diff options
| -rw-r--r-- | README.md | 6 | ||||
| -rwxr-xr-x | SelectiveGenomeAmplification | 8 | ||||
| -rwxr-xr-x | SelectiveGenomeAmplificationUI | 4 | ||||
| -rwxr-xr-x | src/score_mers.py | 5 |
4 files changed, 17 insertions, 6 deletions
@@ -48,7 +48,8 @@ ignore\_mers | Not Enabled | mers to explicitly ignore, space seperated ex. igno foreground | Not Enabled | path of foreground file background | Not Enabled | path of background file max\_consecutive\_binding | 4 | The maxium number of consecutive binding nucleotides in homodimer and heterodimers -fg\_weight | 0 | How much extra weight to give higher frequency mers in fg. see "equations" +fg\_weight | 0 | How much extra weight to give higher frequency mers in fg. see "equations" (between 0 and 1) +primer\_weight | 0 | How much extra weight to give to sets with a higher number of priemrs. (between 0 and 1) ## Equations @@ -66,3 +67,6 @@ environmental variable if you wish to do so. hit = abundance of primer X (ex. 'ATGTA') in background (foreground hit / background hit) * (foreground hit ^ fg_weight) + + +### Score function diff --git a/SelectiveGenomeAmplification b/SelectiveGenomeAmplification index b875fe0..ad8d4a5 100755 --- a/SelectiveGenomeAmplification +++ b/SelectiveGenomeAmplification @@ -56,9 +56,12 @@ fi # maximum number of mers that are consecutively binding : ${max_consecutive_binding=4} -# fg_weight, now much to weight to give the higher bindnig primers +# fg_weight, how much to weight to give the higher bindnig primers : ${fg_weight=0} +# primer_weight, how much weight to give to sets with a higher number of priemrs. (between 0 and 1) +: ${primer_weight=0} + export ignore_mers export min_mer_range export max_mer_range @@ -72,6 +75,7 @@ export max_melting_temp export min_melting_temp export fg_weight +export primer_weight # Make our output directory @@ -131,7 +135,7 @@ if [ "$ignore_mers" ]; then fi echo "outputing current run parameters" -for var in ignore_mers min_mer_range max_check cpus max_consecutive_binding max_mer_range max_select min_mer_count max_mer_distance max_melting_temp min_melting_temp foreground background; do +for var in ignore_mers min_mer_range max_mer_range max_check cpus max_consecutive_binding max_select min_foreground_binding_average max_mer_distance min_melting_temp max_melting_temp foreground background; do echo $var "${!var}" >> $output_directory/$current_run/parameters done; diff --git a/SelectiveGenomeAmplificationUI b/SelectiveGenomeAmplificationUI index 47a488d..810d0ca 100755 --- a/SelectiveGenomeAmplificationUI +++ b/SelectiveGenomeAmplificationUI @@ -50,8 +50,8 @@ questions = [ { 'question': 'maximum melting temperature for mers?', 'default_str': '30c', 'variable': 'max_melting_temp' }, { 'question': 'minimum melting temperature for mers?', 'default_str': '0c', 'variable': 'min_melting_temp' }, { 'question': 'maximum number of consecutively binding mers in hetero and homodimers?', 'default_str': '4', 'variable': 'max_consecutive_binding' }, - { 'question': 'what extra weight you want for highgly binding primers?', 'default_str': '0', 'variable': 'fg_weight' } - + { 'question': 'what extra weight do you want for highgly binding primers? (0-1)', 'default_str': '0', 'variable': 'fg_weight' }, + { 'question': 'what extra weight do you want for sets with a higher number of primers? (0-1)', 'default_str': '0', 'variable': 'primer_weight'} ] def bool_ask(ask_string, default): diff --git a/src/score_mers.py b/src/score_mers.py index 7b6da7a..07d2ff9 100755 --- a/src/score_mers.py +++ b/src/score_mers.py @@ -34,6 +34,9 @@ max_check = int(os.environ.get("max_check", 35)) max_mer_distance = int(os.environ.get("max_mer_distance", 5000)) max_consecutive_binding = int(os.environ.get("max_consecutive_binding", 4)) + +primer_weight = float(os.environ.get("primer_weight", 0)) + def get_max_consecutive_binding(mer1, mer2): ''' Return the maximum number of consecutively binding mers @@ -262,7 +265,7 @@ def score(combination): fg_std_dist = np.std(fg_dist) # this is our equation - mer_score = (nb_primers * fg_mean_dist * fg_std_dist) / bg_ratio + mer_score = (nb_primers**primer_weight) * (fg_mean_dist * fg_std_dist) / bg_ratio return [combination, mer_score, fg_mean_dist, fg_std_dist, bg_ratio] |