Design FISH Probes - Outputs

The results are in the form of two tab-delimited text files: one with every possible single probe, and a second with the top 100 scoring dual probes.

The tables are sorted with the most specific probes at the top, where each row represents probe(s) meeting all of the input constraints. The most critical columns are those showing the probes and their predicted cross-hybridizations with non-target groups (mismatches). Fields in the dual-probes table are named similarly to fields in the single-probes table except that they are demarcated by which probe. For example, the first probe in the set is named "probe one", and the second probe is named "probe two".

The titles and contents of the output columns are as follows:

1. identifier:

   Matches the target group input by the user.

2. start (start_one, start_two):

   Approximate starting position of the probe in a consensus sequence representing the target group. The consensus sequence position does not include gap positions.

3. start_aligned (start_aligned_one, start_aligned_two):

   Approximate positions in the alignment where the probe sequences begin. This starting position includes gap positions in the alignment, whereas the previous column "start" does not.

4. permutations (permutations_one, permutations_two):

   Number of probe permutations required to reach the desired coverage of the target group.

5. score (score_one, score_two, score_set):

   Measures of the specificity of the probes to the target group. A score of zero is the highest specificity, while a more negative score is less specific. The individual probe's specificity scores are calculated by the formula:
   
   specificity score = 0.2*(number of potential non-targets) + Σ(1.2^Δ[FA]_m)
   
   Where Δ[FA]_m is defined as the difference in formamide melt point between the target and non-target, and Σ refers to summing over all potential non-targets with Δ[FA]_m ≥ -20%.
   
   The dual probes' scores are calculated using the minimim Δ[FA]_m of the two individual probes with each potential non-target. For example, if probe one has a Δ[FA]_m of -10% and probe two has a Δ[FA]_m of -5% then only probe two is used to calculate dual probe the specificity score.

6. probe.x (one_probe.x, two_probe.x):

   The sequence of each probe permutation, where “x” ranges from 1 to the total number of permutations. “NA” indicates that the additional probe permutation was not necessary to achieve the desired coverage of the target group.

7. efficiency.x (one_efficiency.x, two_efficiency.x):

   The predicted efficiency of each of the probe permutation(s), which is required to be at least 50% at the input hybridization conditions.

8. FAm.x (one_FAm.x, two_FAm.x):

   Predicted formamide melt point ([FA]_m) of each probe permutation, which is always above the input formamide concentration ([Formamide] = [FA]).

9. coverage.x (one_coverage.x, two_coverage.x):

   Fraction of the target group that perfectly matches each respective probe permutation.

10. mismatches (mismatches_one, mismatches_two, mismatches_set):

    Lists the predicted cross-hybridizations of each probe with non-targets having a Δ[FA]_m ≥ -20%. Each non-target is followed by relevant information, including its group name, predicted hybridiztion efficiency, ΔΔG^°₁, Δ[FA]_m, and finally the probe/non-target alignment. In the case of the dual-probes table, the column "mismatches_set" provides the overlap between the first and second probes' non-target(s).
    
    For example, if E. coli was a non-target in the input file that was predicted to cross hybridize, then "mismatches" might look like:
    
    Escherichia_coli (15%, 5.1kcal/mol, -18%; CACAGTTCCCGAAGGCACC / TGTGCCTTCGGGAACCGTG)
    
    Here the predicted hybridization efficiency of E. coli is 15%, and the predicted Δ[FA]_m is -18%. This prediction was based on a ΔΔG^°₁ calculated as +5.1 kcal/mol.
    
    The probe permutation with the strongest affinity for E. coli is 5'-CAC AGT TCC CGA AGG CAC C-3'. The non-target E. coli sequence is 3'-TGT GCC TTC GGG AAC CGT G-5' based on the input file. This sequence could be used to make a competitor oligonucleotide, such as 5'-CAC GGT TCC CGA AGG CAC A-3'.