Computational tools for understanding sequence variability in recombination signals

The recombination signals (RSs) that guide V(D)J rearrangement are remarkably diverse. In mice, fewer than 16% of RSs carry consensus heptamers and nonamers and none also contain a consensus spacer sequence. It is increasingly clear that this variability regulates recombination: genetic variability in RSs may help enforce allelic exclusion, determine the general nature of antigen receptor repertoires, and mitigate autoreactivity in B lymphocytes. The great diversity of RSs has largely precluded, however, empiric determinations of how RS sequence affects recombination. For example, 4³⁹ unique 23-RSs are possible or approximately 3 × 10²³ sequences; some 7 × 10¹³ unique 23-RSs can be produced just by changes in the spacer. In contrast, the recombination activities of only 100 or so RSs have been measured, and it is unlikely that the activities of even a tiny fraction of extant RSs can be determined. We have addressed the problem of how sequence determines the efficiency of RS templates by generating computational models that describe the correlation structure of mouse RSs. These models successfully predict RS activity and identify functional, cryptic RSs (cRSs). These models permit studies to identify RSs and cRSs for empiric study and constitute a tool useful for understanding RS structure and function.