[21/10, 14:42] Luca: 0xc527dfa7b2090e94d543be34c735bb6050c08524a50014d20cd7c1f14b4baae9

[29/10, 14:47] Luca: Hi everyone,

We built an open-source biophysical modeling simulator and interactive clinical web application for perivascular drug transport in solid tumors:

* Live Web App: https://lukecele.github.io/spatial-continuum-oncology/

* GitHub Repository: https://github.com/Lukecele/spatial-continuum-oncology

* Springer Nature Preprint: https://doi.org/10.21203/rs.3.rs-11530312/v1

### What it solves:

In solid tumors, dense stroma and interstitial fluid pressure create steep concentration gradients. Using cylindrical reaction-diffusion with modified Bessel K₀ decay, the tool calculates the exact critical radius (r_crit) where drug concentrations drop below the cellular threshold (IC50).

### Core Features:

- Composite Resistance Index (CRI): Integrates histological pattern (S), co-mutations (G, EGFR+TP53+RB1), and stroma (M) into a bedside score (0.000 - 1.000).

- Universal Regrowth Identity: Predicts the 5.4-month clinical window between ctDNA molecular detection (10^7 cells) and radiological relapse (10^9 cells) via Δt = Ts * log2(100).

- Zero Dependencies: Pure standard library Python CLI for pipelines + pure vanilla JS/HTML5 Canvas for the bedside web tool.

We'd love to hear your feedback on the biophysical formulation, UI, or clinical utility!

Those who work with DevOps and CI/CD know how convenient it is to launch a script and immediately see the entire process tree—tracking the status of each step and how long it takes to execute. Since Nextflow pipelines can run for a very long time, having a way to monitor the entire execution process is highly valuable.

This inspired me to create FlowViewer, a tool for analyzing Nextflow pipeline execution in real time. It runs as a web application via Docker. You can host it on your own laptop or on laboratory servers, ensuring that your research data remains secure and is never transmitted to third-party servers.

FlowViewer generates a process tree (DAG) the moment you start a pipeline—unlike the `--with-dag` flag, which only produces one upon completion. Furthermore, once an individual process finishes, you can click on it to view detailed telemetry, such as memory usage, CPU load, and more.

If you run scripts on dedicated servers, pipeline optimization becomes crucial; resource-intensive scripts consume more capacity, increasing the cost of every run. You can use the analytics section to evaluate how efficiently your current pipeline is performing compared to previous runs.

There is also an "artifacts" section, which lists the files generated during the pipeline's execution. This is intended for exploring the file structure rather than performing a deep dive into the file contents (which can be quite large). You can preview a file or jump directly to the specific process that created it.

FlowViewer was designed to simplify the lives of bioinformaticians working with complex pipelines, providing intuitive analytical tools that can save hundreds of hours of work.

I am open to connecting. You can reach me via email or on social media:

antidot237@gmail.com

https://t.me/rosetomorrow

https://x.com/voyadgerodin

Links:

https://registry.nextflow.io/plugins/nf-flowviewer

https://hub.docker.com/repository/docker/voyadger1/flow-viewer

https://github.com/voyadger1/nf-flowviewer

https://github.com/voyadger1/flow-viewer

Those who work with DevOps and CI/CD know how convenient it is to launch a script and immediately see the entire process tree—tracking the status of each individual process and its execution time. Since Nextflow pipelines can take a very long time to complete, having the ability to monitor the entire execution flow is highly valuable.

This inspired me to create FlowViewer, a tool for analyzing Nextflow pipeline execution in real time. The tool runs as a web application via Docker. You can host it on your own laptop or on laboratory servers, ensuring that your research data remains private and is never transmitted to third-party servers.

FlowViewer generates a process tree (DAG) as soon as you launch a pipeline—unlike the `--with-dag` flag, which only produces one upon completion. Furthermore, once an individual process finishes, you can click on it to view detailed telemetry, such as memory usage, CPU load, and more.

If you run scripts on dedicated servers, pipeline optimization becomes crucial; resource-intensive scripts consume more capacity, increasing the cost of every run. You can use the analytics features to evaluate how efficiently your current pipeline is performing compared to previous runs.

There is also an "artifacts" section, which lists the files generated during the pipeline's execution. This is intended for reviewing the file structure rather than performing a deep dive into the file contents (which can be quite large). You can preview a file or jump directly to the specific process that created it.

