Biology subjects

Marti-Gomez, C.

Publications and source records attributed to Marti-Gomez, C..

11 recordsLinked to original sources

bioRxiv2026

Inference of fitness landscapes with heterogeneous patterns of epistasis across sites

Fitness landscapes provide a framework for understanding how genetic variation shapes evolutionary outcomes. Although these landscapes were long treated as abstract conceptual objects, recent advances in genetic engineering and high-throughput phenotyping have enabled the empirical measurement of phenotypic values across large combinatorial sequence spaces. These developments create a need for statistical frameworks that can summarize, infer, and interpret fitness landscapes in the presence of complex genetic interactions. Here, we introduce a framework for summarizing the structure of genetic interactions across sites based on the average squared local k-way epistatic coefficients between mutations at different subsets of sites, and derive the precise manner in which the variance in these local k-way epistatic coefficients across backgrounds relates to epistasis of orders higher than k. These statistics can be computed exactly for complete combinatorial landscapes and are related to classical statistics in the fitness landscape literature. Moreover, they can be estimated from empirical correlations when data are incomplete or noisy, and used to define an empirical Bayes prior for fitness landscape inference that differentially penalizes interactions involving different subsets of sites. We apply this inference method to diverse high-throughput protein and RNA combinatorial mutagenesis datasets and find that fitness landscapes often show highly structured patterns of genetic interactions across positions. Finally, we use this model to infer a fitness landscape for a dynamic self-splicing intron comprising 65,536 genotypes, and describe in detail the main genetic interactions that shape the structure of this landscape and how they relate to the underlying molecular mechanism. Together, these results provide new tools for summarizing and modeling complex fitness landscapes, and for linking large-scale empirical data to the mathematical theory of fitness landscapes.

evolutionary biology↗

bioRxiv2025

GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships

BackgroundMathematical models of sequence-function relationships are widely used in computational biology. A key challenge when interpreting these models is that many different choices for model parameters can encode the same sequence-function relationship. These ambiguities, called "gauge freedoms," must be removed before parameter values can be meaningfully interpreted. Doing this requires imposing additional mathematical constraints on parameter values, a process called "fixing the gauge." We recently developed mathematical methods for fixing the gauge of a large class of commonly used models. The most direct computational implementation of these methods is often impractical, however, because it requires a projection matrix whose size scales quadratically with the number of model parameters. ResultsHere we introduce GaugeFixer, a Python package that exploits the mathematical structure of gauge-fixing projections to achieve log-linear scaling in computation time and linear scaling in memory usage. GaugeFixer thus requires orders of magnitude less time and memory than standard matrix multiplication, and can fix the gauge of models having millions of parameters in seconds. To demonstrate its utility, we used GaugeFixer to analyze an empirical fitness landscape for translation initiation in bacteria. This analysis revealed striking similarities (but also fine-scale differences) in ribosome-binding preferences at different positions relative to the start codon, thereby aiding the interpretation of this otherwise unwieldy landscape. ConclusionsGaugeFixer thus fills an important gap in the computational tools available for interpreting quantitative models of sequence-function relationships.

bioinformatics↗

bioRxiv2025

Learning sequence-function relationships with scalable, interpretable Gaussian processes

AbstractUnderstanding the relationship between biological sequences, such as DNA, RNA or protein sequences, and their resulting phenotypes is one of the central goals of genetics. This task is complicated by epistasis, i.e., the context dependence of mutational effects. Advances in high-throughput phenotyping now make it possible to study these relationships at unprecedented scale, generating large datasets that measure phenotypes for tens or hundreds of thousands of sequences. However, standard regression models for analyzing such datasets often make unrealistic assumptions about the generalizability of mutational effects and epistatic coefficients across genetic backgrounds. Deep neural networks offer greater flexibility but suffer from limited interpretability and lack uncertainty quantification. Here, we introduce a family of interpretable Gaussian process models for sequence-function relationships that capture epistasis through flexible prior distributions that generalize classical theoretical models from the fitness landscape literature. In particular, these priors are parameterized by interpretable site-, allele-, and mutation-specific factors controlling the degree to which specific mutations decrease the predictability of the effects of other mutations. Using GPU acceleration to scale to large protein, RNA, and genome-wide SNP datasets, our models consistently deliver superior predictive performance while yielding interpretable parameters that both recover known features and uncover novel epistatic interactions. Overall, our methods provide new insights into the structure of the genotype-phenotype map and offer scalable, interpretable approaches for exploring complex genetic interactions across diverse biological systems.

