Hello Siya123 ,
Thanks for the additional information. The recommendations for the dilution factor are starting recommendations, but may...
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Hello Siya123 What normalization method and library preparation protocol are you using? And before Qubit quantitation, are you performing a library amplification...
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Hello samd199
I know I've worked with people in the past that stored fecal samples in ethanol. I don't recall what the concentration was...
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Hello RNasymetrical that's a good observation. I unfortunately don't have the answer, but it seemed to me that it could be potentially answered using...The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach. We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%). In this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably corrected statistically in well-designed experiments.
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Yeah if you wanted to only prep a couple samples out of a 96 kit, then you could do that. I've done it plenty of times. I'm not sire what you mean about...
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Hi @Davidapplevv
You are correct with the sample kit. That should have everything you need for 24 samples and you can save the rest for...
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Hi Ben3 thank you for your help. I have a question about Lib prep kits.
llumina® DNA PCR-Free Prep, Tagmentation (24 Samples)...this kit...
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Arianna Cerana. Yes, I've had issues with denaturing the ladder in the past. I think it may have been the thermocycler I used. But most of my issues were...
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No worries Davideapplevv. You're correct, with a 200-cycle kit you couldn't do a 1x150bp run. You could do a 200 single read or a 2x100 pair-ended run....
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