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Techniques and protocol discussions on sample preparation, library generation, methods and ideas
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Started by dobbr493, 07-07-2011, 07:16 PM
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12 responses
3,421 views
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by pmiguel
06-05-2012, 05:49 PM
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Started by luc, 06-05-2012, 05:30 PM
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0 responses
2,759 views
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by luc
06-05-2012, 05:30 PM
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Started by whw, 06-05-2012, 01:26 PM
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0 responses
1,502 views
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by whw
06-05-2012, 01:26 PM
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Started by CC_seqanswers, 06-04-2012, 08:50 AM
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4 responses
3,210 views
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Started by nisha barak, 05-22-2012, 06:32 AM
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5 responses
3,708 views
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by pmiguel
06-02-2012, 08:41 AM
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Started by bernardo_bello, 05-28-2012, 02:25 PM
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5 responses
5,025 views
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by pmiguel
05-30-2012, 05:31 AM
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Started by dmneedha, 05-29-2012, 10:52 AM
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0 responses
2,198 views
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by dmneedha
05-29-2012, 10:52 AM
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Started by ValeD, 05-15-2012, 02:47 AM
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8 responses
3,256 views
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by rnal
05-28-2012, 08:58 PM
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Started by pmiguel, 05-25-2012, 06:19 AM
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4 responses
4,030 views
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by pmiguel
05-27-2012, 11:02 AM
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Started by Smriti, 02-16-2012, 10:07 PM
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2 responses
3,349 views
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Last Post
by Smriti
05-25-2012, 01:09 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.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...-
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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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07-01-2026, 11:43 AM -
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Started by SEQadmin2, Yesterday, 10:04 AM
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by SEQadmin2
Yesterday, 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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Started by SEQadmin2, 07-07-2026, 11:05 AM
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07-07-2026, 11:05 AM
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