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Is there paper where this is shown?Originally posted by chadn737 View PostIt has been shown in many cases that Biological variation is far greater than technical variation in Illumina sequencing and typically individual runs are pooled together and treated as one sample. Ideally thats why you multiplex all your biological replicates and spread the sequencing across multiple lanes.
So perhaps I should merge my .sam files per biological replicate and then do the tophat/bowtie alignment?
Thanks.
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It has been shown in many cases that Biological variation is far greater than technical variation in Illumina sequencing and typically individual runs are pooled together and treated as one sample. Ideally thats why you multiplex all your biological replicates and spread the sequencing across multiple lanes.
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Read runs and technical replicates
So I've downloaded some example data from SRA archives at http://www.ncbi.nlm.nih.gov/sra?term=SRA011001 and had a question about technical and biological replicates (see attached picture for a snapshot of the downloads). I know that there are two biological replicates for control and treatment conditions (in this case a miR155 over-expression retrovirus) but I'm not sure about the technical replicates.
Do read runs count as technical replicates?
And, if so, how can I use that in my analysis pipeline (currently I'm using the Tuxedo suite (i.e. tophat > cufflinks > cuffmerge, etc.)).
Right now I think I'm just telling the program that there are six biological replicates of each condition (i.e. 6 x miR155oe vs. 6 x control) which is probably wrong.
Thanks, ScottAttached FilesTags: None
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by SEQadmin2
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...-
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by SEQadmin2
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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