systems biology↗

bioRxiv2025

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing

Mappings from biological sequences (DNA, RNA, protein) to quantitative measures of sequence functionality play an important role in contemporary biology. We are interested in the related tasks of (i) inferring predictive sequence-to-function maps and (ii) decomposing sequence-function maps to elucidate the contributions of individual subsequences. Because each sequence-function map can be written as a weighted sum over subsequences in multiple ways, meaningfully interpreting these weights requires "gauge-fixing," i.e., defining a unique representation for each map. Recent work has established that most existing gauge-fixed representations arise as the unique solutions to L2-regularized regression in an overparameterized "weight space" where the choice of regularizer defines the gauge. Here, we establish the relationship between regularized regression in overparameterized weight space and Gaussian process approaches that operate in "function space," i.e. the space of all real-valued functions on a finite set of sequences. We disentangle how weight space regularizers both impose an implicit prior on the learned function and restrict the optimal weights to a particular gauge. We show how to construct regularizers that correspond to arbitrary explicit Gaussian process priors combined with a wide variety of gauges and characterize the implicit function space priors associated with the most common weight space regularizers. Finally, we derive the posterior distribution of a broad class of sequence-to-function statistics, including gauge-fixed weights and multiple systems for expressing higher-order epistatic coefficients. We show that such distributions can be efficiently computed for product-kernel priors using a kernel trick.

bioinformatics↗

bioRxiv2025

Inference and visualization of complexgenotype-phenotype with gpmap-tools

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmaptools https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

evolutionary biology↗

bioRxiv2023

Protein evolvability under rewired genetic codes

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability -- the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether this robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively-parallel sequence-to-function assays to construct and analyze empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we demonstrate that alternative genetic codes can radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering non-standard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the biocontainment of synthetic organisms, respectively. Our findings demonstrate that the standard genetic code, a critical and near-universal cellular information processing system, not only mitigates replication and translation errors as compared to most alternative genetic codes, but also facilitates predictable and directional adaptive evolution by enabling evolving populations to readily find mutational paths to adaptation.

evolutionary biology↗

bioRxiv2022

Specificity, synergy, and mechanisms of splice-modifying drugs

Drugs that target pre-mRNA splicing hold great therapeutic potential, but the quantitative understanding of how these drugs work is limited. Here we introduce mechanistically interpretable quantitative models for the sequence-specific and concentration-dependent behavior of splice-modifying drugs. Using massively parallel splicing assays, RNA-seq experiments, and precision dose-response curves, we obtain quantitative models for two small-molecule drugs, risdiplam and branaplam, developed for treating spinal muscular atrophy. The results quantitatively characterize the specificities of risdiplam and branaplam for 5 splice site sequences, suggest that branaplam recognizes 5 splice sites via two distinct interaction modes, and disprove the prevailing two-site hypothesis for risdiplam activity at SMN2 exon 7. The results also show that anomalous single-drug cooperativity, as well as multi-drug synergy, are widespread among small-molecule drugs and antisense-oligonucleotide drugs that promote exon inclusion. Our quantitative models thus clarify the mechanisms of existing treatments and provide a basis for the rational development of new therapies.

biophysics↗

bioRxiv2022

MCRS1 modulates the heterogeneity of microtubule minus-end morphologies in mitotic spindles

