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Pattern-based Search of Epigenomic Data Using GeNemo

Compared with the robust text-based search tools for genomic or RNA sequencing data, current methodologies for pattern-based searches of epigenomic and other functional genomic data are very limited. GeNemo is the first online search tool that accomplishes this goal. Users input their functional gen...

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Detalles Bibliográficos
Autores principales: Zheng, Alvin, Cao, Xiaoyi, Zhong, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MyJove Corporation 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752390/
https://www.ncbi.nlm.nih.gov/pubmed/29053670
http://dx.doi.org/10.3791/56136
Descripción
Sumario:Compared with the robust text-based search tools for genomic or RNA sequencing data, current methodologies for pattern-based searches of epigenomic and other functional genomic data are very limited. GeNemo is the first online search tool that accomplishes this goal. Users input their functional genomic data in the Browser Extensible Data (BED), Peaks, and bigWig formats, and may search for data in any of the three formats. Users may specify which types of datasets to search against, choosing from a variety of online datasets, with the Encyclopedia of DNA Elements (ENCODE) representing different epigenomic marks, transcriptional factor binding sites, and chromatin hypersensitivities or accessibilities in specific cell types, and developmental stages or species (mouse or human). GeNemo returns a list of genomic regions with matching patterns to the input data, which may be viewed in the browser as well as downloaded in the BED file format. The upgraded GeNemo has improved graphical display, has more robust interface, and is no longer prone to errors due to changes in the University of California, Santa Cruz (UCSC) genome browser. Troubleshooting steps for common problems are discussed. As the amount of functional genomic data is expanding exponentially, there is a critical need to develop and refine new bioinformatic tools such as GeNemo for data analyses and interpretation.