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Techniques and protocol discussions on sample preparation, library generation, methods and ideas
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Started by docbio, 05-09-2013, 08:22 PM
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16 responses
32,543 views
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by Kaizen
07-09-2017, 11:12 PM
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Started by dinber, 07-03-2017, 03:29 AM
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3 responses
6,956 views
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by Ola
07-06-2017, 02:12 PM
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Started by anjama, 07-02-2017, 05:59 PM
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3 responses
2,885 views
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by Carcharodon
07-04-2017, 02:51 PM
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Started by dBlatt, 02-24-2015, 06:10 AM
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3 responses
3,406 views
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by Gulzar Khan
07-03-2017, 11:30 AM
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Started by sc10021, 03-08-2013, 11:50 AM
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12 responses
18,069 views
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by torben
07-03-2017, 01:26 AM
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Started by anjama, 06-08-2017, 04:49 PM
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3 responses
5,173 views
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by anjama
07-02-2017, 06:07 PM
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Started by HelenaSC, 05-10-2016, 12:45 AM
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2 responses
1,939 views
0 reactions
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by LAO
07-02-2017, 09:52 AM
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Started by kropak1807, 03-29-2017, 08:47 AM
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2 responses
2,571 views
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Started by Dorota, 06-20-2017, 06:16 AM
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3 responses
2,923 views
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Started by renuka22, 06-09-2017, 09:58 AM
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1 response
1,219 views
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by huguesparri
06-14-2017, 11:02 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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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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