Bioinformatics Seminar

How can multimodality improve representations of proteins? Foundation models have shown promise in building powerful representations for many domains. Language models are able to access a vast quantity of human knowledge and are able to perform limited reasoning over this body of knowledge. Protein models learn the evolutionary patterns in proteins, enabling prediction of protein structure and function. This talk will cover the development of protein foundation models, understanding the representations they build, and how they scale. Finally, it will cover incorporating modalities beyond protein sequences, and how additional data could be added to produce better representations in the future.

Sequencing data analysis often begins with aligning reads to a reference genome, where the reference takes the form of a linear string of bases. But linearity leads to reference bias, a tendency to miss or misreport alignments containing non-reference alleles, which can confound downstream statistical and biological results. This is a major concern in human genomics; we don't want to live in a world where diagnostics and therapeutics are differentially effective depending whether and where our genetic variants happen to match the reference.

Fortunately, computer science and bioinformatics are meeting the moment. We can now index and align sequencing reads to references that include many population variants. I will present some of the major and insights that have shaped this journey from the early days of efficient genome indexing -- especially the Burrows-Wheeler Transform -- continuing through recent methods for indexing graph-shaped references and references that include many genomes. I will emphasize recent results that show how to optimize simple and complex pan-genome representations for effective avoidance of reference bias. Finally, I will outline promising methods for the bias, including new ideas for how to measure bias, new proposals in compressed indexing, and new workflows that integrate genotype imputation to improve reference bias.

Darwinian evolution proceeds by natural selection acting on random variation. I will argue that, although mutations are random, the novel phenotypes they produce can be highly biased towards simple or more compressible forms. This bias is so strong that it can dramatically shape the spectrum of adaptive outcomes. The basic intuition follows from an algorithmic twist on the infinite monkey theorem inspired by the fact that natural selection doesn’t act directly on mutations, but rather on the phenotypes that are generated by developmental programmes. If monkeys type at random in a computer language, they are much more likely to generate outputs derived from shorter algorithms. This intuition can be formalised with the coding theorem of algorithmic information theory, predicting that random mutations are exponentially more likely to result in simpler, more compressible phenotypes with low descriptional (Kolmogorov) complexity. Evidence for this evolutionary Occam’s razor can be found in the symmetry in protein complexes [1], and in the simplicity of RNA secondary structures [2], gene regulatory networks, leaf shape, and Richard Dawkins’ biomorphs model of development [3]. This principle may also extend to machine learning, offering insights into why neural networks generalize well on typical datasets [4].

[1] Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution, IG Johnston, et al, PNAS 119 (11), e2113883119 (2022);
[2] Phenotype bias determines how RNA structures occupy the morphospace of all possible shapes, Kamaludin Dingle, Fatme Ghaddar, Petr Sulc, and Ard A. Louis. Molecular Biology and Evolution, 39, msab280 (2021)
[3] Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’s biomorphs, NS Martin, CQ Camargo, AA Louis PLOS Comp. Bio. 20 (3), e1011893 (2024)
[4] Do deep neural networks have an inbuilt Occam's razor? C Mingard, H Rees, G Valle-Pérez, AA Louis arXiv preprint arXiv:2304.06670

In the last decade there has been a data revolution in biology with the advent of high-throughput high dimensional data modalities such as single-cell RNA-sequencing, fMRI data, molecular structure data and other modalities. A key issue in these data types is that they provide static snapshots of highly dynamic biological entities. In this talk I will cover our work inferring and characterizing cellular and neural dynamics during various processes. First, I will cover how to infer cell state dynamics during differentiation and disease with a neural ODE framework called MIOflow that is regularized with data geometric and manifold priors. Then I will discuss RITINI, our recent graph ODE network which allows us to learn gene regulation that underlies cellular dynamics, and potentially find new targets for treatments of disease. I will showcase applications of these in triple negative breast cancer and human embryonic stem cell differentiation. Once these dynamics are available, I will showcase tools to quantify and classify these dynamics based on graph signal processing and topological data analysis. This will involve our learnable geometric scattering transform to capture spatial signal patterns, as well as persistence homology and other tools to quantify time-varying patterns. Applications to characterization of brain activity data will be presented.

