I have 16 samples and would like to multiplexing these samples. There are two ways to do this: multiplexing 16 samples all together and put onto 4 lanes; multiplexing only 4 samples and therefore using 4 lanes. I personally prefer the first choice because there are some controls and biological duplicate in these 16 samples and this way lane to lane difference can be eliminated. But from statistics point of view, I don't know if these two ways of multiplexing would have different statistics power. Can experts here please give me some suggestions about this? Thank you very much!
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multiplexing samples together for best statistical power
I recommend pooling all your samples together and spreading them across all your sequencing lanes. We've written a bit about Replication, Randomization and Multiplexing in our sequencing guide.
- GenohubLast edited by Genohub; 05-07-2014, 05:58 AM. -
Yes I'm facing the same question too. But mine is ChIP-Seq samples, I think ChIP input library need more reads than others IP libraries? In terms of sequencing depth and genome coverage?Originally posted by Genohub View PostComment
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Coverage for Chip-Seq
Yes, ChIP samples typically need higher coverage, take a look at our recommended coverage table. We also have a reference in there to the Encode Guidelines. To calculate the "amount" or units of sequencing you'll need based on coverage or numbers of reads, go to this project page and click on either:
- Minimum number of reads per sample
- Minimum coverage per sample
In your case, click on coverage, enter 100 (for 100x) and your haploid genome size. This will show you how many sequencing units (e.g. lanes) you need to meet your goal. The calculation assumes you want even sequencing coverage across all samples.
- GenohubLast edited by Genohub; 05-07-2014, 05:57 AM.Comment
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