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Approaches to comparative sequence analysis: towards a functional view of vertebrate genomes

Key Points

  • The identification of evolutionarily constrained sequences is an unbiased approach for finding functional sequences in genomes. However, their identification is strongly affected by upstream analyses.

  • Genomes are sequenced to different levels of finishing, which affects downstream comparative analyses.

  • Before genomic sequences can be aligned, segments of homologous collinearity must be identified. Errors at this stage can have a dramatic effect on the identification of constrained sequences.

  • Base-pair sequence-alignment programs differentially handle the complexities of evolution, such as insertions/deletions and duplications. In addition, new approaches to identify regions of alignment uncertainty can be used.

  • Current approaches that utilize evolutionary sequence constraint focus on regions that are deeply constrained. Newer approaches combined with sequences from more species can now be pursued to identify weakly and/or lineage-specific constrained sequences.

  • The amount of detectable constrained sequence depends on the phylogenetic scope being pursued as well as the resolution and intensity of the desired detectable constraint.

  • Large collaborative projects such as ENCODE are shedding light on the correlation between sequence constraint and sequence function. Additional methods are also available for determining the biological significance of constrained sequences.


The comparison of genomic sequences is now a common approach to identifying and characterizing functional regions in vertebrate genomes. However, for theoretical reasons and because of practical issues, the generation of these data sets is non-trivial and can have many pitfalls. We are currently seeing an explosion of comparative sequence data, the benefits and limitations of which need to be disseminated to the scientific community. This Review provides a critical overview of the different types of sequence data that are available for analysis and of contemporary comparative sequence analysis methods, highlighting both their strengths and limitations. Approaches to determining the biological significance of constrained sequence are also explored.

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Figure 1: Overview of comparative sequence analysis.
Figure 2: Vertebrate genomic sequence data.
Figure 3: Challenges in the reconstruction of homologous collinear regions.
Figure 4: Relationship between sequence length and the quantity of constrained sequence detected.


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Correspondence to Elliott H. Margulies.

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Purifying selection

The evolutionary process of rejecting substitutions in functional DNA, thereby making such sequences more similar when compared among different species.


Homologous sequences in different species that arose from a speciation event.


Sequences that have a common ancestor but might be related by either speciation or duplication events in a genome. Pragmatically, homology is detected by the presence of similarity between two sequences.


Contiguous piece of DNA that is assembled from shorter overlapping sequence reads.

Whole-genome shotgun

The process of shearing the DNA from an entire genome into smaller pieces that are randomly (or 'shotgun') sequenced en masse.

Genome coverage

The total number of bases that are sequenced divided by the genome size. Actual coverage differs depending on the statistical properties of a poisson distribution, which takes into account the fact that reads are sequenced at random.

Segmental duplications

Regions (or segments) of a genome that evolved from a single common ancestor and arose from a duplication event. As such, these sequences within the same genome are paralogous to each other.


A presumably non-functional region of DNA with homology to an actual gene. Pseudogenes typically arise from the reincorporation of an RNA intermediate into the genomic sequence.


The homology between two genomic segments that arose from a duplication event.

Heuristic methods

For large compute problems, the application of workable but not formally correct solutions to help reduce the computational time. In the case of sequence alignment, common heuristic methods include the progressive alignment of closer sequences to each other first before aligning to more distant species, and the use of highly similar anchoring sequences to reduce the search space in the alignment.

Compute farms

Large groups of computers, each on their own only able to analyse a small piece of data (similar to a typical desktop PC), but which, when combined together, provide a powerful resource for analysing computationally intense problems.

Dynamic programming

An algorithm that is efficiently designed to analyse data, usually by elegantly breaking down the computational problem down into smaller, simpler sub-problems.

Suffix tree

An indexing technique to efficiently store all sub-sequences of a string of letters.

Hidden markov model

Mathematical concept that describes a finite set of 'states' and a probabilistic model for transitioning from one state to another.

Ancestral repeat

Relics of transposable elements that inserted before a speciation event and are therefore orthologous and presumed to be non-functional. Therefore, these regions are largely thought to be neutrally evolving.

Eutherian radiation

Approximatey 80 million years ago a large diversity of mammalian species began to evolve. These placental mammals provide a rich resource for identifying constrained sequences.

Four-fold degenerate sites

Third positions of codons for which any base yields the same amino acid.

False-discovery rate

A statistical measure of error, specifically defined as 1 – (true positives / (true + false positives)). Such an error estimate allows for greater fluctuation in the total amount of detected true-positives, as it reflects the proportion of false positives in the resulting data set rather than an absolute value of false positives.

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Margulies, E., Birney, E. Approaches to comparative sequence analysis: towards a functional view of vertebrate genomes. Nat Rev Genet 9, 303–313 (2008).

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