The quest to understand the interplay between evolution, genes and traits has been revolutionized by the collection of rich phenotypic and genetic data across millions of individuals in diverse populations. However analyses of these Biobank-scale datasets present substantial statistical and computational challenges.

I will describe how we bring together statistical and computational insights to design accurate and highly scalable algorithms for a suite of problems that arise in the analysis of Biobank data: highly scalable randomized inference algorithms to dissect the genetic architecture of complex traits and deep-learning based phenotype imputation to deal with complex patterns of missingness. By applying these methods to about half a million individuals from the UK Biobank, we obtain novel insights how genetic effects are distributed across the genome, the relative contributions of additive, dominance and gene-environment interaction effects to trait variation, and new genes that confer risk for hard-to-measure diseases.

Deep generative models are increasingly powerful tools for the in silico design of novel proteins. Recently, a family of generative models called diffusion models has demonstrated the ability to generate biologically plausible proteins that are dissimilar to any actual proteins seen in nature, enabling unprecedented capability and control in de novo protein design. However, current state-of-the-art models generate protein structures, which limits the scope of their training data and restricts generations to a small and biased subset of protein design space. Here, we introduce a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural sequence and functional space. Critically, EvoDiff can generate proteins inaccessible to structure-based models, such as those with disordered regions, while maintaining the ability to design scaffolds for functional structural motifs, demonstrating the universality of our sequence-based formulation. We envision that EvoDiff will expand capabilities in protein engineering beyond the structure-function paradigm toward programmable, sequence-first design.

Data Science is central to AI and has driven most of the recent advances in biomedicine and beyond. Human judgment calls are ubiquitous at every step of the data science life cycle (DSLC). We will introduce Veridical (truthful) Data Science (VDS) based on three core principles of data science: Predictability, Computability and Stability (PCS) to formally take into account the human judgment calls as sources of uncertainty. PCS will be showcased through collaborative research in prostate cancer detection and in seeking genetic drivers of a heart disease. We will end with on-going research on PCS uncertainty quantification (UQ) that addresses two unconventional prominent sources of uncertainty in the DSLC from data cleaning and algorithm choices.

In 2022, roughly 20 years after the conclusion of the Human Genome Project, we were finally able to complete the last 8% of the human genome that had been missing from all prior assemblies of the human genome. Our complete, gapless, “telomere-to-telomere” assembly revealed over 200 Mbp of novel sequence, comprising some of the most repetitive and structurally variable regions of the genome. In addition to new methods for genome sequencing and assembly, these regions have also required new methods for sequence alignment, annotation, and analysis that account for their unique evolutionary properties. I will cover some of the key algorithmic details that have now enabled the routine assembly and analysis of complete human genomes, and the new biology we are uncovering.

Assessing how alterations in DNA control disease progression and overall cellular function is a core component of cancer biology and has largely driven how we search for and assign therapies. The advent of single-cell RNA-sequencing (scRNA-seq) has reshaped our understanding of human cancers by revealing that tumors are complex systems of interacting cells and exhibit substantial variation in transcriptional states in addition to mutational heterogeneity. Despite the generation of many high-resolution and multimodal single-cell atlases of cancer, we still have a limited understanding of the relative importance and functional consequences of cell state diversity in human malignancy. Addressing this problem is equal parts biology and computer science. In Project Ex Vivo, a joint cancer research collaboration between Microsoft Research and the Broad Institute, we are leveraging the knowledge within a diverse group of computer scientists, experimentalists, clinicians, and computational biologists to better understand the complexity of cell state phenotypes in cancer. I will discuss our efforts to build AI models to better define, model, and therapeutically target cell states in cancer.

