Sequencing efforts have provided us with detailed information about the genetic content of various organisms across all three domains of life. As genomic sciences continue to evolve we can anticipate that multiple dimensions in genome annotation will emerge as we characterize genome-scale functions. The expansion in dimensionality of genome annotation allows for the formalization of our knowledge about genomes, their attributes and functions.
A one-dimensional annotation provides information on the location of genes and any information on the known or putative function of gene products. A two-dimensional annotation uses information about the functional networks in a cell to specify the cellular components and their interactions.
Two-dimensional annotations of many cellular processes can be represented as biochemical transformations. These two-dimensional annotations serve as biochemically and genetically structured databases through which data can be analysed and from which computational models can be generated.
Manual, automated and iterative methods for generating two-dimensional reconstructions of cellular metabolism from one-dimensional annotations have been developed and can be applied to studying other cellular processes, such as signalling, transcription and translation.
Higher dimensions in genome annotation are beginning to appear, where the three-dimensional structural arrangement of genomes within the confines of a cell are accounted for and where changes in genome sequence over evolutionary time are tracked.
We currently have the methods and information needed to generate one-dimensional and two-dimensional annotations; as we learn more about the structural arrangement of genomes within the cell and how these genomes adaptively evolve we can begin to generate higher levels of annotation.
Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.
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The authors would like to thank T. Allen and S. Fong for useful comments on the manuscript. This work was funded in part by the US National Institutes of Health. B.O.P. serves on the scientific advisory board of Genomatica, Inc.
Jennifer L. Reed, Iman Famili, Ines Thiele & Bernhard O. Palsson Towards multidimensional genome annotation. Nature Reviews Genetics 7, 130–141 (2006); doi:10.1038/nrg1769 Bernhard O. Palsson serves on the Scientific Advisory Board of Genomatica, Inc.
- One-dimensional annotation
Details the position of genes within the genome and describes the cellullar function of gene products.
- Two-dimensional annotation
Accounts for the cellular components that are identified in a one-dimensional annotation as well as their chemical and physical interactions.
- Network reconstruction
A description of the network components and their interactions.
- Three-dimensional annotation
Details the spatial location of genes (rather than the gene products) within the cell as a result of genome packaging.
- Four-dimensional annotation
Details changes in genome sequence that result from adaptive evolution.
- Metabolite connectivity
The number of reactions a given metabolite participates in.
- Systemic reactions
Mathematically derived reactions which represent overall or dominant types of chemical transformation in a given network.
Proteins encoded by different genes that catalyse the same reaction.
- Computational model
A set of equations that mathematically represents network reconstruction and is used to predict the behaviour of a system.
- Precursor metabolites
Metabolites that are generated by catabolic pathways and used by anabolic pathways to generate biomass components.
- Biomass components
The macromolecules (proteins, carbohydrates, lipids and nucleotides), vitamins, cofactors, metals and minerals that make up a cell.
- Boolean rules
Logic statements that use Boolean operators (and, or, not) to evaluate the on/off state of a variable.
- P/O ratio
The number of ATP molecules (P) that are formed per oxygen atom (O) consumed during respiration.
- Network gap
One or more reaction that is missing from the network reconstruction owing to the lack of direct genetic or biochemical evidence.
- Blocked reactions
Reactions that, at steady state, can have no net flux (reactions that involve dead-end metabolites are blocked reactions).
- Pathway holes
Missing reactions from defined metabolic pathways such as glycolysis and amino-acid biosynthesis.
- Dead-end metabolites
A metabolite that is either only produced or only consumed by the metabolic network (pathway holes, network gaps and blocked reactions involve dead-end metabolites).
- Flux-coupling analysis
A computational method that determines how fluxes through a pair of reactions are related.
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Reed, J., Famili, I., Thiele, I. et al. Towards multidimensional genome annotation. Nat Rev Genet 7, 130–141 (2006). https://doi.org/10.1038/nrg1769
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