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Review
. 2003 Apr;111(8):1099-106.
doi: 10.1172/JCI17842.

Comparative genomic tools and databases: providing insights into the human genome

Len A Pennacchio  1 , Edward M Rubin
Affiliations
Review

Comparative genomic tools and databases: providing insights into the human genome

Len A Pennacchio et al. J Clin Invest. 2003 Apr.
No abstract available

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Figures

Figure 1
Figure 1
Comparison of local- and global-alignment algorithm strategies. Top: Global alignments are generated when two DNA sequences (A and B) are compared and an optimal similarity score is determined over the entire length of the two sequences. Bottom: Local alignments are produced when two DNA sequences (A and B) are compared and optimal similarity scores are determined over numerous subregions along the length of the two sequences. The local-alignment algorithm works by first finding very short common segments between the input sequences (A and B), and then expanding out the matching regions as far as possible.
Figure 2
Figure 2
Human/chimpanzee and human/mouse ApoE genomic-sequence comparisons. (a) PipMaker analysis with human sequence depicted on the horizontal axis and percentage similarity to chimpanzee on the vertical axis. Exons are indicated by black boxes and repetitive elements by triangles above the plot. Each PIP horizontal bar indicates regions of similarity based on the percent identity of each gap-free segment in the alignment. Once a gap (insertion or deletion) is found within the alignment, a new bar is created to display the adjacent correspondent gap-free segment. (b) VISTA analysis with human sequence shown on the x axis and percentage similarity to chimpanzee on the y axis. The graphical plot is based on sliding-window analysis of the underlying genomic alignment. In this illustration, a 100-bp window is used that slides at 40-bp nucleotide increments. Blue and pink shading indicate conserved coding and noncoding DNA, respectively. Green and yellow bars immediately above the VISTA plot correspond to various repetitive DNA elements. (c) PipMaker analysis with human sequence depicted on the horizontal axis and percentage similarity to mouse on the vertical axis. (d) VISTA analysis with human sequence shown on the x axis and percentage similarity to mouse on the y axis. Two experimentally defined enhancers are indicated on each of the plots (–6).
Figure 3
Figure 3
UCSC Genome Browser output for human/mouse sequence comparison of the ApoE gene (22). Human sequence is depicted on the x axis, and the numbering corresponds to the position of human chromosome 19 based on the UCSC June 2002 freeze (22). Note the different scoring system in contrast to percent identity, with peaks representing L-scores that take into account the context of the level of conservation. Conservation in relatively nonconserved regions receives higher L-scores than similar conservation in relatively highly conserved regions. As a second display of conservation, the “best mouse” track uses blocks whose length and shading represent the conservation.
Figure 4
Figure 4
VISTA Genome Browser output for human/mouse sequence comparison of the ApoE gene (1). (a) The same genomic interval found in Figure 3 was examined. (b) A twofold “zoom out” was performed on the interval found in a, allowing the neighboring ApoE genes to be determined. Colored bars immediately above the VISTA plot correspond to various repetitive DNA elements.
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References

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