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

run the intersection of these with refseq genes on UCSC (07/21/2010) and keep any results that cross a refseq gene at all. Did this in the genome browser and pasted BED filed into galGal.refseq.200-5000.bed