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Techniques and protocol discussions on sample preparation, library generation, methods and ideas
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Started by tugecko, 06-28-2017, 12:32 PM
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9 responses
3,777 views
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by tugecko
07-20-2017, 06:54 AM
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Started by wangwde, 07-19-2017, 02:03 AM
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2 responses
1,392 views
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by wangwde
07-19-2017, 10:43 PM
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Started by liquidbot, 07-12-2017, 04:16 PM
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2 responses
2,270 views
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by liquidbot
07-17-2017, 10:30 AM
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Started by renuka22, 04-10-2017, 12:15 PM
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12 responses
3,431 views
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by RNA_
07-15-2017, 09:20 AM
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Started by nabilquraishi, 07-15-2017, 12:34 AM
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0 responses
1,265 views
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Started by Chinanicole, 07-11-2017, 09:45 AM
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3 responses
1,638 views
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Started by Tali7, 09-12-2011, 11:54 PM
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19 responses
33,275 views
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Started by danthescienceman, 07-05-2017, 01:13 AM
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6 responses
5,488 views
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by Carcharodon
07-11-2017, 12:14 PM
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Started by Chinanicole, 07-07-2017, 10:24 AM
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5 responses
4,236 views
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by Chinanicole
07-10-2017, 01:03 PM
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Started by patkrat, 04-07-2017, 05:31 AM
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10 responses
6,812 views
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Last Post
by Chinanicole
07-10-2017, 05:19 AM
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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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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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by SEQadmin2
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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