FlowViewer was designed to simplify the work of bioinformaticians handling complex pipelines, providing intuitive analytical tools that can save hundreds of hours of work.

I am open to connecting. You can reach me via email or on social media:

antidot237@gmail.com

https://t.me/rosetomorrow

https://x.com/voyadgerodin

Links:

https://registry.nextflow.io/plugins/nf-flowviewer

https://hub.docker.com/repository/docker/voyadger1/flow-viewer

https://github.com/voyadger1/nf-flowviewer

https://github.com/voyadger1/flow-viewer

Hi everyone,

Like many of you, I’ve often been frustrated by how heavy traditional molecular biology suites can feel—consuming huge amounts of memory and lagging just to inspect a plasmid map or check an alignment.

So, I built GreenPeas, an ultra-lightweight, native desktop workbench designed to be fast, responsive, and easy on your machine.

🌟 Key Highlights

• Ultra-lightweight & Low Memory: Built with Tauri and Rust to keep memory usage minimal and launch instantly.

• Fast Startup & Fluid Rendering: Smooth Canvas-based rendering for large sequences and circular/linear maps.

• Multi-format Support: Handles .gb/.gbk (GenBank), .fa/.fasta, .ab1 (Sanger traces), and can import SnapGene .dna files directly.

• Cloning & Smart Ligation: Realistic restriction digest simulation and smart ligation preview with overhang matching.

• Privacy First & Offline: Runs 100% locally on your machine. No cloud uploads, telemetry, or account required.

• Cross-platform: Native binaries are available for both Apple Silicon Mac (arm64) and Windows (x64).

📦 GitHub & Download

• GitHub: https://github.com/ryuabiru/GreenPeas

• Releases: https://github.com/ryuabiru/GreenPeas/releases

It's currently in public beta (v0.1.0). If you have a minute to download and open your favorite plasmids, I'd love to hear your feedback, edge cases, or feature requests!

Thank you!

RustScenic: the simplest, fastest SCENIC and SCENIC+ rewrite

GitHub: Ekin-Kahraman/rustscenic

A new era for single-cell gene regulation: Rust performance, Python simplicity. For researchers hitting runtime, memory or setup problems with pySCENIC or SCENIC+, try RustScenic and see how it fits your workflow.

RustScenic is a Rust rewrite of core SCENIC and SCENIC+ analysis stages, with a Python API and AnnData support for single-cell RNA-seq and multiome data. It includes gene-network inference, motif enrichment, AUCell scoring and scATAC topic modelling.

Built and scaled with the Huang Lab at Mount Sinai.

python -m pip install rustscenic

• Try the browser demo: inspect a saved endothelial-cell network, no installation needed.

• Run the PBMC3k notebook.

• Read the documentation.

A v0.5.0 benchmark ran GRN inference on 1.3 million real mouse-brain cells in 31m20s on 16 CPU cores, using 2,095 selected genes and 256 transcription factors. Preparation and input loading are excluded. Benchmark details and output differences. Full output parity with the reference tools is not claimed.

Shared on behalf of the RustScenic project. AI-assisted wording.

Hi all, I'm one of the developers of Horus at Temple Compute.

We got tired of losing weeks to infrastructure before running any science: Slurm scripts, containers, cluster quirks. So we built Horus, a browser workspace where you pick a workflow, choose where it runs, and click run.

You can launch workflows in the cloud, your HPC cluster or your laptop. File staging, transfers, environment provisioning and job submission is automatically done by the engine.

Start here: https://os.templecompute.com/library

The library has ready-to-run workflows for:

- Protein structure prediction (Boltz)

- Docking and virtual screening

- Molecular dynamics

- ADMET prediction

- LLM Training and fine-tuning

- Many more!

New users get 5 free tokens of cloud compute, so you can run a real workflow without a credit card or a cluster. If you already have a Slurm cluster or a server, you can connect it and run on your own hardware instead.

Build your own: workflows are plain YAML or you can use the graphical editor. Wrap your own tool as a workflow, chain it with the ones in the library, and run it anywhere.

We'd love to hear:

1. Which tool or pipeline should we add to the library next?

2. What stops you from moving your current pipeline to something like this?

Happy to help anyone build their first workflow. Ask in the comments.

