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  • Strandlife
    strandlife
    • May 2013
    • 67

    Unique Molecular Identifiers Workflow Support -Now in Strand NGS v3.1

    Unique Molecular Identifiers, or UMIs, are short sequences (6-12bp) added to sequencing libraries prior to PCR amplification. UMIs can improve the accuracy of RNA-Seq quantification and lead to improved sepcificity in low-frequency variant calling for DNA-Seq. Strand NGS v3.1 implements a complete, end-to-end UMI workflow for DNA-, RNA-, and small RNA-Seq. To know more, write to us at [email protected]

    You could also try Strand NGS v3.1 for free by registering at http://www.strand-ngs.com/signup/freetrial

Latest Articles

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  • SEQadmin2
    Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
    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.
    ...
    Yesterday, 11:10 AM
  • SEQadmin2
    Cancer Drug Resistance: The Lingering Barrier to Rising Survival
    by SEQadmin2



    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...
    07-08-2026, 05:17 AM
  • GATTACAT
    Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by GATTACAT
    Love 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.
    07-01-2026, 11:43 AM

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