Genome-Scale Analysis of Data from High-Throughput Technologies

Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Few technical advances have excited such a broad spectrum of basic and clinical scientists as high-throughput technologies (microarrays and sequencing). Having learned in training that somewhere in the genome lies the key to just about any phenotype, scientists are fast joining the movement to decrease cost and improve access to these technologies. Generating enormous amounts of high-dimensional data brings certain challenges, and many researchers are turning even further from their training to collaborate with computer scientists and biostatisticians, who are equally excited to analyze these promising datasets. As new and truly interdisciplinary teams are created, we are seeing major advances; the current environment is exciting for all involved. Technology has brought entire scientific fields to the brink of discovery before, and will again, and thus the overall enthusiasm must be tempered by the fact that new technology brings new problems and new artifacts that we have not seen before. We can circumvent some of these by paying careful attention to experimental design, staying mindful of the complexities of the underlying biology, and by soliciting assistance from analysts versed in high-dimensional data.


Tiling Array Exon Array Matrix Attachment Region Short Read Sequencing Molecular Biology Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreUSA

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