build maf from axt files
for i in /Users/bcf/git/brant/consprimers/data/conserved/input/axt/*.axt;
do axtToMaf $i \
/Users/bcf/git/brant/consprimers/data/conserved/input/taeGut1.sizes \
/Users/bcf/git/brant/consprimers/data/conserved/input/galGal3.sizes \
/Users/bcf/git/brant/consprimers/data/conserved/input/$i.maf \
-tPrefix=taeGut1. -qPrefix=galGal3.;
done
scanned the alignment of taeGut1 and galGal3 with:
# scanning parameters were inbuilt in this version
python summary.py
determined locations between conserved areas (and duplicates)
python cons_distance_scanner.py
determined number of regions 200-5000 bp in birdcons.distance table
select * from distance where \
(close_target_distance >= 200 and close_target_distance <= 5000) and \
(close_query_distance >= 200 and close_query_distance <= 5000) and \
close_target = close_query;
designed primers for these loci, storing them in the primers table
python cons_primer_designer.py --configuration=db.conf
used relatively specific primer design criteria (Tm ~ 65; len > 19) in hopes of generating pretty specific primers.
this created many primers - roughly half of regions in the table:
select count(*) from distance where (close_target_distance >= 200 and \
close_target_distance <= 5000) and (close_query_distance >= 200 \
and close_query_distance <= 5000) and close_target = close_query;
+----------+
| count(*) |
+----------+
| 15851 |
+----------+
select count(*) from primers where primer = 0;
+----------+
| count(*) |
+----------+
| 8032 |
+----------+
updated distance table with amplicon averages
alter table distance add column average_amplicon double;
update distance set average_amplicon = \
(close_target_distance+close_query_distance)/2;
and amplicon confidence intervals
alter table distance add column average_amplicon_ci double;
update distance set average_amplicon_ci = \
round(1.96*(sqrt((pow(close_target_distance - average_amplicon,2) + \
pow(close_query_distance - average_amplicon,2))/2)/sqrt(2)),2);
map out primer positions in gallus and zfinch
python make_primer_bed.py --configuration=db.conf \
--output=galGal3.primers.200-5000.bed --chicken
python make_primer_bed.py --configuration=db.conf \
--output=taeGut1.primers.200-5000.bed
make a BED file for the cons regions
python make_cons_bed.py --conf=db.conf --cons-min=200 --cons-max=5000
get the amplicons created from each of the primers
python make_amplicons_from_primers.py
--input=data/conserved/output/galGal3.primers.200-5000.bed
--output=data/conserved/output/galGal3.amplicons.200-5000.bed