motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites.

Simon G Coetzee, Gerhard A Coetzee, Dennis J Hazelett
Author Information
  1. Simon G Coetzee: Bioinformatics and Computational Biology Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA and.
  2. Gerhard A Coetzee: Department of Urology and Preventive Medicine, USC Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
  3. Dennis J Hazelett: Bioinformatics and Computational Biology Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA and.

Abstract

Functional annotation represents a key step toward the understanding and interpretation of germline and somatic variation as revealed by genome-wide association studies (GWAS) and The Cancer Genome Atlas (TCGA), respectively. GWAS have revealed numerous genetic risk variants residing in non-coding DNA associated with complex diseases. For sequences that lie within enhancers or promoters of transcription, it is not straightforward to assess the effects of variants on likely transcription factor binding sites. Consequently we introduce motifbreakR, which allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism or mutation relative to the other. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from many public sources that users can choose from. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within bioconductor.
AVAILABILITY AND IMPLEMENTATION: https://github.com/Simon-Coetzee/MotifBreakR, www.bioconductor.org.
CONTACT: dennis.hazelett@cshs.org.

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Grants

  1. R01 CA136924/NCI NIH HHS
  2. U01CA184826/NCI NIH HHS
  3. R01CA136924/NCI NIH HHS
  4. R01 CA190182/NCI NIH HHS
  5. R01CA190182/NCI NIH HHS
  6. U01 CA184826/NCI NIH HHS

MeSH Term

Algorithms
Animals
Binding Sites
Genomics
Humans
Mice
Mutation
Polymorphism, Single Nucleotide
Regulatory Elements, Transcriptional
Regulatory Sequences, Nucleic Acid
Sequence Analysis, DNA
Software
Transcription Factors

Chemicals

Transcription Factors

Word Cloud

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