Using Claude Code Wrong? Real Genomics Workflow Works
1/ Bioinformaticians get mediocre results with Claude Code and blame the tool. You are doing it wrong. Here's what actually works for genomics analysis: https://t.co/GDOq7SPzla
Six Essential Bioinformatics Workflow Tools Simplify Analysis
6 links on workflow to make your life easier 🧵 Bioinformatics analysis involves a lot of steps, 6 links on workflow to make your life easier: 1. over hundreds of workflow tools and engines https://t.co/R29TTEYSMB
AI Rebuilt My RNA‑seq Pipeline in Minutes
1/ I wasted hours debugging an RNA-seq pipeline. The next day, I rebuilt it in 45 minutes using Claude Code.
Free Tutorial: From FASTQ to GSEA in Bulk RNA‑seq
Free tutorial: bulk RNAseq analysis from fastq to GSEA analysis (watch the full playlist) https://t.co/v0UHRSqJ63 https://t.co/LtdxPNYG5Y
Resumes Boast AI Bioinformatics, but Real Expertise Missing
Looking at bioinformaticians’ profiles these days, you'd think everyone has decades of experience in cutting-edge single-cell and AI-driven bioinformatics. But something’s missing… 👇 https://t.co/yvmvcwXWyr
Massive Single‑cell Data: Deeper Insight or Just Bigger Counts?
1/ Another single-cell study drops. 500,000 cells sequenced. More UMAP plots. More clusters. But here’s the question: Are we learning more—or just counting better? 🧵 https://t.co/nuYRT0GTAq
Claude Code Boosts Bioinformatics Efficiency Tenfold
learn how to use Claude Code. It's changing how I am working as a bioinformatician. If you know what you want, it 10x my efficiency. It is scary to see its power. but like any technology, it will cause disruption. We just need to...
Sequencing Depth Drives Artifacts, Confounds scRNA‑seq Analyses
1/ Per-cell sequencing depth is a major technical effect in scRNA-seq. Different depths change what the data looks like and create artifacts that propagate into clustering, DE, and downstream modeling. And depth heterogeneity itself becomes the signal your methods pick up. https://t.co/RlAD1ONGVX
Linking DNA Mutations to Gene Expression in Single Cells
Genotype-to-phenotype mapping of somatic clonal mosaicism via single-cell co-capture of DNA mutations and mRNA transcripts https://t.co/6Lss1ukbvp https://t.co/bGwnmQEYfI
AI Accelerates Answers, but Domain Knowledge Solves Problems
1/ AI tools are useless if you don't know what you're looking for. I use Perplexity for search. But the AI didn't solve my IGV bug - my domain knowledge did. The AI just helped me find the answer faster. https://t.co/zi2e2FbO5O
Dataset in Hg19 Forces Costly Remapping to Hg38
1/ Found the perfect ChIP-seq dataset on GEO. Then saw "hg19" in the methods. Now you need to remap everything to hg38 before you can integrate it with your data. And the authors didn't share their processing pipeline. https://t.co/3Ui4dyEnkb
Bioinformatics Evolves Fast—Static Recipes Become Outdated
1/ Bioinformatics moves fast. If you rely only on recipes from books, you’ll soon find they’re obsolete. Let me show you why. 🧵 https://t.co/aYztybAnOu
Perturb-Seq Maps T Cell Regulators and Immune Traits
Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits https://t.co/tMc4efSMxe https://t.co/XUdax0qxn1
Small P-Values Aren't Always Biologically Meaningful in Bioinformatics
1/ Bioinformatics is NOT just statistics. The p-value is small, but is it biologically meaningful? Let’s talk. 🧵 https://t.co/iRgiOXPR8v
Sequencing Depth Introduces Predictable Artifacts Affecting DE Analysis
1/ Different sequencing depths don't just change detection. They create specific, predictable artifacts that propagate into your DE calls and any downstream modeling. https://t.co/u0U7E5yq6J
Recreate Any Bioinformatics Figure Easily with One Trick
The Secret to Recreating Any Bioinformatics Figure (It’s easier than You Think). The other day, I was on Bioinformatics subReddit and saw someone ask how to generate the figure below https://t.co/5oOU8As3T3