Runs of transition. A total of 15,829,349 states had been identified to satisfy the power cutoff situation G(S)Gmin zh. At superhelical density s {0:07 this analysis took 2.25 minutes to run on the same machine. In this case there were averages of 44.2 Z-form base pairs and 3.9 runs of transition, and 1,047,067,293 states satisfied the energy cutoff condition. We find that the execution time is almost constant at superhelix densities jsjv0:05, and increases quadratically thereafter. The algorithm scales approximately linearly with sequence length. These performance characteristics suggest that the SIBZ analysis of the complete human genome at s {0:055 would take approximately 12 hours on a 100 CPU cluster of slightly faster (viz. Opteron) processors, if the sequence was partitioned into 5 kb segments that were analyzed individually. A similar analysis at s {0:07 would take approximately ten days. We note that there is substantial variability of execution speed depending on the attributes of the sequence being analyzed. SIBZ executes quickly on sequences that have one dominant Zsusceptible site. However, the analysis under identical conditions of a sequence in which there is substantial competition among numerous sites can take up to ten times longer.Other MethodsZ-Hunt was the first algorithm to predict Z-forming regions in DNA sequences based on energy considerations [35]. Although the original version only accepted sequences GNE140 racemate custom synthesis shorter than 1 Mbp, recently Z-Hunt II was implemented to identify potential Zforming regions in longer sequences, and specifically in the humanStress Induced B-Z Transitionsgenome [48]. In both versions of Z-Hunt a series of fixed length segments within a sequence are separately tested for their Zforming potential. This is done by inserting the segment in a standard background, which is a circular plasmid in which the inserted segment is the only site that can undergo a structural transition. Z-Hunt then calculates the propensity of the segment to form Z-DNA under these standardized conditions. A Z-score is assigned to each segment by comparing its ability to adopt Z-form with those of a collection of randomly generated sequences. Unlike in SIBZ where we assign a superhelical density, Z-Hunt bases its Zscore on the superhelix density at which onset of transition occurs in this standard background. So there is no direct relationship between a segment’s Z-score and its probability of transition at a specific superhelix density. Z-Hunt also provides no information about the competition among multiple Z-susceptible regions within the sequence. Z-Catcher uses a different approach to identify sites with Zforming potential [47]. This algorithm includes a superhelix density as one of its inputs [47]. It treats the B-Z transition as a simple binary, “on-off” process at a single site. A critical threshold superhelix density is calculated for each individual segment of the sequence being analyzed, PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/20150669 at which the energy required by the B-Z transition of that site exactly balances the stress energy released from this transition when it occurs alone in a standard background. If the input superhelix density is more negative than this critical s, the region is said to be Z-forming. Its output is a list of predicted Z-forming sites, with no weight or probability assigned to them. Z-Catcher analyzes individual sites as though complete transition at that site is the only possibility. No consideration is given to how each site competes with.
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