Thank you ATpoint for your reply.

I'll try to describe the issue in more detail.

1. I've mapped my SE reads to the ref genome using bowtie2 --end-to-end -k 6.
2. I've sorted and indexed the reads using samtools
3. I've marked duplicates using Picard
4. I've called peaks using MACS2 (with the --broad function)

Below is the code used for DiffBind

>samples<-read.csv("samples.csv")
>samples
>flu <- dba(sampleSheet=samples)
>flu

12 Samples, 12455 sites in matrix (15815 total):
ID Condition Replicate Intervals
1   S1   control         1      8771
2   S2   control         2      8937
3   S3   control         3      9608
4   S4   control         4      6338
5   S5   flu_low         1      9127
6   S6   flu_low         2      7849
7   S7   flu_low         3      9081
8   S8   flu_low         4      7916
9   S9  flu_high         1     10432
10 S10  flu_high         2      9284
11 S11  flu_high         3      8311
12 S12  flu_high         4     11119

>flu_counts <- dba.count(flu)
>flu_counts

12 Samples, 12374 sites in matrix:
ID Condition Replicate     Reads FRiP
1   S1   control         1 137864693 0.06
2   S2   control         2 111003187 0.09
3   S3   control         3 142181269 0.07
4   S4   control         4 110355671 0.07
5   S5   flu_low         1 166460540 0.05
6   S6   flu_low         2 128119574 0.06
7   S7   flu_low         3 164164658 0.05
8   S8   flu_low         4 133877017 0.06
9   S9  flu_high         1 169378646 0.05
10 S10  flu_high         2 162547426 0.07
11 S11  flu_high         3 173471350 0.05
12 S12  flu_high         4 196066616 0.05

Now the weird thing is that it seems there are, for instance in sample S1, more than 130M reads. This is not possible as for this sample there mere 60M raw reads! Do you think that running bowtie2 with -k argument can be a problem? As setting -k 6 the sam file will retain reads mapping to a maximum of 6 different locations.

Thank you

Best

Marianna