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Analyzing genomic data: understanding the genome

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Abstract The effort that led to the sequencing of the human genome ushered life sciences into a new era. One of the largest international scientific endeavors of the time delivered the genetic makeup of our species to the research community. Further international consortia (HapMap, ENCODE, International Human Epigenome Consortium, 1000 Genomes Project) are shaping our understanding of the genomic landscape. Current high‐throughput sequencing technologies can deliver a whole human genome in a matter of days. The subsequent data avalanche is stored in public repositories (including individual genomes). Such developments emphasize the need for data mining approaches. In this contribution, several tools and strategies to access these data are presented. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Data Mining Software Tools

From gels to capillary‐based sequencing instruments moving to massive parallel sequencing, there has been a dramatic increase in the amount of sequence produced.

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Distributed Annotation System Registry showing status of different servers.

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Graphical representation of a Taverna workflow centered around BioMart to identify conserved regulatory elements.

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BioMart Central Portal (December 2011).

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Focus of different databases, data from the European Network of Excellence, ENFIN (2009).

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Distributed model with different data resources sharing a framework to access central repositories and work together in workflows.

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An array of tools are available for variant detection (green), remapping (blue), and transcriptomics analysis (purple), and several resources keep track of this fast moving field. This image has been generated with Wordle from tools listed in SEQanswers' Wiki51 and a virtual issue of Bioinformatics.52 (For more details and references see Refs 51 and 52 and Table 3 in Ref 53).

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Application Areas > Data Mining Software Tools
Algorithmic Development > Biological Data Mining

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