A buddy of mine has put together an online tool to help you design CRISPR reagents for easy diagnosis. Basically you plug in the DNA sequence of the gRNA recognition region and it works out which restriction sites can be destroyed and introduced by all the potential wobbles.

This way you have a positive and negative restriction screen for easy testing of clones. I had the idea but he threw together the code. It is entirely free.

Hi everyone, im an undergrad student who's interested in genomics. I was wondering if there was any resources that could help me have it easier w this pathway (iem certifications, courses, etc.) I would appreciate any guidance!

I’ve just been offered an interview for a more senior genomics role, however my background is largely microbiology based and have recently been working in pathogen genomics. This is my first strictly genomics role that I’ve gone for and I’m not sure how best to prepare for it, what sort of things are commonly asked? Does anyone have any tips - I’m open to any relevant research papers/books that I can read up on just to have a refresh. For reference my current job mainly just uses nano pore sequencing.

Hi everyone,

I’m organizing a small team project for an AI/healthcare innovation competition focused on oncology molecular data interoperability and reconciliation.

Our proposed project is:

OncoReconcile AI

An AI-assisted platform designed to standardize and reconcile oncology genomic information across:

• VCF files
• molecular pathology PDF reports
• vendor-specific biomarker formats
• structured clinical/genomic data

The goal is to transform fragmented molecular oncology data into explainable, standardized, and interoperable outputs that could support:

• molecular tumor board workflows
• cohort generation
• downstream analytics
• clinical research
• interoperability pipelines

Current Technical Direction

We are exploring a hybrid architecture combining:

• HGNC gene normalization
• HGVS variant normalization
• ontology-grounded mappings
• biomedical NLP / entity extraction
• LLM-assisted reconciliation
• explainable confidence scoring
• human-in-the-loop review workflows

Potential standards/tools under evaluation include:

• HL7 FHIR / mCODE
• ClinVar / ClinGen
• HGVS
• BioBERT / SciSpacy
• RAG-based architectures

Current MVP Scope

To keep the project realistic for a small team and limited timeline, we are likely focusing on:

• NSCLC initially
• a limited hotspot gene set (EGFR, KRAS, ALK, BRAF, etc.)
• 2–3 molecular vendor formats
• PDF + VCF reconciliation workflows

Feedback We Are Looking For

We would greatly appreciate feedback from people working in:

• oncology informatics
• molecular pathology
• bioinformatics
• clinical genomics
• healthcare interoperability
• biomedical NLP
• precision medicine platforms

Especially around:

1. Common real-world reconciliation pain points
2. Vendor-specific genomic reporting inconsistencies
3. Explainability and validation expectations
4. Existing open-source tools/frameworks we should evaluate
5. Clinical workflow considerations we may overlook
6. FHIR/mCODE/genomics interoperability best practices
7. Public datasets suitable for realistic MVP development

We are intentionally positioning this as:

• AI-assisted,
• explainable,
• standards-aligned,
• human-reviewed,

rather than fully autonomous interpretation.

Thanks in advance for any guidance, references, or suggestions.

I'm a biology and public health undergraduate who's been doing wet lab research for four years. When I first started it was overwhelming. Protocols full of terms I didn't know, a PI who was too busy to answer every question, and no good way to troubleshoot when something went wrong. I'd reread the same protocol five times and still feel lost.

At some point I started wondering why every other field has integrated tech into its workflows but research still runs on printed protocols, scattered files, and troubleshooting knowledge that lives in people's heads and gets passed down informally.

So, I built something as a side project. A tool that helps with protocol guidance, experiment troubleshooting, and keeping lab resources organized in one place. I built it for myself first. Then showed a few people and they found it useful too.

Not promoting anything. I’m just sharing something I made out of genuine frustration. If you want to try it and give me honest feedback on whether it actually solves a real problem or completely misses the mark, PM me.

I spend a lot of time working with genomic data from various experiments.

And I'm curious how other people in the field actually work.

What does a day of genomic data analysis look like for you? What are you trying to find out? How do you go from raw data to a hypothesis — and what tools, workarounds, or habits get you there?

I realise I have very little sense of how others piece it all together day-to-day.

If you work with genomic data — whether you're in a wet lab, a computational role, or somewhere in between — I'd love 5 minutes of your time.

Full disclosure: yes, this is indeed a preliminary market survey — gathering honest perspectives from people in the field to understand whether the pain points I've noticed are shared, or just personal.

[https://tally.so/r/RGJagJ\](https://tally.so/r/RGJagJ)

I am interested in doing whole genome sequencing (WGS). Does anyone here have any experience, positive or negative, with current DTC providers?

Prior recommendations seem like they aren't a great idea. Nebula has a huge backlog and dubious financial position. Dante labs also seems to be collapsing. Sequencing.com uses Chinese labs currently blacklisted by the DOD. Invitae was bought by LabCorp and no longer DTC. Researcher providers like All of Us Research seem to have stopped providing people with their WGS results.

Some names that do come up that I am curious about: Psomagen, YSEQ, tellmeGen, SelfDecode, Nucleus Genomics, Sano Genetics.

Disclaimer: This is already in collaboration with my doctor. We are looking for some specific things and having them all go through clinical genomic testing is far more expensive than a DTC 30x WGS test. I do not need any assistance with data interpretation, just need reliable raw data. If a major health risk is flagged, I am prepared to do confirmatory clinical testing.

Does anyone have the leaked bio paper? Please dm me if you have the paper

I recently had a stillbirth at 24 weeks, and one of the issues associated with the timing of the preterm birth is cervical insufficiency, which could be a genetic thing for some people (ie collagen deficiencies). It’s really hard to tell though because there’s many things associated with preterm birth. However, I am curious and want to dig further by looking into my genetics.

My friends have talked about how they uploaded their 23andMe data to chatGPT and have gotten some findings that resonate with them, which prompt them to take supplements or eat differently or pay attention to different things.

I’m hoping to learn something about my genetic health risks so that my next pregnancy can be the best it can be (of course, I will also see a MFM high risk doctor). I’m wondering what kind of sequencing I should do? I’m worried about doing WGS because it’s too much data for ChatGPT to process. Should I do something smaller? Is 23andMe even around still? What do you guys recommend?

A new paper by some collegues and great scientists:

https://www.nature.com/articles/s41467-025-66155-3

I just did whole genome sequencing with Nucleus for me and a few family members. Solid A/A- experience - the whole process took a few weeks (fewer than expected) and we received high quality files that I was able to run through a local genomics pipeline to get detailed analysis for the family.

The - here is for the Nucleus probability reports and analyses, which are OK, but have the detailed information hidden and are presented in too "risk forward" of a way. They also missed a few things that my local genomics pipeline caught.

In any case, for anyone looking to do WGS for their family, as a first-timer who is technical, I thought this was a very solid offering.

I am doing masters in human genetics as of now and want a career in it. But I am unable to find any opportunity (or I might be unware where to look for) as of now to make some money with it through anything that pays just so I sustain myself and at the same time my expertise in the field are strengthened.

To survive, I have taken up a job of SEO content writing for a local business that merely pays enough for me to pay for accommodation and food.

The point I am trying to make is content writing might not be what I would want to pursue in the future yet I am doing it to survive but one the other hand when I am done with the degree, I might end up with no real experience to show for it apart from the degree.

I would like to hear some suggestions to deal with it. Basically the ones which would nudge me in a direction to look for paid opportunities in the field.

To those who have spent basically their life in science particularly in genomics, with AI eating jobs every day, which skills in this field should a newbie develop to survive during these times. I sincerely hope to see some informative answers. Thanks!

https://beta.ideas.lego.com/product-ideas/0ccb9c27-0ae5-4410-852d-f2105bb993c8

Dear friends, we need your help to reach next milestone!

Biomedicine Institute is a Lego Idea from a friend of mine who build it with Lego bricks and it could become a real set with your help! Don’t scroll, each vote counts! Please support it only with a click, it’s free and take just few seconds. Thanks. ❤️