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Techniques and protocol discussions on sample preparation, library generation, methods and ideas
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Started by vanillasky, 03-22-2014, 10:37 PM
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2 responses
4,510 views
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by vanillasky
06-19-2014, 10:17 PM
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Started by Todd McLay, 06-19-2014, 07:23 PM
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0 responses
3,528 views
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by Todd McLay
06-19-2014, 07:23 PM
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Started by blair.chr, 03-02-2013, 07:09 AM
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10 responses
10,972 views
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by rosatoc
06-19-2014, 02:01 PM
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Started by MLog, 03-07-2014, 02:47 PM
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5 responses
5,980 views
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by MLog
06-11-2014, 02:37 PM
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Started by kzarn, 06-09-2014, 02:39 PM
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2 responses
3,498 views
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by kzarn
06-11-2014, 09:04 AM
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Started by Acacianpunk, 06-09-2014, 05:06 PM
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3 responses
4,009 views
0 reactions
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Started by DrMarineMicro, 01-06-2013, 11:31 AM
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10 responses
8,802 views
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Started by luc, 06-10-2014, 09:49 AM
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0 responses
2,585 views
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by luc
06-10-2014, 09:49 AM
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Started by mollusc, 10-03-2009, 05:20 PM
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6 responses
3,972 views
0 reactions
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by Susanne
06-09-2014, 11:38 PM
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Started by dtm2451, 03-21-2014, 09:23 AM
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15 responses
13,136 views
0 reactions
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Last Post
by pmiguel
06-09-2014, 06:47 AM
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by SEQadmin2
Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
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Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
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07-08-2026, 05:17 AM -
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by GATTACATLove 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.
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Started by SEQadmin2, 07-13-2026, 10:26 AM
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18 views
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by SEQadmin2
07-13-2026, 10:26 AM
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Started by SEQadmin2, 07-09-2026, 10:04 AM
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30 views
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by SEQadmin2
07-09-2026, 10:04 AM
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Started by SEQadmin2, 07-08-2026, 10:08 AM
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16 views
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07-08-2026, 10:08 AM
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