Faithful chromosome segregation requires the assembly of a bipolar spindle, consisting of two antiparallel microtubule (MT) arrays having most of their minus ends focused at the spindle poles and their plus ends overlapping in the spindle midzone. Spindle assembly, chromosome alignment and segregation require highly dynamic MTs. The plus ends of MTs have been extensively investigated; instead, their minus end structure remains poorly characterized. Here, we used large-scale electron tomography to study the morphology of the MT minus ends in 3D-reconstructed metaphase spindles in HeLa cells. In contrast to the homogeneous open morphology of the MT plus ends at the kinetochores, we found that MT minus ends are heterogeneous showing either open or closed morphologies. Silencing the minus-end specific stabilizer, MCRS1 increased the proportion of open MT minus ends. Altogether, these data suggest a correlation between the morphology and the dynamic state of the MT ends. Taking this heterogeneity of the MT minus end morphologies into account, our work indicates an unsynchronized behavior of MTs at the spindle poles, thus laying the ground for further studies on the complexity of MT dynamics regulation.

cell biology↗

bioRxiv2020

Functional impact and regulation of global alternative splicing patterns in heart development and disease

Alternative splicing (AS) plays a major role in the generation of transcript diversity. In the heart, roles have been described for some AS variants and individual regulatory RNA binding proteins (RBPs); however, the global impact and regulation of AS patterns in cardiac pathophysiology is poorly understood. Here, we studied the AS profiles in heart disease, their relationship with heart development and the regulatory mechanisms control-ling AS dynamics in the mouse heart using a total of 136 RNA-seq samples. We found that AS and gene expression changes affect different genes, which are also involved in distinct biological functions. Developmental AS changes were more abundant and had stronger predicted impact on the encoded protein than those taking place during heart disease. However, AS changes in heart disease significantly modified protein interaction patterns and rewire the protein-protein interaction network. Using a database of experimentally determined binding sites of a large collection of RNA binding proteins, we studied the regulatory proteins associated to AS changes in each condition. Computational modelling revealed that developmental transitions were mainly driven by the up-regulation of MBNL1, whereas disease associated AS changes were driven by a more complex regulatory network, characterized by the interaction of different RNA binding proteins, with PTBP1 as the largest individual modulator. In adult mice, PTBP1 over-expression was sufficient to induce cardiac hypertrophy and diastolic dysfunction and significantly alter the AS profile. Overall, our study provides new in-sights into the functional impact of AS patterns in cardiac physiology and how computationally driven hypotheses can help to improve our understanding of RNA regulation and its contribution to heart disease.

bioinformatics↗

bioRxiv2019

dSreg: A bayesian model to integrate changes in splicing and RNA binding protein activity

Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two step-framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis. To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNAseq with data from regulatory features, e.g. binding sites of RNA binding proteins (RBPs). dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified alternative splicing changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RBPs experiments from ENCODE, as opposed to traditional enrichment methods such as Over-representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments. dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg.

bioinformatics↗

bioRxiv2017

De-novo assembly of zucchini genome reveals a whole genome duplication associated with the origin of the Cucurbita genus

The Cucurbita genus (squashes, pumpkins, gourds) includes important domesticated species such as C. pepo, C. maxima and C. moschata. In this study, we present a high-quality draft of the zucchini (C. pepo) genome. The assembly has a size of 263 Mb, a scaffold N50 of 1.8 Mb, 34,240 gene models, includes 92% of the conserved BUSCO core gene set, and it is estimated to cover 93.0% of the genome. The genome is organized in 20 pseudomolecules, that represent 81.4% of the assembly, and it is integrated with a genetic map of 7,718 SNPs. Despite its small genome size three independent evidences support that the C. pepo genome is the result of a Whole Genome Duplication: the topology of the gene family phylogenies, the karyotype organization, and the distribution of 4DTv distances. Additionally, 40 transcriptomes of 12 species of the genus were assembled and analyzed together with all the other published genomes of the Cucurbitaceae family. The duplication was detected in all the Cucurbita species analyzed, including C. maxima and C. moschata, but not in the more distant cucurbits belonging to the Cucumis and Citrullus genera, and it is likely to have happened 30 {+/-} 4 Mya in the ancestral species that gave rise to the genus.

genomics↗