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I'm building a system to generate fastq files as they are being output from HiSeq 3000 and HiSeq 4000. This will serve us to test our internal systems with some gold standards.

As we are working with microbiome a standard sample may contain around 1500 different species, is there a a bias in the output regarding how 'close' each assembly record appear next to each other or is it truly random?

If record 1,1 is from assembly 2913, what are the odds that record 1,2 is of that assembly as well (assuming 1500 species with the same strain length) ?

I couldn't find any paper on that so any help from experience would be great.

Thank, Eden

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  • $\begingroup$ I'm a bit confused what you mean by 'close' - is it the order of the entries in the fastq file? I'm pretty sure that is completely random (it should be more or less dependent on the cluster order on the flowcell). Also note that the primary output of the HiSeq (or any Illumina Sequencer) are not fastq files, but bcl files. Most sequencing facilities do the demultiplexing which generates fastq files for you, so you never see the 'raw' sequencing data. $\endgroup$ – Nicolai Apr 1 at 14:37
  • $\begingroup$ @Nicolai By close I mean records in the fastq file one after another. But by now I think it totally random I didn't know about the bcl file. I'll check it out. Thanks. $\endgroup$ – Eden Apr 1 at 20:54
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In my experience, the distribution of reads on the flow-cell is pretty much random, but the sequencing errors are not necessarily randomly distributed. In all Illumina instruments I have seen, it is common to see edge-effects, or other localities on the flow cell where the error profile differs from the rest of the cell, and QC pipelines generally test for this problem.

A particular issue with the HiSeq3000/4000 patterned flow-cells is that, in situations of under-clustered runs, it would be typical to find the same sequence repeated multiple times in nearby 'clusters' - in other words, a clustering of clusters all containing replicates of the same underlying molecule. Even for runs where there is no severe under-clustering, it might be expected that duplicates would be found in adjacent cells. qcfail.com has an outline of this phenomenon: https://sequencing.qcfail.com/technologies/hiseq/

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