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Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc
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Started by laura, 05-09-2011, 04:42 AM
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35 responses
107,473 views
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by laura
12-05-2013, 02:37 AM
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Started by sci_guy, 01-23-2008, 11:19 PM
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236 responses
683,708 views
1 reaction
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by ECO
12-25-2009, 06:45 PM
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Started by create.share, 05-13-2014, 12:03 AM
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1 response
1,156 views
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by maubp
05-18-2014, 10:00 AM
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Started by Bara'a Al-Hammad, 05-18-2014, 01:06 AM
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0 responses
1,257 views
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Started by silin284, 12-03-2010, 11:53 AM
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3 responses
5,095 views
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by emanlee
05-17-2014, 11:19 PM
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Started by daniel_navarro, 05-16-2014, 07:56 AM
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1 response
2,800 views
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by rnaeye
05-17-2014, 10:59 AM
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Started by punto_c, 05-16-2014, 12:36 AM
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4 responses
3,894 views
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Started by Fernas, 05-17-2014, 03:58 AM
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2 responses
2,291 views
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by Fernas
05-17-2014, 05:13 AM
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Started by bagaric, 05-16-2014, 09:18 PM
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2 responses
1,348 views
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by bagaric
05-16-2014, 09:32 PM
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Started by lwhitmore, 05-16-2014, 01:52 PM
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0 responses
1,168 views
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by lwhitmore
05-16-2014, 01:52 PM
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Started by AdrianP, 05-16-2014, 01:03 PM
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0 responses
1,012 views
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by AdrianP
05-16-2014, 01:03 PM
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Started by ju.lie, 05-15-2014, 09:15 AM
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7 responses
2,348 views
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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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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, 07-13-2026, 10:26 AM
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20 views
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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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by SEQadmin2
07-08-2026, 10:08 AM
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