Illumina Sequencing Metrics

[Justin Wong]

I am wondering if anyone can share any Illumina run metrics with us that we should expect with SMART3, e.g. %>Q330, or %PF.  We are also interested in the base distribution, where we are seeing some biases in the %base by cycle plot. 

So far, we have been using a NextSeq500, and are doing clean-up on each individual library, which requires more PCR cycles.  After talking to Illumina, it is possible that some of the results we are seeing could be due to reduced library complexity.

Thanks,

~Justin

[Joe Foley]

It depends on your input material. In the dataset from human reference RNAs (in the paper) across the range from 10 pg to 100 ng input, which we sequenced together with negative controls in one batch on the NextSeq, we got 75% PF, 79% Q30, and the first base composition graph below. There are several reasons for the non-uniform base composition:

1. built-in GGG in positions 6-8
2. high contamination by adapter dimers in the lowest-input samples
3. contamination by poly(A) sequence increasing toward the end
4. spurious G's detected at the end after sequencing through the adapter dimer (on a NextSeq no signal looks the same as G)

Point 3 is exacerbated when the input is degraded RNA, such as the FFPE LCM experiment in the paper (bulk tissue and single cells in the same batch), which had 72% PF, 61% Q30, and the second base composition graph below; those results are typical or maybe even a little better than usual for FFPE LCM libraries.

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