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In a recent experiment we sequenced the same libraries on a MiSeq (random FC) and NovaSeq (patterend FC) with similar number of reads but with a 10x higher number of duplicate reads on the NovaSeq. So, I'm wondering if there is a way to deal with optical duplicates (OD) on Illumina patterned flow cells when creating the UMI groups?
If I understand the documentation correctly, all reads with the same coordinates and UMI sequence are grouped regardless if they are PCR or optical duplicates and later used to create a consensus call. In the attached example, there is a tag family with 14 read pairs. However, looking at their location of the flow cell, there are several copies that are within a pixel distance of 2500 which is considered to be ODs on a patterned FC. Some OD cluster have 3-4 copies while other members of the same UMI family have no OD. This will skew the representation of PCR/library prep errors and also the overall size of the UMI family is overestimated (accounting for OD there are only 7 unique copies of the same UMI left). Or do I need to remove optical duplicates first (e.g with picard) and then create my UMI consensus?
Thank you very much for your comments.
The text was updated successfully, but these errors were encountered:
Hi.
In a recent experiment we sequenced the same libraries on a MiSeq (random FC) and NovaSeq (patterend FC) with similar number of reads but with a 10x higher number of duplicate reads on the NovaSeq. So, I'm wondering if there is a way to deal with optical duplicates (OD) on Illumina patterned flow cells when creating the UMI groups?
If I understand the documentation correctly, all reads with the same coordinates and UMI sequence are grouped regardless if they are PCR or optical duplicates and later used to create a consensus call. In the attached example, there is a tag family with 14 read pairs. However, looking at their location of the flow cell, there are several copies that are within a pixel distance of 2500 which is considered to be ODs on a patterned FC. Some OD cluster have 3-4 copies while other members of the same UMI family have no OD. This will skew the representation of PCR/library prep errors and also the overall size of the UMI family is overestimated (accounting for OD there are only 7 unique copies of the same UMI left). Or do I need to remove optical duplicates first (e.g with picard) and then create my UMI consensus?
Thank you very much for your comments.
The text was updated successfully, but these errors were encountered: