Hi All! 
Welcome to the Community forum for AlphaGenome. This is your space to discuss the model and the API, and make connections with other users. By way of introduction: I’m Clare Bycroft, one of the research scientists behind AlphaGenome. I specialise in human genetics 
, and look forward to your thoughts and feedback on AlphaGenome. Excited to see what you all do with it!
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Hi! I’m Adam and I’m another research scientist behind AlphaGenome. My background is in genomics and machine learning. I’m looking forward to reading your posts and learning how you all use AlphaGenome.
Please feel free to introduce yourself below!
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Hi Clare & Adam, I am Aastha, a MS Bioinformatics & Computational Genomics student from the UK. Thanks for this community; while I am still in my learning phase, would definitely love to learn more about AlphaGenome and its usecase. Looking up to people like y’all 
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Hi! I am Shishir, Prof. Dr. Shishir Urdhwareshe (Ophthalmology) in India. I am interested in epigenetics and exploring AlphaGenome for it. Looking forward to any ideas, suggestions regarding this
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Hi all, Natasha here, another genomics team member and AlphaGenome co-author Looking forward to learning more about your use cases and research questions!
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Hi Everyone! My name is Josh, and I’m a recently-joined computational biologist on the AlphaGenome team. My favorite AlphaGenome activities are: enhancer to gene regulation, in-silico mutagenesis, and matching predictions to ground truth experiments from the literature. Great to meet everyone!
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Hi all, my name’s Tom and I’m one of the engineers behind AlphaGenome 
Really looking forward to hearing about all the wonderful ways you use our API. Cheers!
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Hi Everyone, my name is Arnold and I have a Ph.D. in genomics and live in Uppsala, Sweden. My favorite field is synthetic biology and I hope to do some cool research with AlphaGenome!
Great to meet everyone!
Hi everyone, I’m Cam, and I have absolutely no formal education; a week ago I could hardly tell you the difference between a gene and a genome.
I read about AlphaGenome and was inspired to learn more, and as I’ve been stumbling my way through learning, I’ve been building out a tool that enables anyone to easily access and use the AlphaGenome API through a really cool GUI while expanding the possibilities of how it can be used by more people, even those like myself without any experience. I’ll be open sourcing it on Github later this week, and hope to share it here with you all when it’s ready
I posted a short video demo of the UI over on X if you’re interested: https://x.com/CameronDWills/status/1939197173360648498
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Hi! I am Pietro, MSc in Med Biotech and 2nd year PhD student in Bioinformatics at the University of Ferrara, Italy. Looking forward to trying AlphaGenome in my oncology research!
Cheers!
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Hi there! I’m Sufyan, and I’ll be applying for undergraduate study in the UK for 2026 entry. I’ve been deeply inspired by DeepMind’s work in biology, especially AlphaGenome.
My goal is to one day work on projects like this, using AI to tackle fundamental problems in biology. Since it’s such an interdisciplinary area, I’m unsure what the best undergraduate path is to build toward that. I’d really appreciate any advice on how to approach this decision. Thank you!
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Hi all! I’m Claus, an MD, MSc in computational intelligence, and BSc in medical informatics. I’ve been following DeepMind for a while now and have been intrigued by AlphaFold. Now I’ll explore AlphaGenome and take a look at its source code - maybe I’ll find something that could be improved.
Sufyan: You will have to acquire knowledge by autodidactical learning no matter what field of study you pursue. The undergraduate degree is just something like a brand that looks good in your CV, but I have 10 years of experience as a professional software developer by now and close to 100% of what I’m doing is based on skills I’ve acquired through self-study. The best advice is: Never stop learning.
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Hi everyone! My name’s Gabriel Duarte! I’m a pretty new scientist at the Wexner Medical Center. I started out my career last year after graduating in the ML/DL field last year, trying to solve some problems in biomedicine. I think what you guys are doing is great! I’m so happy for you!
I’m currently trying to use AlphaGenome to see if we can build disease-signatures for neurodegenerative diseases in a more sophisticated way than we have today! If somebody is also working on the field, let’s connect!
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Hi Adam.
AplhaGenome is a crucial discovery, and it’s shaping the world.
Im also trying to use alphagenome
Hi! Clare Bycroft. Iam interested to learn more. Particularly on Pharmacogenomics.
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Noted, thanks! If anyone has ideas on a suitable degree path, I’d love to hear.
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Hi, Gabriel, it’s nice to know what you’re doing. I’m exploring AlphaGenome the same way as you do, but trying to understand genetic ophthalmology diseases. Let’s connect.
Hello, folks, it was great to meet with AlphaGenome team at ashg-2025.
I am Marianna Weener, and I am a researcher at Mass Eye and Ear, Harvard Medical School working on intronic variants pathogenicity testing in vitro.
As we discussed at #5034F Ocular Genomics Institute (OGI) poster, non-coding variants which alter mRNA splicing are increasingly recognized as important contributors to the genetic causality of Mendelian disorders such as IRDs (~30%). For example, intronic variants (IVs) that activate cryptic exons (CEs) contribute to the genetic cause of disease for significant portion of IRD cases, yet it remains challenging to predict intronic variant pathogenicity accurately. To improve prediction of intronic variant pathogenicity, we used a high throughput splicing assay (HTSA) to measure the effects of rare IVs and variant library (VL ~5000 variants) of 300 bp length was generated and cloned into the HTSA plasmid which contains a minigene composed of an intron flanked by 5’ and 3’ split GFP exons. The plasmid VL was transfected into 293HEK cells, and 48 hours later total RNA was extracted from the transfected cells. RNA transcripts produced by HTSA minigene were amplified by RT-PCR and sequenced. The intron sequences spliced between the two GFP exons were identified and quantified.
992 deep intronic variants (>50 bp from the nearest exon) and 4056 periexonic variants (<50 bp from the nearest exon) were chosen from patients and experimentally evaluated using stable transfection with subsequent FACS sorting. Periexonic variants experimental results served as control for deep intronic variants evaluation. Performance of the top 4 best-performing classifiers evaluated for autosomal recessive inheritance pattern. We found the AlphaGenome tool to be very informative and useful; however, we noticed some discrepancies, particularly in the pathogenicity predictions of deep intronic variants—both false positives and false negatives. While many recent studies present newly developed algorithms for variant pathogenicity prediction, there remains a clear lack of robust experimental validation. Our goal is to help bridge that gap.
We understand that you may have thousands of potential collaborators aiming to improve variant predictions across different tissues. We can contribute RNA-seq data from ocular tissues. How can we ensure that the dataset is being used in a biologically accurate way? Would it be beneficial to have a retina expert on the team to help guide the appropriate use of this data? Additionally, could you share what type of training data you are currently using, and how it might be improved or complemented with our cases?
We would also like to report interesting findings or validated results as we identify them—potentially as a starting point for collaboration, now or in the future.
We will be happy to discuss with you best next steps.
Hi everyone! My name is Amanda and I’ll be working with the AlphaGenome team to build a great community around the tool; I’ll be gathering your feedback and ideas, and helping make sure we are shaping AlphaGenome in a way that truly supports all your amazing science! I have a PhD in Computational Genetics, and have a particular interest in the co-evolution of chromosomal alterations and chromatin architecture.
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