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Application of sequencing to RNA analysis (RNA-Seq, whole transcriptome, SAGE, expression analysis, novel organism mining, splice variants)
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Started by jgoecks, 11-29-2010, 02:06 PM
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11-07-2016, 01:02 AM
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Started by skoddo, 03-08-2013, 03:59 AM
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01-20-2015, 06:31 PM
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Started by analog900, 01-15-2015, 03:03 PM
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01-20-2015, 09:56 AM
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Started by giuseppepuglia, 01-18-2015, 03:14 PM
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Started by Fabien Campagne, 01-16-2015, 12:43 PM
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01-15-2015, 02:00 AM
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Started by yjhua2110, 01-14-2015, 07:18 PM
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01-14-2015, 07:18 PM
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Started by samhokin, 02-01-2014, 10:20 AM
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01-14-2015, 11:23 AM
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by SEQadmin2
Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.
The systematic characterization of the human proteome has...-
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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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