Arjun Krishnan

From Arjun Krishnan @compbiologist.bsky.social

Expertise before augmentation: a practical guide to using generative AI during research training: zenodo.org/records/1845...

Build expertise first: why PhD training must sequence AI use after foundational skill development: zenodo.org/records/1864...

CU Anschutz Recruits National Leader to Launch Functional Personalized Medicine Initiative CU Anschutz welcomes Alice Soragni to launch a Functional Personalized Medicine Initiative using rapid tumor organoid testing to guide treatment decisions.

Happy to share this new, very intentional chapter. I have left UCLA after 14 years to join the University of Colorado Anschutz as Professor of Biomedical Informatics and Neurosurgery and the inaugural Marsico Chair in Excellence in Functional Precision Medicine/n

news.cuanschutz.edu/dbmi/cu-ansc...

Our Fish EvoDevoGeno Lab @michiganstateu.bsky.social has its 10th anniversary today! 🐠🐟🧪🧬🔬

Thanks to all lab members - present & past, pictured or not - for making the last decade a success!

& thanks to our partners in crime of the @brainyfishguts.bsky.social Lab, too!

#EndlessFishMostBeautiful

4/4 This advanced short course formalizes the instruction of these ideas. The goal is to:
1) Discuss common misunderstandings & typical errors in the practice of statistical data analysis.
2) Provide a mental toolkit for critically thinking about statistical methods & results.

Feedback welcome 🙌🏼

3/4 As a result, most students piece together a mental model of acceptable, standard, or "best" practices in their field from shards of information gathered from mentors, peers, and published papers.

2/4 Statistical inquiry, data analysis, and visualization are immensely powerful, but many of the ideas underlying them are nuanced and unintuitive. Unfortunately, these ideas—and the skills needed to apply them to real problems and datasets—are rarely taught in statistics or data-analysis courses.

HMGP 7622 Rethinking Data Analysis — A researcher’s guide to avoiding missteps and misuse Feb 3 – May 5, 2026 | Tue 2–3:30p OVERVIEW This is a short (1-credit) course designed to: 1) Discuss common misunderstandings & typical errors in the practice of statistical data analysis. 2) Provide a mental toolkit for critically thinking about statistical methods and results. TOPICS Estimating error, uncertainty • Underpowered statistics • Multiple testing • P-hacking • Pseudoreplication • Regression to the mean • Double dipping • Spurious associations • Visualization challenges • Reproducibility, replicability PREREQUISITES 1) Introductory knowledge of statistics & probability 2) Introductory experience with data wrangling, analysis, & visualization using R/Python. INSTRUCTOR Arjun Krishnan Associate Professor, Department of Biomedical Informatics University of Colorado Anschutz Medical Campus arjun.krishnan@cuanschutz.edu | @compbiologist | thekrishnanlab.org

I'm looking forward to re-teaching:
Rethinking Data Analysis — A researcher’s guide to avoiding missteps and misuse

This is an advanced short course on developing a mental toolkit for rigorous practice & critical consumption of statistical data analyses. 🧵 1/4

Computational strategies for cross-species knowledge transfer - Nature Methods This Perspective reviews computational methods for cross-species knowledge transfer and introduces ‘agnology’, a data-driven concept of functional equivalence independent of evolutionary origin.

A Perspective reviews computational methods for cross-species knowledge transfer.

www.nature.com/articles/s41...

10/10 Big thanks to NIH/NIGMS, NSF, &
@simonsfoundation.org for funding this work!
We welcome feedback from the community! 🙌
#Bioinformatics #TranslationalResearch #OpenScience

9/10 By embracing data-driven, evolution-agnostic approaches, we believe that the field can accelerate discoveries in both common and rare diseases, improving model organism selection and ultimately paving the way for more reliable therapeutic interventions.

8/10 Key future directions we highlight:
- Capturing specific facets of complex diseases
- Building networks for more species & contexts
- Automated ontology/knowledge graph construction
- Better benchmarking for cross-species single-cell methods
- Leveraging non-traditional research organisms

7/10 Kudos to resources like @geneontology.bsky.social , @monarchinitiative.bsky.social, @alliancegenome.bsky.social, & @bgee.org for grounding so much data & knowledge in this space in structured formats. These & many others are included in our catalog ☝🏽

6/10 We provide detailed resources to help computational & wet-lab researchers find, improve-upon, and apply appropriate methods:
📊 Supp Table 1: Comprehensive catalog of methods (name, category, input/output, data types)
📚 Supp Table 2 & Note: Valuable datasets for cross-species work

5/10 With the explosion of large-scale multi-species genomics data and advanced #AI & #ML methods, it's an exciting time to rethink cross-species translational biomedicine. Our article offers a roadmap to navigate this frontier and maximize the value of research organisms.

