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Hi all, I’m pretty new to NGS and library prep. I just completed library prep and WES for a pilot of 12 FFPE DNA samples, and got back Fastq files that varied dramatically in size. I used NEB FFPE DNA prep kit and created normalized pooled libraries immediately after tape station results. However, my tumor samples were overrepresented pretty dramatically, with some samples having fastq files nearly 6 times larger. I’m pretty stuck as to why this happened, as the bioanalyzer curves looked pretty consistent across my samples and I used 11 PCR cycles for the NEB kit.
Specifically, my PT6 and PT5 samples had HUGE fastq file sizes, and normalized those samples by spiking in a larger volume of those samples. Attached are my bioanalyzer curves, all of my "PT" samples had much larger fastq files (ranging from 8-17gb for each R1/R2 complement), compared to fastq file sizes of PM and N tissues being between 2.5-5gb. My analysis pipeline for these samples to get insert size and coverage is still running for the larger fastq files.
What can explain why some of my fastq files are so large, even after normalizing the concentrations from bioanalyzer data just prior to pooling my samples? Is this due to an issue with fragmentation (large fragments on the non-tumor samples causing under sequencing), and if so how can this be mitigated in the future? If this is from daisy-chaining, would this cause unusually low or high sequencing ratio for those samples? Would appreciate any help!
Specifically, my PT6 and PT5 samples had HUGE fastq file sizes, and normalized those samples by spiking in a larger volume of those samples. Attached are my bioanalyzer curves, all of my "PT" samples had much larger fastq files (ranging from 8-17gb for each R1/R2 complement), compared to fastq file sizes of PM and N tissues being between 2.5-5gb. My analysis pipeline for these samples to get insert size and coverage is still running for the larger fastq files.
What can explain why some of my fastq files are so large, even after normalizing the concentrations from bioanalyzer data just prior to pooling my samples? Is this due to an issue with fragmentation (large fragments on the non-tumor samples causing under sequencing), and if so how can this be mitigated in the future? If this is from daisy-chaining, would this cause unusually low or high sequencing ratio for those samples? Would appreciate any help!