Advertisement
Research Article

High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

  • Phaedra Agius,

    Affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America

    X
  • Aaron Arvey,

    Affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America

    X
  • William Chang,

    Affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America

    X
  • William Stafford Noble,

    Affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

    X
  • Christina Leslie mail

    cleslie@cbio.mskcc.org

    Affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America

    X
  • Published: September 09, 2010
  • DOI: 10.1371/journal.pcbi.1000916

About the Authors

Phaedra Agius, Aaron Arvey, William Chang, Christina Leslie
Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
William Stafford Noble
Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America

Corresponding Author

Email: cleslie@cbio.mskcc.org

Competing Interests

The authors have declared that no competing interests exist.

Author Contributions

Wrote the paper: PA AA CL. Developed the kernel approach, carried out the computational experiments on the protein binding microarray data sets, assisted with analysis of the ChIP-seq data sets, and helped to supervise students working on the project : PA. Conceived of project and helped to design computational approach, supervised the research: CL. Carried out the computational experiments on the ChIP-seq data sets: AA. Carried out the yeast conservation analysis and helped to test the kernel approach on protein binding microarray data sets: WC. Helped to supervise the research and edit the manuscript: WSN.