Shaman Narayanasamy 🔝

NEW: $3M in Fast Grants for AI-driven life sciences research. We’re funding pilot projects in AI diagnostics, prediction, and personalized therapeutics — with a two-week review turnaround. 35 grants · $25K–$100K · Deadline: June 15 Please share! biswasfamilyfoundation.org/science/fast-g…

We wrote our own hyper-efficient version of the STAR aligner. Typically it costs around $35 in cloud compute to process 30k cells with Cell Ranger. Ours costs so little that we don't charge for it. Also ours works with nanopore. If you prefer pseudoalignments, then you can run it in pseudoalignment mode -- obviously we don't charge for that too. All results instantly integrated with our other tools. As soon as a run finishes you can open alignments or expression matrices in our viewers without moving anything. Let me know if you want to try it!

Sci-Hub is an evil website that pirated 85M+ research papers and made them freely available And now they've added AI to their database to make Sci-Bot. It answers your questions using latest, full-text articles. But DO NOT use it. We should all try to make billion-dollar academic publishers richer. I'm putting the link below so you know how to avoid it.

📢🎉@hhmi_science competition for the next cohorts of #HannaGray Fellows and #FreemanHrabowski Scholars opening 11/3/2026. Senior postdocs and faculty within 7 years of appointment. Up to $10M over 10 yrs, + salary & benefits. Spread the word and consider applying! bit.ly/4vhC0LA

🧵Want to speed up your bioinformatics tasks? Stop looping. Start paralleling. Here’s how to use GNU parallel like a pro:

I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter divingintogeneticsandgenomics.ck.page/profile

DefensePredictor: A machine learning model to discover prokaryotic immune systems | Science science.org/doi/10.1126/sc…

Traditional single cell transcriptomics doesn't capture most non-coding RNAs. In this work, led by @alinaisakovaSci and @StephenQuake, we introduce TotalX, a @10xGenomics-compatible pipeline that captures both coding and non-coding transcripts. Out now in @NatureBiotech! 1/7

Single-molecule peptide sequencing through reverse translation of peptides into DNA go.nature.com/4sRPr2K

Aged adults with extraordinary memory capacity – known as SuperAgers – exhibit a unique neurogenesis profile with a surprisingly high number of young neurons, according to a study published in @Nature. go.nature.com/4l8VJsf

Out Now! Multi-omics reveals nitrogen dynamics associated with soil microbial blooms during snowmelt dlvr.it/TQc0Lc #Microbiology #SoilHealth #NitrogenCycle

Attention scientists from all over the world🙃 The Call for Letters of Intent for #HFSPResearchGrants 2027 is now open! Get your international & interdisciplinary team. It's time to put your bold research idea into practice! 🔗bit.ly/48VsGCG 📅 15 Dec 2025–26 Mar 2026

A single blood test could reveal the biological stage of Alzheimer's disease nature.com/articles/s4358…

scplotter provides a set of functions to visualize single-cell sequencing data in an easy and efficient way github.com/pwwang/scplott…

Today in @Nature, in work led by @aditimerch, we report the ability to prompt Evo to generate functional de novo genes. You shall know a gene by the company it keeps! 1/n

BioContextAI is an open-source initiative for creating agentic biomedical research assistants from standardized components go.nature.com/3JmqKux rdcu.be/ePz3v

Claude scientific skills 🧬🖥️🧪 github.com/K-Dense-AI/cla… 🧬 Bioinformatics & Genomics 🧪 Cheminformatics & Drug Discovery 🔬 Proteomics & Mass Spectrometry 🤖 Machine Learning & AI 🔮 Materials Science & Chemistry 📊 Data Analysis & Visualization

Phage therapy faces a major hurdle: rapid immune clearance. The latest #mBio #mGem Perspective highlights the need for stealthier, next-gen phages to outsmart host defenses and fight resistant infections. doi.org/10.1128/mbio.0… #bacteriophage #phage #virology

Every researcher's nightmare: "Your sample size is too small to detect that effect." This open-access paper in Frontiers in Medicine solves that problem BEFORE you collect data. "How to calculate sample size in animal and human studies" by Zhang & Hartmann breaks down: ✓ What is statistical power? (and why 0.8 isn't magic) ✓ How to choose effect size ✓ What to do when you have pilot data ✓ What to do when you have NO pilot data ✓ How to account for dropouts ✓ Actual G*Power screenshots with parameters filled in The paper includes worked examples for: - Comparing two proportions - Comparing two means - Studies with unequal group sizes - Studies expecting attrition The hard truth: Most underpowered studies aren't because researchers are careless. They're because sample size calculation is genuinely confusing and rarely taught well. This paper treats you like an intelligent person who just needs clear explanation and practical examples. Read it before you write your next ethics application or grant proposal. Your future self (and your reviewers) will thank you.

BioWorkflow: Retrieving comprehensive bioinformatics workflows from publications academic.oup.com/bib/article/26… 🧬🖥️🧪