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Techniques and protocol discussions on sample preparation, library generation, methods and ideas
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Started by cement_head, 05-29-2018, 12:22 PM
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1 response
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by cement_head
05-30-2018, 05:54 AM
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Started by burns_newsome, 05-29-2018, 07:10 AM
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2,046 views
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Started by thang198, 05-22-2018, 08:25 AM
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1,061 views
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by thang198
05-22-2018, 08:25 AM
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Started by sasignor, 01-18-2018, 03:22 PM
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1 response
2,512 views
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by coralxanpu
05-21-2018, 03:51 PM
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Started by robins91, 05-09-2018, 01:30 PM
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4 responses
2,601 views
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by cement_head
05-21-2018, 10:38 AM
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Started by tmm343, 06-29-2016, 11:56 AM
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13 responses
7,292 views
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by DPVT
05-21-2018, 07:59 AM
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Started by JakobHedegaard, 05-18-2018, 02:03 AM
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1 response
2,492 views
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by kwaraska
05-18-2018, 01:13 PM
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Started by anjama, 05-17-2018, 02:41 PM
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1 response
2,002 views
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by austinso
05-17-2018, 06:56 PM
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Started by sweetph3, 10-16-2013, 12:24 PM
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3 responses
4,910 views
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by kwaraska
05-16-2018, 11:59 AM
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Started by torbean, 05-09-2018, 06:29 AM
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0 responses
1,898 views
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Last Post
by torbean
05-09-2018, 06:29 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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07-09-2026, 11:10 AM -
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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, 10:26 AM
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by SEQadmin2
Yesterday, 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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