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Comparative genomics: genome-wide analysis in metazoan eukaryotes

Nature Reviews Genetics volume 4, pages 251–262 (2003)Cite this article

Key Points

  • With the availability of genome sequences for an increasing number of metazoan organisms, the data are now present to carry out large-scale comparative genomic studies.

  • The size of sequenced metazoan genomes, which range from 100 Mb to 3 Gb, makes their comparison a real challenge. Several new approaches have been developed to solve some of the associated problems.

  • The alignment of whole genomes extends the genoome regions that are available to analyse evolutionary mechanisms such as neutral, negative and positive selection, and the history of large insertions and deletions.

  • The potential to compare closely, and even less closely, evolutionarily related metazoans provides new opportunities to identify conserved functional sequences, such as genes or regulatory regions, that are not easily predictable by conventional approaches on a single genome.

  • Hidden Markov model-based programs have been developed mainly in the field of gene prediction to make the most of genome-comparison alignments.

  • The ab initio identification of regulatory regions on a single genome often gives sensitive, but not highly specific, results. Comparative genomic data allow a significant increase in the specificity of such processes.

Abstract

The increasing number of complete and nearly complete metazoan genome sequences provides a significant amount of material for large-scale comparative genomic analysis. Finding new effective methods to analyse such enormous datasets has been the object of intense research. Three main areas in comparative genomics have recently shown important developments: whole-genome alignment, gene prediction and regulatory-region prediction. Each of these areas improves the methods of deciphering long genomic sequences and uncovering what lies hidden in them.

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Figure 1: Evolutionary relationship between metazoans that are sequenced or due for sequencing.
Figure 2: Whole-genome alignments available online.
Figure 3: Schematic description of whole-genome alignment processes.

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Acknowledgements

We thank M. Brudno, W. Miller and L. Pachter for providing their respective manuscripts before publication and W. J. Kent, W. Miller, M. Brudno and L. Bentolila for helpful discussion and comments on the manuscript. We also thank the anonymous reviewers for many helpful suggestions. A.U.-V. is funded by the Wellcome Trust. E.B. and L.E. are funded by the European Molecular Biology Laboratory.

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  1. EnsEMBL Project, Room A2–06, EMBL–European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, Cambridge, UK

    Abel Ureta-Vidal, Laurence Ettwiller & Ewan Birney

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Correspondence to Ewan Birney.

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DATABASES

LocusLink

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FURTHER INFORMATION

Berkeley Genome Pipeline

Comparative analysis of the rat genome

ECR Browser

Ensembl Genome Browser

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UCSC Genome Browser

WormBase

Glossary

RATE MATRIX

Denotes the probability of mutation from one amino acid to another (or from one nucleotide to another) for a given period of evolution. The most well known rate matrices are BLOSUM and PAM.

FUNCTIONAL SEQUENCE

A genomic sequence that provides a function that is under selection and tends to be conserved between species. For example, a protein-coding region or transcription-factor binding site

SEEDS

A short exact, or nearly exact, matching string od characters aligning between two sequences.

PARALOGUES

Sequences, or genes, that have originated from a common ancestral sequence, or gene, by a duplication event.

ORTHOLOGUES

Sequences, or genes, that have originated from a common ancestral sequence, or gene, by a speciation event.

SYNTENIC REGION

A genomic region that is collinear in the order of genes (or of other DNA sequences) in a chromosomal region of two species.

SYNTENIC ANCHORS

Short aligned segments between genome sequences from two species, which are believed to define an orthologous relationship.

DOT PLOT MATRIX

A visualization technique that allows the easy identification of matching nucleotides or amino acids (letters) between two sequences. For example, for two sequences X and Y, each letter has a unique coordinate on the x axis and the y axis respectively. When two letters are the same at a specified coordinate, a dot is plotted in the matrix at that position.

HIDDEN MARKOV MODEL

(HMM). A probabilistic model that is applied to protein- and DNA-sequence pattern recognition. HMMs represent a system as a set of discrete states and as transitions between those states. Each transition has an associated probability. HMMs are valuable because they enable a search or alignment algorithm to be built on firm probabilistic bases, and the parameters (transition probabilities) can be easily trained on a known data set.

DISCRIMINANT FUNCTIONS

Classical statistical pattern-recognition methods that are used to categorize samples into two classes of data.

NEURAL NETWORKS

Mathematical models inspired by analogy with biological neurons to distinguish two or more classes of data.

SCORE MAXIMIZATION PROCESS

Many algorithms attempt to find the solution, under a scoring scheme, that is believed to best reflect reality. For 'simple' models, including hidden Markov models, precise mathematical formulae can be used that will guarantee to find the highest score.

NEUTRAL DRIFT

The process by which a DNA sequence acquires many mutations over time that have no phenotypic effect, and are not acted on by Darwinian selection.

STABILIZING SELECTION

Selection that favours intermediate phenotypes over extreme phenotypes.

GAP PENALTY

Alignment programs deal with insertions and deletions (indels) by introducing a 'gap' in the sequence that contains the deletion. The introduction of gaps and their extension decreases the overall alignment score by a certain value. This value is defined by a gap-opening penalty and a gap-extension penalty, both of which are used as parameters in alignment programs.

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Ureta-Vidal, A., Ettwiller, L. & Birney, E. Comparative genomics: genome-wide analysis in metazoan eukaryotes. Nat Rev Genet 4, 251–262 (2003). https://doi.org/10.1038/nrg1043

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