consortia (grant n°RP140113).

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