3k threads and almost 7k members

ECO

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Join Date: Oct 2007

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#1

3k threads and almost 7k members

02-04-2010, 08:42 PM

Another milestone this evening...just hit the 3,000 thread mark, and we're rapidly closing in on the 7,000 members mark.

We've just about crossed the line where I can't keep up reading every single post...many thanks to nilshomer for helping moderate as well as all the other people who report posts.
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Xi Wang

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Join Date: Oct 2009

Posts: 317
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#2

02-04-2010, 11:15 PM

Originally posted by ECO View Post

Another milestone this evening...just hit the 3,000 thread mark, and we're rapidly closing in on the 7,000 members mark.

We've just about crossed the line where I can't keep up reading every single post...many thanks to nilshomer for helping moderate as well as all the other people who report posts.

Congratulations!

Xi Wang
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Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing

by GATTACAT

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.
- Channel: Articles
Today, 11:43 AM
Nine Things a Sample Prep Scientist Thinks About Before Sequencing

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...
- Channel: Articles
06-18-2026, 07:11 AM
From Collection to Sequencing: Why Sample Preparation and Preservation Define Sequencing Data

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.
...
- Channel: Articles
06-02-2026, 10:05 AM

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