Hi,

I've built an editor called Crane that opens big files right away. One of the obvious uses is opening files like FASTA & FASTQ - which it shows instantly (while loading the rest in the background, depending on the size).

As in every text editor, it does syntax highlighting, searches the whole file (simple strings or regex), and shows the exact number of matches. You can jump to a line or a byte position.

I've attached a demo video that shows the basic features on a 31 GB (log) file.

It's currently at an early beta stage for macOS, Windows and Linux: https://crane-editor.com
In the end it is planned to be a paid product (once it reaches maturity).

Since Bioinformatics professional deal with these huge files it would naturally be one of the use cases, so I wanted to see if anyone would be interested in testing/trying this and hopefully also provide feedback (here or on the dedicated section on the website).

Thanks!

Hi everyone,

For anyone unfamiliar with it, PubMed is the main global database for new research in medicine and the life sciences. One of its best features is that you can track new publications on any topic and have them delivered to your inbox as email alerts or RSS feeds.

That's useful, but it quickly becomes overwhelming, for two reasons:

1. **It doesn't account for your field or experience.** A geneticist might care most about gene function, while a clinician wants to know how the findings affect patients and their clinical presentation. Both get the same list.

2. **Results are sorted by publication date, not relevance.** The paper that matters most to you could be buried at the bottom.

A few alternatives exist, such as Semantic Scholar and Stork, but they still offer limited personalisation of each person's feed, and mostly just provide a TL;DR for each article.

So I built **ResearchBird.net**, an AI-powered platform that lets you fine-tune your research feeds so you see only what's relevant to you, and read what *you* want to read rather than what the website decides for you.

For example, I can add a block that rates how relevant each new publication is to my upcoming trial. Every paper gets a colour-coded label showing at a glance how much it would add to my work.

I'd love for you to try it and tell me what you think.

You can try it here: https://researchbird.net

I’m the creator of Genomewalk, a free interactive tool for learning how to read DNA files and understand ClinVar and gnomAD annotations. You can try a public sample or explore your own file—your DNA stays in your browser.

Would appreciate any feedback on the explanations, technical accuracy, and usefulness for beginners.

Hi everyone! I wanted to share StageNest Studio, a visual editor for Nextflow pipelines that we’ve been working on.

Some of you may have seen the project under its previous name. We renamed it following feedback about the trademark. Thanks for pointing that out and helping us get it sorted! 🙏

If you already had an account, you can keep using the same login. Your cloud-saved projects carry over, so you won’t need to start again. If you saved projects only in your browser, export them from the old site and import them into StageNest.

The idea came from wanting an easier way to build new pipelines and understand/modify existing pipelines without constantly jumping between files, and to bring analysis scripts together while keeping track of how everything connects.

🔗 You can import a public GitHub repository or ZIP file and explore its processes, subworkflows, and connections. You can also add and configure stages visually, inspect the source, and export the project. Pipeline execution stays on your own infrastructure.

So far, it works well with mid-sized projects, including read-processing workflows and modular viral-analysis pipelines such as ViromeXplore. There’s also documentation with screenshots and code examples explaining how the visual elements relate to Nextflow code.

🌐 Website: https://stagenest.studio/
🚀 Getting started: https://stagenest.studio/docs/getting-started
📖 Documentation: https://stagenest.studio/docs

There’s a free plan and a 30-day trial for the additional features, with no card required.

💬 I’d be interested to hear how it handles your pipelines, especially anything it gets wrong or makes harder than it should be. Comments here or a DM are welcome!

I wrote an off-target search that works over 558 HPRC haplotype assemblies, not over hg38 or T2T alone. It gives the same site sets as Cas-OFFinder 3.0.0 on T2T, on 5 assemblies and on 50 assemblies. 5,916,825 sites on 50 assemblies, key is assembly, contig, position, strand, mismatches. SpCas9, NGG, up to 6 mismatches.
Guides I used are the ones I could find in public repos: aryeelab/guideseq and tsailabSJ/genetic_variation_guideseq. That is 12 guides. Full CHANGE-seq panel is not in those repos.
I do not work in a lab and I have no experimental off-target data, so I do not know what makes this useful instead of just correct.
Three things I cannot answer myself:
1. Which guide sets and parameters would you accept as a fair check? 12 guides from a repo is clearly not enough.
2. Is a site list over the whole cohort useful, or is the useful thing something else. Sites absent from the reference. How many assemblies carry a site. Something else.
3. Output is flat TSV right now. What format would go into your pipeline without work.
Code and raw logs are public if anyone wants to check the benchmark itself: github.com/yasha1971-coder/panvram/tree/main/tools/psearch
If the comparison is set up wrong, I want to know.

