According to UCSB, this is true for NextSeq. Is this the same for other machines?
If this is true, how do we deal with this problem? Can quality trimming help?
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According to UCSB, this is true for NextSeq. Is this the same for other machines?
If this is true, how do we deal with this problem? Can quality trimming help? -
Is this mostly a theoretical question?
Sometimes we encounter exceptional libraries that will sequences well even with read numbers above spec (rare). In general, over-clustering will lead to drop in Q scores. The drop may be spread across the run or may be more towards the ends of reads (if the run was borderline over-clustered).
Whether Q score trimming can help will depend on the data you have.
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