These are the results of 1000 simulations testing for excess reads in the replication origins.
The way I did it a couple weeks ago was wrong.
We decided to not make simulated replication origins on chrXII between about positions 430000 and 500000.
But when counting reads in origins there are a lot of reads in this chrXII region and there are some replication origins there too, so the counts in origins in the real data was always much higher in the real data than in any of the simulations in all samples both G1 and HU90.
I reran the simulation excluding all reads and origins in the original data in this chrXII region.
Now the simulation results show that the G1 samples have no significant enrichment in origins but the HU90 samples do.
The data below shows for each sample how many reads are in origins (excluding chrXII:430000-500000) and the rank among the 1000 simulations.  You'll see that the G1 samples are in the middle of the 1000 simulations but the HU90 samples are all more enriched in the original data than in any of the 1000 simulations.  The 0 means there were 0 out of the 1000 simulations with more reads in origins than in the original data.  The first example below, a G1 sample, shows 321 of the simulations had more reads in origins than the original data.


==> 1-G1-50pg-dp.stillman05.origin.counts02.txt <==
838513 321

==> 1-G1-50pg-ss.stillman05.origin.counts02.txt <==
783263 476

==> 2-HU90-50pg-dp.stillman05.origin.counts02.txt <==
778247 0

==> 2-HU90-50pg-ss.stillman05.origin.counts02.txt <==
772941 0

==> 3-G1-1ng-dp.stillman05.origin.counts02.txt <==
788480 539

==> 3-G1-1ng-ss.stillman05.origin.counts02.txt <==
717883 647

==> 4-HU90-1ng-dp.stillman05.origin.counts02.txt <==
284816 0

==> 4-HU90-1ng-ss.stillman05.origin.counts02.txt <==
260162 0

==> 5-G1-50pg-dp.stillman05.origin.counts02.txt <==
908390 302

==> 5-G1-50pg-ss.stillman05.origin.counts02.txt <==
895494 445

==> 6-HU90-50pg-dp.stillman05.origin.counts02.txt <==
828661 0

==> 6-HU90-50pg-ss.stillman05.origin.counts02.txt <==
825158 0

==> 7-G1-1ng-dp.stillman05.origin.counts02.txt <==
722673 309

==> 7-G1-1ng-ss.stillman05.origin.counts02.txt <==
684749 419

==> 8-HU90-1ng-dp.stillman05.origin.counts02.txt <==
245031 0

==> 8-HU90-1ng-ss.stillman05.origin.counts02.txt <==
243865 0



The first simulation from a couple weeks ago has the G1 sammples also having more reads in origins than any of the simulations.

==> 1-G1-50pg-dp.stillman05.origin.counts01.txt <==
1242318 0

==> 1-G1-50pg-ss.stillman05.origin.counts01.txt <==
1176720 0

==> 2-HU90-50pg-dp.stillman05.origin.counts01.txt <==
1181359 0

==> 2-HU90-50pg-ss.stillman05.origin.counts01.txt <==
1175452 0

==> 3-G1-1ng-dp.stillman05.origin.counts01.txt <==
1237416 0

==> 3-G1-1ng-ss.stillman05.origin.counts01.txt <==
1148871 0

==> 4-HU90-1ng-dp.stillman05.origin.counts01.txt <==
510278 0

==> 4-HU90-1ng-ss.stillman05.origin.counts01.txt <==
462991 0

==> 5-G1-50pg-dp.stillman05.origin.counts01.txt <==
1334388 0

==> 5-G1-50pg-ss.stillman05.origin.counts01.txt <==
1340924 0

==> 6-HU90-50pg-dp.stillman05.origin.counts01.txt <==
1248483 0

==> 6-HU90-50pg-ss.stillman05.origin.counts01.txt <==
1257728 0

==> 7-G1-1ng-dp.stillman05.origin.counts01.txt <==
1083530 0

==> 7-G1-1ng-ss.stillman05.origin.counts01.txt <==
1035739 0

==> 8-HU90-1ng-dp.stillman05.origin.counts01.txt <==
434702 0

==> 8-HU90-1ng-ss.stillman05.origin.counts01.txt <==
433126 0


I think there is some useful biological signal in the data even though it's hard to see in the bin count plots.