Figure depicting how traditional methods rely on homology to determine functional equivalence and, by contrast, modern data-driven approaches use what we call 'agnology', identifying fully or partially functionally equivalent genes regardless of evolutionary origin, allowing for ambiguity in homology or analogy.

4/10 Traditional approaches rely heavily on homology. But shared ancestry ≠ shared function & vice-versa.
Here, we introduce the concept of Agnology, which embraces this complexity: "agno-" = unknown/not known, reflecting data-driven functional equivalence regardless of evolutionary origin.

3/10 Our article covers methods that tackle 4 key questions in cross-species research:
1. Predicting function/disease-gene relationships across species
2. Identifying agnologous molecular components
3. Inferring perturbed transcriptomes across species
4. Mapping agnologous cell types and states

2/10 #ResearchOrganisms like 🐭 & 🐟 are crucial for studying genes, functions, cell types, & disease. But translating findings to 👨‍⚕️ is tricky. We explore data-driven methods to bridge the gap & introduce the concept of "agnologs" — functional equivalents identified independent of evolutionary origin.

Figure depicting the four classes of important questions that frequently arise when using research organisms to study biomedical questions and translating findings to humans. a, How to predict disease–gene or function–gene relationships across species? Diagram depicts genes in each species associating with specific functions, diseases and phenotypes. b, How to identify functionally equivalent molecular components across species? Diagram depicts finding the most equivalent gene, pathway or expression module or phenotype between species. c, How to infer perturbed molecular profiles across species? Diagram depicts gene expression in each species as a result of taking a particular perturbation like a drug. d, How to map equivalent cell types and cell states across species? Diagram depicts alignment of cell types across species. This Perspective comprehensively lays out the landscape of recent and state-of-the-art data-driven strategies, including those that leverage artificial intelligence and machine learning (ML), for answering these questions.

Our Perspective article on Computational Strategies for Cross-Species Knowledge Transfer is now published in @natmethods.nature.com!

This was a collab b/w @krishnanlab.bsky.social & @fishevodevogeno.bsky.social, led by the amazing Hao Yuan @yhbioinfo.bsky.social. 🧵

www.nature.com/articles/s41...

Same from @cp-cellreports.bsky.social: Dec 24th, 25th, & 27th. Closed/removed on Dec 30th!

Known for decades: DNA sequence drives nucleosome "rotational positioning" (which face of DNA contacts histones)

But: How does this persist when remodelers & transcription constantly mobilize nucleosomes? Our new preprint 1/ :

www.biorxiv.org/content/10.6...

This preprint is now out after peer review! Check it out: www.cell.com/cell-genomic.... Huge congrats (and thanks!) to the whole team that contributed!

Very proud to be a member of this team. A huge group effort to improve the lives of those with Down syndrome. Thank you to everyone that has helped us along the way.

Congratulations! Kudos to @richabdill.com & @samanthagraham.bsky.social for leading this huge project!

Thanks for bring us onboard! Mansooreh Ahmadian & Parker Hicks lead the part of the work on inferring study annotations from unstructured metadata and text from the linked publications.

🖐🏼

We just use the #papers-articles channel our group’s Slack workspace.

A favorite!

Interestingly, Goodhart stated (in 1975): “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”

Marilyn Strathern generalized it in 1997 to its famous version👇🏽

pmc.ncbi.nlm.nih.gov/articles/PMC...

Regularly tempted to write in my NIH grants innovation section: "Funding software that already exists and works well would be highly innovative for the NIH."

(I bet half the panel would break down ROFL, but I'm also highly skeptical that I'd get a good score, or that the PO would be amused.)

Last update of our databases for the year.

Download them here:

493 early-career funding opportunities: research.jhu.edu/rdt/funding-...

313 postdoc fellowships: research.jhu.edu/rdt/funding-...

189 PhD fellowships: research.jhu.edu/rdt/funding-...

A morning landscape shot from the rooftop of a building in Aurora, CO. The scene faces west towards the Front Range mountains, with the city skyline of Denver in the center of the shot. The view, rows of scattered buildings, evergreens, and brown branches of leafless deciduous trees, rising up into the mountains, is blanketed in a crisp white layer of snow. The sky is a sharp blue to white gradient with clouds enveloping the highest mountain peaks including Mt. Blue Sky in the distance. Some goofy Canadian geese are in mid-flight at around the level of the horizon, and while you can't hear them in a photo, I certainly could at the time.

The views from the office are another real perk of working here at @cubiomedinfo.bsky.social.

Incidentally, on ping with this discussion…

bsky.app/profile/harm...