Accurate interpretation of medical images is crucial for disease diagnosis and treatment, and AI has the potential to minimize errors, reduce delays, and improve accessibility. The focal point of this presentation lies in a grand ambition: the development of 'Generalist Medical AI' systems that can closely resemble doctors in their ability to reason through a wide range of medical tasks, incorporate multiple data modalities, and communicate in natural language. Starting with pioneering algorithms that have already demonstrated their potential in diagnosing diseases from chest X-rays or electrocardiograms, matching the proficiency of expert radiologists and cardiologists, I will delve into the core challenges and advancements in the field. The discussion will navigate towards the topic of label-efficient AI models: with a scarcity of meticulously annotated data in healthcare, the development of AI systems capable of learning effectively from limited labels has become a key concern. In this vein, I'll delve into how the innovative use of self-supervision and pre-training methods has led to algorithmic advancements that can perform high-level diagnostic tasks using significantly less annotated data. Additionally, I will talk about initiatives in data curation, human-AI collaboration, and the creation of open benchmarks to evaluate the generalizability of medical AI algorithms. In sum, this talk aims to deliver a comprehensive picture of the state of 'Generalist Medical AI,' the advancements made, the challenges faced, and the prospects lying ahead.

Some human viruses including SARS-CoV-2 and seasonal influenza evolve rapidly to erode antibody immunity. I will discuss how new high-throughput experimental techniques including deep mutational scanning and sequencing-based neutralization assays can be used to understand and to some extent forecast this evolution.

Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. Can this information be used to identify a person uniquely? If it really is a personalised medical record, can it inform us about the outcomes of a COVID-19 infection? I will show how statistical analysis of immune repertoires sequencing experiments can answer these questions.

Protein language models (PLMs) are powerful tools for extracting information from large natural protein sequence databases. By learning from the manifold of natural proteins, these models are able to learn structural and functional properties in an unsupervised manner, making them powerful foundation models for protein structure and function prediction. However, these models become increasingly impractical as they scale to more and more parameters, need to be retrained to incorporate new data, and generally lack controllability. In this talk, I'll discuss PoET (the Protein Evolutionary Transformer), a fully generative model of whole protein families as sequences-of-sequences. By reformulating protein language modeling as a family-level, rather than individual protein-level, generative problem, PoET learns to extract evolutionary signatures from extremely small numbers of example sequences to generate novel proteins and model fitness distributions. Through controlling this sequence context, PoET can be prompted to generate proteins from any target distribution of interest while benefiting from learning general principles across the entire natural protein landscape. This enables PoET to achieve state-of-the-art performance for zero-shot variant effect prediction across deep mutational scanning and clinical datasets, without any structure conditioning. Homology-augmented PoET embeddings are state-of-the-art for transfer learning and protein function prediction, enabling accurate function predictors to be trained with 10x less data than alternative foundation models. PoET is also an order-of-magnitude smaller than other transformer-based PLMs. PoET is open source on github (https://github.com/OpenProteinAI/PoET) and is also available through the OpenProtein.AI web app where it underpins our protein property prediction and design tools.

Biological systems are difficult to study because they consist of thousands of parts, vary in space and time, and their fundamental unit—the cell—displays remarkable variation in its behavior. These challenges have spurred the development of genomics and imaging technologies over the past 30 years that have revolutionized our ability to capture information about biological systems in the form of images. Excitingly, these advances are poised to place the microscope back at the center of the modern biologist’s toolkit. Because we can now access temporal, spatial, and “parts list” variation via imaging, images have the potential to be a standard data type for biology.

Biology needs a new data infrastructure for this vision to become a reality. Imaging methods are of little use if it is too difficult to convert the resulting data into quantitative, interpretable information. New deep learning methods are essential to the reliable interpretation of imaging data. These methods differ from conventional algorithms in that they learn how to perform tasks from labeled data; they have demonstrated immense promise, but they are challenging to use in practice. The expansive training data required to power them are sorely lacking, as are easy-to-use software tools for creating and deploying new models. Solving these challenges through open software is a key goal of the Van Valen lab. In this talk, I describe DeepCell, a collection of software tools that meet the data, model, and deployment challenges associated with deep learning. These include tools for distributed labeling of biological imaging data, a collection of modern deep learning architectures tailored for biological-image analysis tasks, and cloud-native software for making deep learning methods accessible to the broader life science community. I discuss how we have used DeepCell to label large-scale imaging datasets to power deep learning methods that achieve human-level performance and enable new experimental designs for imaging-based experiments.