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AI Tools for Experimental Design: a Guide for Lab Leaders

Free download here: https://mchimenti.gumroad.com/l/zcuabn

Are you a scientist who:

  • Has read claims about AI transforming research but aren’t sure where to start?
  • Is too busy to test out endless tools and separate hype from reality?
  • Feels skeptical (as any good scientist should!) about AI’s real value for research problems?

This guide is for you – the busy researcher who wants practical ways to incorporate AI into their experimental design workflow.

This is a focused “quick-start” guide that’s already helping lab PIs:

  • Save hours on experimental design
  • Generate more comprehensive and reproducible protocols
  • Avoid common AI pitfalls in research contexts

What’s inside:

  • An beginner-friendly intro to Large Language Models (LLMs)
  • Simple step-by-step workflows using tools like Elicit and NotebookLM
  • A money-saving “hack” to get useful results without expensive paid subscriptions
  • Real examples and practical caveats

After seeing these tools transform my own research workflow, I wanted to make this knowledge accessible to every lab.

That’s why I’m offering it for free or as pay-what-you-value!

Download it free if you’re just exploring, or buy me a cup of coffee if you find value for your research program. Think about the time you could save on experimental design!

Get it here:

https://mchimenti.gumroad.com/l/zcuabn

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ATAC-seq best practices (tips)

Figure 3. Signal features generated by different methods for profiling chromatin accessibility.

A few best practices for ATAC-seq assays are suggested as follows:

  • Digest away background DNA (medium/dead cells) using DNase I22
  • Use fresh/cyropreserved cells/tissues to isolate nuclei7,9
  • Reduce mitochondrial/chloroplast DNA contamination as much as possible by using the Omni-ATAC protocol or other methods22–25
  • Optimize the ratio of the amount of Tn5 enzyme to the number of nuclei
  • Optimize the number of PCR cycles19
  • Perform Paired-end (PE) sequencing, e.g., 2 x 50 to 100 bp
  • Sequence > 50 M PE reads (~200 M for footprinting analysis)7

A few best practices for ATAC-seq data analysis are suggested as follows:

  • Perform raw read QC using FASTQC before alignment
  • Perform post-alignment QC using ATACSeqQC10
  • Perform peak calling using a peak caller, such as MACS226 in narrowdPeak mode with option settings: “shift -s and extend 2s”, Genrich, or HMMRATAC.27
  • Perform post-peak calling QC
    • Annotate peaks and generate peak distribution among genomic features using ChIPpeakAnno28
    • Obtain functions of genes associated to peaks using the Genomic Regions Enrichment of Annotations Tool (GREAT)29

Copied From: https://haibol2016.github.io/ATACseqQCWorkshop/articles/ATACseqQC_workshop.html

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New position at the University of Iowa

I am very excited to have begun a new position this July as a Bioinformatics Specialist within the Iowa Institute for Human Genetics (IIHG).

I am fortunate to be working  with a very talented team of expert bioinformaticians, researchers, and programmers.   I have a lot to learn about my new field, but I’m enthusiastic about the challenge and looking forward to participating in the fast-moving and cutting-edge research at the nexus of computer science, data analysis, genetics, and biology.