Paircomp
Several different classes of algorithms are available for comparing two genomic sequences. Windowed comparisons do an exhaustive comparison of two sequences with a fixed-width window, and record strict (ungapped) sequence identity within that window [2, 12]. Local alignment algorithms such as BLAST search for common "words" of DNA in a pair of sequences and build a gapped alignment around these words [13]. These gapped alignments are often scored by overall length, so that e.g. a 500 bp match at 90% is ranked higher than a 200 bp match at 90%. Global alignment algorithms such as AVID [14] and LAGAN [15] seek to build a start-to-end gapped alignment of syntenic genomic regions. Windowed comparisons and local alignment algorithms usually search for matches in both forward and reverse complement directions, while global alignment algorithms typically try to build an alignment without inversions. Implementations of all three strategies for genomic comparisons have been publicly available for some time: Dotter and seqcomp implement windowed comparisons [2, 12]; PipMaker uses a local alignment algorithm, blastz [16, 17]; and Vista relies on a global alignment generated by AVID [18]. All three comparison strategies have been successful at finding regulatory regions [1, 19].
Of the three general classes of algorithms, we chose to use windowed comparisons in our search for cis-regulatory modules. Our decision was based on several criteria. First, these comparisons report matches based solely on strict sequence identity with no gapping, unlike alignment algorithms. This is a good ab initio requirement when comparing sequences in search of cis-regulatory modules, whose evolution is still poorly understood; in particular, binding sites could be sensitive to indels, which are somewhat elided in gapped alignments. Moreover, we had no a priori expectation for the locations, sizes, or degrees of similarity of conserved regions, necessitating an exhaustive search strategy that did not bias scores based on the length or position of matches. And, finally, from a user-interface perspective the parameters for paircomp – windowsize and threshold – are simple and intuitively linked to the results. Our success with this basic approach means that we have not needed to move to alternative algorithms.
Paircomp is a standalone program that executes windowed comparisons (see Methods). It searches for matches in both the forward and reverse complement directions. Paircomp runs within Cartwheel; the results are stored in a database and communicated to FRII.
Cartwheel
Cartwheel is a Web site through which analyses are executed and from which analyses are loaded into FamilyRelationsII. It provides an easy-to-use interface through which to establish a set of analyses on a pair of sequences. Cartwheel also allows the annotation of sequences with a variety of features; features can be uploaded to Cartwheel in the standard GFF format. A tutorial for setting up pairwise comparisons is available online [11].
FamilyRelationsII
FamilyRelationsII, or FRII, displays comparisons of BAC-sized genomic sequences of lengths ~100 kb. It is a graphical program that runs directly from a desktop and loads data from the Cartwheel server. From within FRII, users can zoom in to look more closely at features, alter scoring thresholds for comparisons, change the color of features, and turn on or off the display of specific analyses. FRII can also display closeup views of comparisons and alignments against DNA and protein sequence.
Figure 1 shows the main FRII view of a comparison between the otx locus in S. purpuratus and L. variegatus, two sea urchins that diverged approx. 50 mya. The genomic sequences were obtained from BAC libraries as described in [3]. In the case of S. purpuratus, the BAC contains the entire otx coding region; the L. variegatus sequence contains only the 5' region of the gene, and not the final exon.
The comparison shown is a paircomp comparison performed with a 20 bp window at 90% and then displayed at a 95% threshold. The general colinearity of the matches suggests that the majority of the similar regions are conserved with respect to size, orientation, and relative distance from the exons. This colinearity is typical of conserved features in our comparisons. The diagonal lines crossing the comparison often identify low complexity regions such as simple sequence repeats present throughout both genomic regions. This pairwise mapping view is one of the two large-scale views in FRII; the other large-scale view is a dot-plot view, shown in Figure 2.
Figure 2 shows a dot-plot view of an expanded region of the comparison, centered on the first exon of the α-otx transcript. In addition to the exon itself, there is patchy conservation throughout the region; again, this is typical of many comparisons. This view also shows that all of the elements are collinear on scales of ~10 kb.
In both the dot-plot and pairwise mapping view, multiple comparisons done with different parameters can be displayed in different colors. The threshold for the matches shown can be adjusted until the desired view is obtained, and sequence can be exported from any of the views via a pop-up menu.
Once a threshold is chosen, the user can expand the view of a particular region. Figure 3 shows a closeup view of the region outlined in blue in Figure 2. The sequence shown in Figure 3 is a small patch of conservation upstream of the first exon, displayed at a 19/20 threshold. Here the user scans along the sequence and visually compares both the boundaries of the matches and the complexity of the sequence. Sequences are directly exported to other applications via the "paste" buffer.
FRII also performs searches for motifs using the IUPAC notation in which e.g. W represents A or T. This feature allows users to search for matches to known "consensus" binding sites for transcription factors. Searches are either stored on the Cartwheel server and displayed as individual features on FRII views, or executed directly in FRII. One particularly convenient feature is the ability to ask for motifs that have mismatches in up to 5 positions; this lets users search for weaker matches to known consensi.
Other analyses
FRII displays a variety of analyses. In addition to paircomp windowed comparisons, FRII displays and manipulates Vista-style comparisons, BLAST and blastz comparisons, BLAST database searches, cDNA and protein comparisons, and the results of several different gene finders (genscan, geneid, and hmmgene [20–22]). All of these analyses may be executed directly on the Cartwheel server, excepting only Vista comparisons using the (default) AVID alignment program. The data for Vista comparisons must be uploaded from the results returned by the Vista Web site; however, Vista-style comparisons with the LAGAN global alignment tool are executed directly on Cartwheel.
Discovering and analyzing regulatory regions
We and others have successfully used paircomp, FRII, and Cartwheel to discover a number of regulatory regions (see Introduction). Once we have a pair of genomic regions to compare, the steps we follow are essentially invariant from region to region:
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We set up two to three paircomp analyses at the following windowsizes and thresholds: 10 bp/90%; 20 bp/80%; 50 bp/60%.
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We match the cDNA or protein of interest against both regions, to determine where the coding regions lie.
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We also compare the RefSeq database from NCBI against both regions, to find other genes in the region.
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We load these analyses into FRII and zoom in to a view that includes as much intergenic sequence around the gene as is possible without also including other genes. We then adjust the thresholds on the 20 bp and 50 bp analyses until we obtain a roughly collinear pattern of conserved blocks. Typical values for these thresholds are 80–100% for a 20 bp windowed comparison, and 60–80% for a 50 bp windowed comparison.
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We use the closeup view to extract the conserved blocks, and design PCR primers to isolate all of the contiguous blocks of conserved sequence. We then individually subclone or fuse them into a GFP reporter construct together with a basal promoter. These constructs are then introduced into the sea urchin by microinjection and analyzed for appropriate spatiotemporal expression.
In our experience, we have always been able to identify the relevant enhancer elements using this procedure. A similar procedure in which putatively negative elements are fused with a ubiquitous driver of expression often identifies necessary repressive elements. Also note that one caveat of these procedures is that for some genes, e.g. transcription factors, there are often many regions that appear to do nothing. These may be regulatory regions that affect expression at times or in places that are not under consideration, or could be other genomic features not relevant to gene regulation.
