FoG2025London: From a Perspective
I will be sharing my notes and summaries from the talks I attended at the conference (before they start to be embedded somewhere less accessible in my memory about the event). Shared here first.
What Was About It (#FoG2025London)
It covers many aspects of drug discovery/development, genome-editing, rare diseases, single-cell, microbes, sequencing, and transcript/epigen/gen/prote/metabol/metagen/pharmacogen/spatial/multi-omics generating/storing/managing/using/distributing the big data. You can find more details on the event webpage (https://festivalofgenomics.com/london/en/page/2025-homepage).
As you might guess from the topic range and industrial partnerships, the talks are usually prepared for a broader audience and are less detailed (if you know ISCB, HIBIT-like computational biology-focused conferences, it is less intense in terms of the computational nitty gritty side).
Moreover, there are a lot of companies sponsoring the event and making it free for academic researchers. Therefore, you must also be prepared for product application-oriented talks (which might be useful if you are looking for a certain type of product for your research and follow the trends with improved techniques).
Apart from this, I appreciated the clinical focus and patient-oriented talks (from patients or take-carers) that reminded (curiosity-driven) us of the most valuable aspect of biology research and encouraged us to make an invaluable difference in life.
Tip: If it is your first time attending such an event, be careful about time management. You cannot be in multiple places at the same time. Whether it is a minute decision or long-planning, take a pen or open an event planner on the phone and choose the sessions based on your interest. If only the title of the talks given, then try to google the speaker’s name and see whether the background and title matching are relevant for you. There might be a recording available, however, if you have a genuine interest and some of your questions are to be answered, give priority to that one.
I will share mostly the research/technical-oriented perspective of mine (from selected talks I attended) here.
Day 1
Constellation: Revolutionising The Approach to Rapid Genome Sequencing in Acutely Unwell Children
It covered the clinical applications of rapid-genome sequencing. Illumina representative described the PCR-free 3-step Whole Genome Sequencing (WGS) approach. For more details, you can read the product page: https://www.illumina.com/content/dam/illumina-marketing/documents/products/appnotes/illumina-dna-pcr-free-wgs-app-note-770-2020-006.pdf
He also mentioned the game-changer effect of the “constellation technology” based on the recovery of ambiguous reads to improve read mapping. You can find the details here; https://www.illumina.com/science/genomics-research/articles/constellation-mapped-read-technology.html
Putting The UK at The Forefront of Genomics and Precision Medicine
It is good to know NHS Southwest Genome Medicine Service can check >6000 rare conditions in a test.
Illumina mentioned how the constellation tech could be useful for various conditions here (e.g., a patient with only single parent genome availability, a genome stored >10 years ago).
Advancing Genomics & Health Through Data
Tim Hubbard gave an introduction to ELIXIR (https://elixir-europe.org, e.g., database infrastructure, training programs for collaborators, establishing strategies, diverse species, and federated structure for each country for European Genome Archive-EGA to eliminate data-privacy concerns across borders).
Enhancing the Nextflow Experience: Boosting Efficiency, Scalability, and Best Practices for Bioinformatics Workflows
Geraldine’s talk was helpful for newcomers to pipeline development and understanding the effective role of the LLM revolution (a.k.a. some popular AI chatting tool algorithms) for faster processing. (more info: https://www.nextflow.io)
She started with the challenges of good pipeline frameworks of big biodata, such as dealing with large files, the requirement of multiple coding languages, and complex algorithmic tasks. Then, she continued with the emphasis on readability, stability, portability, and reproducibility and improved these by simply using nf-core pipelines. (more info: NextFlow open-source community: https://nf-co.re).
She mentioned the LLM-integrated AI assisting Nexflow System (NextFlow AI assistant: https://seqera.io/nextflow/) that not only trained based on solid nf-core pipelines but also has an automated testing/debugging and correcting feature for the relevant pipeline of interest. (for those interested, you can check out the VScode extension for hands-on coding as well)
Accelerating Drug Discovery Through the Power of Genomics, Machine Learning, and Medical Big Data
Boehringer Ingelheim’s representative shared examples of how to effectively use machine learning (ML)/deep learning based on large-scale, multi-modal, and longitudinal genomic data for drug discovery.
Identification and Validation of Non-Coding Pharmacological Target
AstraZeneca’s perspective of using non-coding targets and taking advantage of them (e.g., low abundance requiring low dose treatments) was interesting. Although there were structure and expression challenges in targeting non-coding, they took advantage of interactions (such as RNA-DNA) for identification in the proximity (more info about RADICL-seq; https://www.nature.com/articles/s41467-020-14337-6).
NAT antisense transcripts in the context of therapeutics using assisting oligo (ASO) were discussed.
