AlanFung:LabNotes/2015/2015-7-14
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Swift Methyl-Seq DNA Library Prep & Capturing
- We are interested in the performance of the Swift Bioscience Methyl-Seq DNA library kit
- Since we have WGB libraries made using Kapa we can compare swift against it.
- 2nd Experiment was done by Noi (Kapa Hyper Lib. Prep + Seqcap) on Noi/NOTES/2014-12-25
- 2nd Data analysis was done by Dinh and Dr. Zhang Dinh/Dinh 2015/NOTES/2015-1-7 Kun:LabNotes/MONOD/2015-1-9
- 3rd Experiment was done by me (Kapa Hyper Lib. Prep + Seqcap-optimized) on AlanFung:LabNotes/2015/2015-1-15
- 3rd Data analysis was done by Dr. Zhang Kun:LabNotes/MONOD/2015-3-4
Summary
Experiment | Person | Capture Date | Lib Prep | Capture | Conclusion |
1 | Noi | 141104 | Tang | SeqCap | lower complexity and higher clonal rates due to Tang et assumption the DNA fragment ends |
2 | Noi | 141225 | Kapa Hyper | SeqCap | lower specificity and enrichment due to capture condition |
3 | Alan | 150115 | Kapa Hyper | SeqCap | Mapping rates, library complexity and capture specificity is high |
4 | Alan | 150714 | Swift | ? | ? |
Samples
- We only have 12 reactions from the swift methyl-seq kit and Dana needs to use it for her project so I can work on 4 samples.
- I am going to pick 1x tumor 2x plasma sample and 1x NC plasma
- I will pick the ones that have relatively low % trimmed (percentage of adapters being trimmed) and high % mapped rate from 2nd capture.
- I will pick the ones with high % on target and low % clonal from 3rd capture.
SAMPLE ID | Total PE reads | Total reads | Total reads after trimming | Total mapped reads | %trimmed | %mapped | |||
6T-2_map | 7,489,616 | 14,979,232 | 14,638,550 | 12,820,323 | 2% | 88% | |||
Sample | N_mapped_reads | N_non-clonal_reads | N_on-target_reads | N_non-clonal_on-target_reads | N_on-target_haplotypes | N_target_coverred | Pct_on-target | Pct_clonal | Enrichment_factor |
6P-3 | 28,124,349 | 27,174,711 | 20,126,384 | 19,455,168 | 7,299,019 | 48,605 | 71.60% | 3.40% | 254 |
PCP-3 | 9,225,527 | 9,067,481 | 6,226,370 | 6,133,490 | 1,984,048 | 47,803 | 67.50% | 1.70% | 210 |
NC-30 | 15,359,075 | 13,957,906 | 9,350,399 | 8,223,219 | 3,641,546 | 45,664 | 60.90% | 9.10% | 157 |