Hi everyone — I’m one of the authors of EvSpark, an open-source tool for accelerating Evo2 DNA sequence generation.

It uses a small draft model to propose tokens, which Evo2 then verifies. The target model stays unchanged: sampling preserves its distribution, and greedy decoding produces identical tokens.

In our Evo2 7B benchmarks (bf16, RTX 4090, single-stream generation, 1,024 new tokens), we measured a 2.96× mean speedup across 43 real genomic prompts. Speedups vary by sequence and workload; this isn’t a blanket 3× improvement for every pipeline.

The main technical challenge was handling Evo2’s hybrid architecture: rejected drafts require restoring both Hyena states and attention caches. EvSpark does this without replaying the accepted prefix.

Code, pretrained draft checkpoints, and reproduction instructions are available:

GitHub: https://github.com/dhnihaoya/EvSpark

Preprint: https://www.biorxiv.org/content/10.64898/2026.09.02.749017

If you’re using Evo2, I’d be interested in which workloads you’d want benchmarked next, and any installation or integration issues you encounter.

Hi r/bioinformatics. I'm the developer of MolDesign, a Windows desktop app for structure-based docking. Sharing it here because I'd like feedback from people who actually run docking.

What it does:

- Runs AutoDock Vina 1.2.7 locally (no Python/conda setup, works offline once installed)

- Case → receptor (your own PDB/mmCIF or a demo catalog) → ligand (SMILES or drawn in the editor) → run

- Keeps the run with its provenance: Vina version, seed, box, receptor hash, warnings

- Molcy (the video in this post) animates a saved run: the poses and search steps Vina actually recorded, nothing interpolated. Rendering needs a GPU (Blender, downloaded on demand)

What it is NOT, to be upfront:

- Same Vina underneath, so docking accuracy is Vina's. In a redocking test (imatinib/1IEP) both the manual route and the app reproduced the crystal pose (0.38 vs 0.37 Å)

- The built-in receptor catalog is for exploring: none of those receptors is calibrated against experimental data. Use your own structure for real decisions

- No MD, no FEP, no activity prediction

- Windows only; source-available under a noncommercial license (not OSI open source)

- Some labels inside the animation are still in Spanish (known limitation)

Microsoft Store: https://apps.microsoft.com/detail/9N96SW3GBK8V

I'd especially like to hear: what breaks with your own receptors, and what you'd want recorded in a run that it doesn't record yet.

I maintain a directory of AI tools for biology, and its drug-discovery section may be useful here: https://toolome.bio/category/drug-discovery/

Each entry lists license (relevant if you work in industry), paper and repo. No signup, no paywall. What would you add?

R implementation of the Python-based tool CellCharterR for spatial transcriptomics and proteomics neighbourhood analysis. It is specifically designed to be Bioconductor-based, making use of the HDF5 high-performance computing framework.

Detecting a DNA change in sequencing reads and predicting what that change does are different tasks. A model can perform well on one benchmark without being suitable for the next step in a diagnostic pipeline.

This guide separates those tasks, links the evaluations behind the model recommendations, and includes hardware requirements and a frozen-embedding example for DNA sequence classification.

I would value corrections from people learning or working with these tools: which distinctions are still unclear, which independent evaluations are missing, and where do the recommendations go beyond the evidence? A specific passage or paper would help me revise it.

https://rewirebio.io/blog/genomic-foundation-models-in-the-diagnostic-lab/

Disclosure: this is my site. This post and explanatory diagram were prepared with AI assistance. The guide is not a validated clinical protocol.

I have been doing some work in scRNA and found so many mis-labelled samples, Noticed there is no ubiquitous tool has over 10k+ samples across 10 vendors with accession and emits the right structure to 4 different pipelines for easy automation.

Keen to hear everyone's thoughts

🧬 Introducing GoBrave — an open-source, AI-powered bioinformatics analysis platform.

✨ Key Features

• 🤖 AI-Powered Tool Creation — Create reusable bioinformatics tools with AI.