Designing Efficient, Manufacturable & Safe mRNA Therapeutics
Tobias von der Haar discussed the different usage of mRNA formulations (e.g., antigens, antibodies, secreted proteins, or intracellular proteins) and how to design mRNA-based therapeutics for this considering yield, manufacturability, off-target effects with modifying Untranslated Region (UTR) and ORF (Open Reading Frame).
Integrative Analysis of RNA-Protein Interactome Dynamics with Different Datasets Reveals Enzymatic Insights
Dr. Guber mentioned two aspects of RNA-protein interaction research: RNA or protein-centric. Protein-centric approaches were summarized as RIP, CLIP, PAR-CLIP. RIP was used as an RNA-centric approach. Particularly, mRNA-binding RNA Binding Proteins (RBPs) were discussed (a.k.a. mRBPs).
He mentioned that these mRBPs are evolutionarily conserved, pointing to functional importance across species (more info: https://pubmed.ncbi.nlm.nih.gov/26595419/). Further investigations revealed that there is a competition between mRBPs and enzymatic activity, indicating mRBPs’ role in modulating fast response to change.
Day 2
The Rare Disease Multiomics Programme at Genomics England
I attended this talk to learn more about rare disease programs. However, I learned more than that! Although I had been using Illumina sequencing (such as Miseq) during my PhD, I realized that I was not aware of some of their products in other omics stages such as SOMAmer technology for proteomics (more info: https://www.illumina.com/products/by-type/sequencing-kits/library-prep-kits/protein-prep.html).
SOMAmer is oligonucleotides folding into different shapes/secondary structures binding to proteins. It looks like they are using barcoded SOMAmers for the identification of unique proteins up to ~6k (for now) following sequencing for easy workflow. If you are looking for a Mass Spectrometry (MS) alternative for high throughput (HT) protein detection, I think it is worth checking it out.
Enabling Innovation with FAIR OMICS Data Management: A Boehringer Ingelheim Perspective
I think Vitaly’s talk was very well delivered. He mentioned how costly and time-consuming it is not to be “FAIR” (https://www.go-fair.org/fair-principles/) in the context of big biodata for a company. (for research and advancement as well!)
His emphasis on a “solid data foundation” requires FAIR data management to be fair. The omics data management repository structure was described in 3 categories: metadata with knowledge graphs, large files with cloud-based systems, and derived results in the data warehouse.
Single Cell Sequencing of Bacterial Populations
I participated in this talk just for fun. It turns out that single-cell sequencing is becoming popular for bacteria as well.
As Matt explained, these are very small cells and mostly ribosomal RNA and no particular poly-A dominated, so requires further fine-tuning of the established protocols. Nevertheless, they accomplished FACS-based single-cell isolation following individually sequencing the single clonal population.
After Barynard experiments, error-filtering using bulk-seq as a reference, and further confirmations of sequencing went well, they drew the phylogeny of this individual bacteria of a single clone. Surprisingly (or not), they found heterogeneity and could generate the population structure of a single clone. They further confirmed whether cell number is enough to identify variance using hypermutator sampling. It might be helpful to uncover driving small bacterial populations in resistance or certain conditions. (more info: https://pmc.ncbi.nlm.nih.gov/articles/PMC9676037/)
Advancing Rare Disease Research With Omics Technologies
I learned about NIHR Biosource for rare disease research (https://bioresource.nihr.ac.uk), hosting transcriptomics, genomics, epigenomics, proteomics (and metabolomics?) data.
There were genome, single-cell, and spatial-omics-focused domes for improved smaller group interactions and chats. I could not join them as the space was not big, and there was hype on the topics.
Overall, I enjoyed and learned a lot. I would like to thank everyone for making this event possible. #FoG2025 #FoG2025London
The most disappointing part could be the visa requirement to be able to join this conference in UK. I know a lot of brilliant colleagues who wanted to attend. However, they could not join due to visa and it’s fees. I hope there are also more solution-oritented policies on FAIR conference opportunities (not just data management and sharing).
I am looking forward to attending the next exciting conference!
Fun fact: This was my first time attending the Festival of Genomics (FoG). I realized that I was the only researcher(academic) from Cork(Ireland).
BONUS: Brian Cox was the final speaker. I did not know there were two different approaches to space exploration by super rich. You can google more how Musk versus Bezos approach this.. The discussion about “what is time” was surely fun. However, the discussion about the possibility of life in different formats as an example about chirality of nucleic acids, the foundation of the discussion, was a bit misleading. I am not the expert, but technically they exists in either one of the formats dominating (right or left) in different contexts/nucleic acids (e.g., only left for one type of nucleic acid and only right for the other but not always right or left for all, and possibly related to magnetism). If you have ever read any of Dr. Ozturk (Harvard Kavli Prize winner) papers about the topic, you might have a better idea.
