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  • MGN
    Junior Member
    • Nov 2015
    • 3

    Introduction

    Hi everybody. Glad to see there's a forum for this kind of work.

    I'm a Ph.D. student in statistics at UIUC but doing my research with Tandy Warnow in the CS department. Her main focus is phylogeny estimation and sequence alignment methods but I'm branching out into metagenomics and other applications as I learn more about the possibilities.

    It's funny to have ended up in this world because I haven't taken a biology class since 9th grade, so I'm always trying to soak in the nuts and bolts of how this stuff is applied rather than just blindly developing new methods. Hope to keep learning more from you all!

    Mike Nute

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.
    ...
    07-09-2026, 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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