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Techniques and protocol discussions on sample preparation, library generation, methods and ideas
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Started by mbjohnson, 05-18-2012, 01:19 PM
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1 response
1,506 views
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by mikaelk
07-30-2012, 05:48 AM
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Started by LukaszKielpinski, 07-06-2012, 03:17 AM
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2 responses
2,685 views
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by mikaelk
07-30-2012, 05:26 AM
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Started by darren.obbard, 07-30-2012, 01:00 AM
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0 responses
1,895 views
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Started by alanc, 11-07-2008, 02:12 AM
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5 responses
9,222 views
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by TPayette
07-25-2012, 06:53 AM
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Started by lee27, 07-19-2012, 12:13 PM
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1 response
1,607 views
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by Zaag
07-24-2012, 09:43 AM
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Started by genbio64, 07-20-2012, 11:02 AM
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6 responses
5,178 views
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by Olaf Blue
07-23-2012, 09:35 AM
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Started by hawaii454-0, 07-19-2012, 03:37 AM
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1 response
1,645 views
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by pmiguel
07-20-2012, 10:16 AM
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Started by OptimusBrien, 01-24-2011, 10:46 AM
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1 response
3,126 views
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by mira636
07-12-2012, 12:26 PM
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Started by fgoetz, 10-24-2011, 01:34 PM
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3 responses
7,339 views
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by kmkocot
07-09-2012, 06:34 PM
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Started by MouseCrusader, 06-29-2011, 10:29 AM
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28 responses
24,578 views
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Last Post
by geneart
07-09-2012, 03:34 PM
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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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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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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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07-08-2026, 10:08 AM
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