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Spark: A navigational paradigm for genomic data exploration
Biologists possess the detailed knowledge critical for extracting biological insight from genome-wide data resources, and yet they are increasingly faced with nontrivial computational analysis challenges posed by genome-scale methodologies. To lower this computational barrier, particularly in the ea...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483555/ https://www.ncbi.nlm.nih.gov/pubmed/22960372 http://dx.doi.org/10.1101/gr.140665.112 |
Sumario: | Biologists possess the detailed knowledge critical for extracting biological insight from genome-wide data resources, and yet they are increasingly faced with nontrivial computational analysis challenges posed by genome-scale methodologies. To lower this computational barrier, particularly in the early data exploration phases, we have developed an interactive pattern discovery and visualization approach, Spark, designed with epigenomic data in mind. Here we demonstrate Spark's ability to reveal both known and novel epigenetic signatures, including a previously unappreciated binding association between the YY1 transcription factor and the corepressor CTBP2 in human embryonic stem cells. |
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