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I have seen duplicate percentage of 33% in targeted high coverage experiment and based on my assessment of false positive and false negative I am happy not removing duplicates.
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It depends on what your experiment is. In some cases it has very little effect, in some it can be detrimental. In no case that I've heard of does leaving PCR duplicates in your data improve your results, however.Originally posted by husamia View PostMendelian Disorder: A blogshare of random useful information for general public consumption. [Blog]
Breakway: A Program to Identify Structural Variations in Genomic Data [Website] [Forum Post]
Projects: U87MG whole genome sequence [Website] [Paper]Comment
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Hi. everybody
I have a problem about remove duplicates,
In this study , http://www.ncbi.nlm.nih.gov/Traces/sra/?study=ERP000603
It's have 2 Experiments, and 13 RUNs,
How to do remove duplicates? by study? by Experiments? or by one runs?
Thanks !!Comment
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Remove PCR duplicates by library (and this looks like one big "library" mixed from 4 PCR pools). Optical duplicates are removed by lane (but I guess Picard would detect the within lane reads, given proper read group labelling).Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.Yesterday, 11:43 AM
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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... -
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.
...06-02-2026, 10:05 AM
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