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Genome annotation: from sequence to biology

Key Points

  • Now that many genome sequences are available, attention is shifting towards developing and improving approaches for genome annotation.

  • Genome annotation can be classified into three levels: the nucleotide, protein and process levels.

  • Gene finding is a chief aspect of nucleotide-level annotation. For complex genomes, the most successful methods use a combination of ab initio gene prediction and sequence comparison with expressed sequence databases and other organisms. Nucleotide-level annotation also allows the integration of genome sequence with other genetic and physical maps of the genome.

  • The principal aim of protein-level annotation is to assign function to the products of the genome. Databases of protein sequences and functional domains and motifs are powerful resources for this type of annotation. Nevertheless, half of the predicted proteins in a new genome sequence tend to have no obvious function.

  • Understanding the function of genes and their products in the context of cellular and organismal physiology is the goal of process-level annotation. One of the obstacles to this level of annotation has been the inconsistency of terms used by different model systems. The Gene Ontology Consortium is helping to solve this problem.

  • There are several approaches to genome annotation: the factory (reliance on automation), museum (manual curation), cottage industry (exemplified by Proteome, Inc.) and party (the Celera Drosophila annotation jamboree).

  • As more scientists come to rely on genome annotation, it will become more important for the scientific community as a whole to contribute to this continuing process.


The genome sequence of an organism is an information resource unlike any that biologists have previously had access to. But the value of the genome is only as good as its annotation. It is the annotation that bridges the gap from the sequence to the biology of the organism. The aim of high-quality annotation is to identify the key features of the genome — in particular, the genes and their products. The tools and resources for annotation are developing rapidly, and the scientific community is becoming increasingly reliant on this information for all aspects of biological research.

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Figure 1
Figure 2: A hidden Markov model explicitly models the probabilities for the transition from one part of a gene to another.
Figure 3: Segmental duplications.
Figure 4: An example of genome annotation.


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I acknowledge R. Durbin, S. Eddy, E. Birney and A. Neuwald for helpful discussions during the preparation of this review. A portion of this work was supported by the National Human Genome Research Institute at the US National Institutes of Health.

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A 'working draft' sequence has come to mean a genomic sequence before it is finished. Working draft sequences contain multiple gaps, unrepresented areas and misassemblies. In addition, the error rate of working draft sequence is higher than the 1 in 10,000 error rate that is standard for finished sequence.


A common file format used for the storage and transfer of sequence data. It contains raw DNA or protein sequence, but no annotation information.


An experimental technique that uses radiation-induced chromosomal breakpoints in somatic-cell hybrids to map the positions of sequence tagged sites (STSs).


Genomic sequencing projects typically sequence the ends of bacterial artificial chromosome and plasmid clones, in addition to shotgun sequencing entire clones. During assembly, the clone end sequences are used to create a scaffold on which the genome sequence is pieced together.


An algorithm specialized to identify a feature of a sequence, such as a possible splice site.


Neural networks are analytical techniques modelled after the (proposed) processes of learning in cognitive systems and the neurological functions of the brain. Neural networks use a data 'training set' to build rules that can make predictions or classifications on data sets.


A type of computer algorithm that uses an explicit set of rules to make decisions.


A type of computer algorithm that represents a system as a set of discrete states and transitions between those states. Each transition has an associated probability. Markov models are 'hidden' when one or more of the states cannot be directly observed.


A class of software that attempts to predict genes from sequence data without the use of prior knowledge about similarities to other genes.


A dispersed, intermediately repetitive DNA sequence found in the human genome in about 300,000 copies. The sequence is about 300 base pairs long. The name Alu comes from the restriction endonuclease (AluI) that cleaves it.


Flowering seed plant.


One of the two principal classes of flowering plant, monocots are characterized by a single cotyledon (primitive leaf) in the embryonic plant. Maize, rice, wheat and other grasses are common monocots.


One of the two principal classes of flowering plant, dicots are characterized by two cotyledons (primitive leaves) in the embryonic plant. Tomatoes, maple trees and mustard are common dicots.


A class of ring-shaped, heat-shock proteins that have a key role in protein folding and protection from stress.


(DAG). A type of hierarchy similar to the outline of a paper in that it has headers, subheaders and sub-subheaders. The main difference from a strict hierarchy is that each topic in a DAG is allowed to have more than one parent topic.


A phenomenon in which the expression of a gene is temporarily inhibited when a double-stranded complementary RNA is introduced into the organism.


This is an in vitro selection method in which very large collections of oligonucleotides can be screened for specific functions.


A type of software distribution in which the source code (the human-readable instructions) are made freely available. The Linux operating system is open source. The Microsoft Windows and Macintosh operating systems are not.

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Stein, L. Genome annotation: from sequence to biology. Nat Rev Genet 2, 493–503 (2001).

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