datasets. Learning how to coordinate the different kinds of data collected and analyzed by scientists in so many disciplines is a major challenge, both conceptually and bioinformatically.


Currently, metagenomics is heavily biased toward sequencing and its associated computational analyses, and pioneering functional analyses. While the current distribution of effort is appropriate for this initial exploratory phase, it will not be sufficient for the next phase of metagenomics, when more value will be desired from the sequence and its metadata. It is important to plan early for the mid- and longer-range development of the field so that both the researchers and agencies plan for and invest in new approaches and capabilities. Many of these downstream uses are difficult to predict in advance but the infrastructure for their encouragement and support can be established. It is important that the field not be slowed by overemphasis on massive sequencing without at least equivalent if not greater advances in other metagenomics approaches.

A 10-year trajectory of possible resource distribution would initially show a shift from emphasis on sequencing toward more computational development and analysis. Later, greater emphasis could be placed on such approaches as proteomics and transcriptomics, more in-depth analyses of metabolic and synthetic pathways (chemical bioinformatics, for example, could focus on detecting the genetic machinery for producing small biomolecules like signaling chemicals), and comprehensive knowledge building. The committee does not intend to deemphasize the importance of adequate sequencing resources, but to point out that the field will need to update its vision, tools, and goals continually, so that resources are appropriately divided between generating sequence and all of the other analytical and experimental approaches that comprise metagenomics.

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