Cloud computing for genomic data analysis and collaboration

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Abstract

<p class="first" id="P2">DNA sequencing made huge strides in the last decade. Studies based on large sequencing datasets appear frequently, and public archives for raw sequencing data have been doubling in size every 18 months. Meanwhile, commercial and academic cloud computing have matured, leading to more providers, greater total capacity, and a larger variety of services. Here we describe how cloud computing is used for large-scale genomics collaborations and research and argue how cloud computing will likely be a basic underpinning for future large-scale genomics collaborations and for efforts to re-analyze archived data, including privacy-protected data. </p>

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Most cited references 48

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James Brown, Nathan Boley, Robert Eisman … (2014)

Animal transcriptomes are dynamic, each cell type, tissue and organ system expressing an ensemble of transcript isoforms that give rise to substantial diversity. We identified new genes, transcripts, and proteins using poly(A)+ RNA sequence from Drosophila melanogaster cultured cell lines, dissected organ systems, and environmental perturbations. We found a small set of mostly neural-specific genes has the potential to encode thousands of transcripts each through extensive alternative promoter usage and RNA splicing. The magnitudes of splicing changes are larger between tissues than between developmental stages, and most sex-specific splicing is gonad-specific. Gonads express hundreds of previously unknown coding and long noncoding RNAs (lncRNAs) some of which are antisense to protein-coding genes and produce short regulatory RNAs. Furthermore, previously identified pervasive intergenic transcription occurs primarily within newly identified introns. The fly transcriptome is substantially more complex than previously recognized arising from combinatorial usage of promoters, splice sites, and polyadenylation sites.

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Daniel Rhodes, Jianjun Yu, K. Shanker … (2004)

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.