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New Resources for 1000 Genomes
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by laura
12-05-2013, 02:37 AM
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Software packages for next gen sequence analysis
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236 responses
682,665 views
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by ECO
12-25-2009, 06:45 PM
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by ccdm
10-24-2016, 04:35 PM
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by Trevaly
10-24-2016, 01:21 PM
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by zjrouc
10-24-2016, 11:47 AM
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by ksawatzki
10-24-2016, 06:09 AM
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by johnng wang
10-23-2016, 07:40 PM
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by aghosh
10-23-2016, 08:40 AM
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by GenoMax
10-23-2016, 05:27 AM
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by tfcardoso
10-22-2016, 07:38 AM
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by Ursus07
10-21-2016, 04:11 PM
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by LacquerHead
10-21-2016, 06:35 AM
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No topics found.
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Childhood cancers, effectively fatal 60 years ago, are now curable for up to 85% of children with access to contemporary treatments and...05-08-2026, 01:42 AM
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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.07-01-2026, 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