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In the "Cost per Megabase of DNA Sequence" graph, the data reflect the cost of generating raw, unassembled sequence data; no adjustment was made for data generated using different instruments despite significant differences in the sequence read lengths. In contrast, the "Cost per Genome" graph does take these differences into account since sequence read length influences the ability to generate an assembled genome sequence.

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Mardis E. A decade's perspective on DNA sequencing technology. Nature, 470: 198-203. 2011. []

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To calculate the cost for sequencing a genome, one needs to know the size of that genome and the required 'sequence coverage' (i.e., 'sequence redundancy') to generate a high-quality assembly of the genome given the specific sequencing platform being used. For generating the "Cost per Genome" graph, the assumed genome size was 3,000 Mb (i.e., the size of a human genome). The assumed sequence coverage needed differed among sequencing platforms, depending on the average sequence read length for that platform.

IBM100 - The DNA Transistor - IBM WWW Page

For data since January 2008 (representing data generated using 'second-generation' sequencing platforms), the "Cost per Genome" graph reflects projects involving the 're-sequencing' of the human genome, where an available reference human genome sequence is available to serve as a backbone for downstream data analyses. The required 'sequence coverage' would be greater for sequencing genomes for which no reference genome sequence is available.

Metzker M. Sequencing technologies - the next generation. Nature Genetics, 11: 31-46. 2010. []

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Below are links to projects and scientific rationales that were discussed and at least partially approved over the history (more than 4 years ago) of the program. Please note that not every concept or white paper was approved in full, but the ideas included were considered significant enough to initiate specific projects. NHGRI will do its best to complete every approved project unless significant scientific (ie, technology changes make the project design obsolete), or practical (e.g, no samples available; genome sequenced sooner elsewhere) reasons, until the sequencing program ends.

How to Analyze DNA Microarray Data | HHMI …

For many years, the National Human Genome Research Institute (NHGRI) has tracked the costs associated with DNA sequencing performed at the sequencing centers funded by the Institute. This information has served as an important benchmark for assessing improvements in DNA sequencing technologies and for establishing the DNA sequencing capacity of the NHGRI Genome Sequencing Program (GSP). Here, NHGRI provides an analysis of these data, which gives one view of the remarkable improvements in DNA sequencing technologies and data-production pipelines in recent years.

Stein L. The case for cloud computing in genome informatics. Genome Biology, 11: 207-213. 2010. []

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DNA microarrays, or gene chips, are an important new technology for genomic research. Learn how researchers use computing to analyze and interpret the huge datasets generated by microarray experiments.

Human genome at ten: the sequence explosion. Nature, 464: 670-671. 2010. []

Division of Genome Sciences Archive

DNA microarrays, or gene chips, are an important new technology for genomic research. Learn how researchers use computing to analyze and interpret the huge datasets generated by microarray experiments.

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Molecular biology has focused on the understanding of single genes in great detail. However, many biological processes, including diseases, are affected by the activity of many genes. In this feature, we present the fundamentals of a microarray experiment and the basic principles in analyzing its data.