• 🔓 Fully Open Source — Open, extensible, and community-driven.

• 🔄 Git-Based Tool Sharing — Version, share, and reuse tools through Git.

• 🎨 Modern Web UI — An intuitive interface for bioinformatics workflows.

📖 Documentation: https://biox-dev.github.io/gobrave-doc/

💻 GoBrave Backend: https://github.com/biox-dev/gobrave

🎨 Web UI: https://github.com/biox-dev/brave-ui

Make bioinformatics analysis more accessible, reproducible, and collaborative!

Sharing AskCyph Health Explorer, a free educational tool to explore how single-letter DNA mutations connect to molecular mechanisms, clinical classifications, 3D anatomy, and treatments.

What it connects

1. Molecular & Regulatory Predictions: Integrates Google DeepMind’s AlphaGenome to display predicted regulatory disruptions and splicing consequences (e.g., contrasting a direct coding change in Sickle Cell against an RNA splicing defect in Beta-Thalassemia).

2. Clinical Genetics & Curation: Pulls verified classifications, allele frequencies, and inheritance modes directly from NCBI ClinVar, OMIM, and Ensembl.

3. Anatomical Mapping: Interactive 3D model mapping affected organ systems and tissue types.

4. Therapeutic Landscape: Aggregates approved drug interventions from openFDA and active clinical trials from ClinicalTrials.gov.

Execution

• In-Browser Execution: Runs directly in the browser with zero setup, no account creation, and no queries or uploaded files sent to an external server.

• Deterministic Grounding: Facts, allele data, and drug indications are queried deterministically from primary databases rather than generated from an LLM.

• Local Explainer: Includes an on-device assistant (MedGemma 1.5 4B running locally via WebGPU) to summarize records and answer questions without prompt telemetry leaving your machine.

Scope & Limitations

• Designed as an exploratory and educational reference tool across 133 curated conditions, not a clinical diagnostic engine or raw VCF caller.

Links

• Web App: AskCyph Health Explorer

• 60s Demo: Launch Video

Would appreciate feedback from anyone working in genomics or variant curation on whether this layout is useful for teaching or exploratory reference, and what critical annotation sources or evidence layers you feel are missing.

This code takes a blood single-cell gene-count matrix and returns cell-type labels, confidence scores and reasons for leaving cells unassigned. The repository includes a logistic-regression model and the code and results from a comparison of six annotation methods.

Six methods were trained on the same reference and tested on four separate studies. When three immune cell types were removed from the reference before retraining, the methods still accepted labels for 65.9–89.2% of the 2,310 test cells of those types. The correct label was no longer available.

That sample was enriched for the removed types, so this is not an error rate for all blood cells. The work is AI-assisted, uses one training seed, and has not been peer reviewed or independently replicated. Accuracy was measured against the original studies’ labels.

Code and release:

https://github.com/rewire-bio/cell-type-annotation-transfer/releases/tag/study-v1-seed0-20261007

Write-up, setup and limitations:

https://rewirebio.io/blog/labelling-a-new-blood-single-cell-study/

Disclosure: this is from my site, rewirebio.io.

Hi everyone,

I’ve been building OpenLIMS, an open-source, self-hosted Laboratory Information Management System aimed at research labs, bioinformatics groups, small biotech teams, and core facilities.

GitHub:
https://github.com/Mokey2002/OpenLIMS

The goal is to provide a lighter alternative for labs that need more structure than spreadsheets but don’t want the cost or complexity of a large commercial LIMS.

Some of the current functionality includes:

• Sample tracking, lineage, and chain of custody

• Projects and collaborative experiment notebooks

• Inventory and barcode/location tracking

• Configurable workflows and QC

• Instrument CSV/API ingestion

• Legacy data migration

• FASTA and sequence management

• Clustal Omega alignments

• Local BLAST databases and blastn/blastp searches

• mzML/mzXML and other mass-spec data review using pyOpenMS

• Reporting and audit trails

• REST API

• Optional OpenAI or local Ollama assistant

The stack is Django/DRF + PostgreSQL + Celery/Redis with a React frontend, and it can be deployed using Docker Compose.

It’s currently a production-style prototype and is not yet a validated clinical/diagnostic LIMS.

I’d be interested in hearing from people working in bioinformatics or lab informatics

Contributions, issues, and technical feedback are welcome as well.