Evolution of comparative transcriptomics: biological scales, phylogenetic spans, and modeling frameworks.

Comparative transcriptomic studies are key to understanding how molecular evolution drives phenotypic divergence across the tree of life. Here, we discuss three major directions in which the field of comparative transcriptomics is evolving. The first one is enabled by advances in sequencing technologies.

Guidelines for releasing a variant effect predictor.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs.

Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration.

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for 'actionable' genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.

MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays.

Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update to MaveDB ( https://www.mavedb.

A Bi-CMOS electronic photonic integrated circuit quantum light detector.

Complimentary metal-oxide semiconductor (CMOS) integration of quantum technology provides a route to manufacture at volume, simplify assembly, reduce footprint, and increase performance. Quantum noise-limited homodyne detectors have applications across quantum technologies, and they comprise photonics and electronics. Here, we report a quantum noise-limited monolithic electronic-photonic integrated homodyne detector, with a footprint of 80 micrometers by 220 micrometers, fabricated in a 250-nanometer lithography bipolar CMOS process.

Guidelines for releasing a variant effect predictor.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them.

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction.

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale.

Proteome-wide model for human disease genetics.

Identifying variants driving disease accelerates both genetic diagnosis and therapeutic development, but missense variants still present a bottleneck as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are sufficiently accurate to be of clinical value for variants in disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome . To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data and achieves state-of-the-art performance on a suite of proteome-wide prediction tasks, without overestimating the prevalence of deleterious variants in the population.

Disease variant prediction with deep generative models of evolutionary data.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods have relied on training machine learning models on known disease labels.

Simple Emergent Power Spectra from Complex Inflationary Physics.

We construct ensembles of random scalar potentials for N_{f}-interacting scalar fields using nonequilibrium random matrix theory, and use these to study the generation of observables during small-field inflation. For N_{f}=O(few), these heavily featured scalar potentials give rise to power spectra that are highly nonlinear, at odds with observations. For N_{f}≫1, the superhorizon evolution of the perturbations is generically substantial, yet the power spectra simplify considerably and become more predictive, with most realizations being well approximated by a linear power spectrum.

Gravitational wave consistency relations for multifield inflation.

We study the tensor spectral index n(t) and the tensor-to-scalar ratio r in the simplest multifield extension to single-field, slow-roll inflation models. We show that multifield models with potentials V∼[under ∑]iλ_{i}|ϕ_{i}|^{p} have different predictions for n(t)/r than single-field models, even when all the couplings are equal λ_{i}=λ_{j}, due to the probabilistic nature of the fields' initial values. We analyze well-motivated prior probabilities for the λ_{i} and initial conditions to make detailed predictions for the marginalized probability distribution of n(t)/r.

Simple predictions from multifield inflationary models.

We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases.