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?
-
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.
-
Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?
Liquid biopsies can detect tumor-derived material circulating in the bloodstream, providing a way to study treatment response, detect emerging resistance, and discover new biomarkers. Circulating...10-14-2026, 09:55 AM -
Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.
We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing...
Comment