There are no conversations in this channel.
Please log in to your account to view your subscribed posts.
| Topics | Statistics | Last Post | ||
|---|---|---|---|---|
|
Closed, Sticky:
NGS Blogs
|
52 responses
117,796 views
0 reactions
|
by archana87
04-23-2019, 01:18 PM
|
||
|
0 responses
2,757 views
0 reactions
|
by sah11
01-12-2021, 06:37 PM
|
|||
|
1 response
3,719 views
0 reactions
|
by AllisonSharp
11-20-2020, 03:18 AM
|
|||
|
0 responses
2,890 views
0 reactions
|
11-16-2020, 12:55 PM
|
|||
|
0 responses
30 views
0 reactions
|
11-16-2020, 12:02 PM
|
|||
|
1 response
2,907 views
0 reactions
|
by fainabuff
11-06-2020, 02:45 AM
|
|||
|
0 responses
4,204 views
0 reactions
|
by oliver010
11-04-2020, 02:08 AM
|
|||
|
69 responses
134,289 views
0 reactions
|
by omadmelrag7y
11-03-2020, 03:58 PM
|
|||
|
11 responses
66,230 views
0 reactions
|
by Shazzas
10-25-2020, 12:59 AM
|
|||
|
0 responses
4,268 views
0 reactions
|
by aditi1234
10-20-2020, 07:17 PM
|
|||
|
10 responses
18,765 views
0 reactions
|
by CindyBoone
10-19-2020, 03:46 AM
|
No topics found.
widgetinstance 229 (Webinars) skipped due to lack of content & hide_module_if_empty option.
-
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
-
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