Biology subjects

Sasse, A.

Publications and source records attributed to Sasse, A..

8 recordsLinked to original sources

bioRxiv2025

Deep genomic models of allele-specific measurements

Allele-specific quantification of sequencing data allows for a causal investigation of how DNA sequence variations influence cis gene regulation. Current methods for analyzing allele-specific measurements for causal analysis rely on statistical associations between genetic variation across individuals and allelic imbalance. Instead, we propose DeepAllele, a novel deep learning sequence-to-function model using paired allele-specific input, designed to learn sequence features that predict subtle changes in gene regulation between alleles. Our approach is suited for datasets with unambiguous phasing, such as F1 hybrids and other controlled genetic crosses, or long-read sequencing technologies used in Fiber-seq, in which reads can be assigned to complete allele sequences. We apply our framework to allele-specific measurements in immune cells from F1 hybrid mice, and show that the models additionally learned cis-regulatory grammar aligns with known biological mechanisms across a significantly larger number of genomic regions compared to baseline models. In summary, our work presents a computational framework to leverage genetic variation to uncover functionally-relevant regulatory motifs, enhancing causal discovery in genomics.

bioinformatics↗

bioRxiv2025

A scalable approach to investigating sequence-to-expression prediction from personal genomes

Sequence-to-function (S2F) models hold the promise of evaluating arbitrary DNA sequences, providing a powerful framework for linking genotype to phenotype. Yet, despite strong performance across genomic loci, these models often struggle to capture inter-individual variation in gene expression. To address this, we propose personal genome training--training models to make genotype-specific predictions at a single locus. We introduce SAGE-net, a scalable framework and software package for training and evaluating S2F models using personal genomes. Using SAGE-net, we systematically explore model architectures and training regimes, showing that personal genome training improves gene expression prediction accuracy for held-out individuals. However, performance gains arise primarily from identifying predictive variants, rather than learning a cis-regulatory grammar that generalizes across loci. This lack of generalization persists across a wide range of hyperparameters. In contrast, when applied to DNA methylation (DNAm), personal genome training enables improved generalization to unseen individuals in unseen genomic regions. This suggests that S2F models may more readily capture the sequence-level determinants of inter-individual variation in epigenomic traits. These findings highlight the need for further exploration to unlock the full potential of S2F models in decoding the regulatory grammar of personal genomes. Scalable software and infrastructure development will be critical to this progress.

genomics↗

bioRxiv2025

Refining the cis-regulatory grammar learned by sequence-to-activity models by increasing model resolution

Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. To identify regulatory motifs and their regulatory syntax, deep learning based sequence-to-activity (S2A) models learn transcription factor binding motifs and their combinations from DNA sequence by modeling measured chromatin accessibility. Previously, we developed AI-TAC, a S2A model that predicts chromatin accessibility across various immune cell types in multi-task fashion, effectively decoding the regulatory syntax underlying immune cell differentiation. While ATAC-seq is commonly used to measure regional accessibility, it also provides high-resolution profiles, the distribution of Tn5 insertion sites, that offer additional insights into the precise location and strength of TF binding sites. Here we demonstrate that modeling ATAC-seq profiles alongside accessibility consistently improves predictions of differential chromatin accessibility across cell types. Moreover, we also find that multi-task learning across related immune cell types consistently outperforms single-task models. To understand what additional information bpAI-TAC learns from ATAC-seq profiles, we systematically compare sequence attributions from models trained with and without ATAC-seq profiles. We identify novel motifs with strong effect sizes that emerge only when profile data is included. Our findings suggest that modeling ATAC-seq at base-pair resolution enables the model to learn a more nuanced and sensitive representation of the cis-regulatory syntax driving immune cell-specific chromatin landscapes.

genomics↗

bioRxiv2024

Reconstructing the sequence specificities of RNA-binding proteins across eukaryotes

RNA-binding proteins (RBPs) are key regulators of gene expression. Here, we introduce EuPRI (Eukaryotic Protein-RNA Interactions) - a freely available resource of RNA motifs for 34,736 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of reconstructed motifs. We reconstruct these motifs with a new computational platform -- Joint Protein-Ligand Embedding (JPLE) -- which can detect distant homology relationships and map specificity-determining peptides. EuPRI quadruples the number of known RBP motifs, expanding the motif repertoire across all major eukaryotic clades, and assigning motifs to the majority of human RBPs. EuPRI drastically improves knowledge of RBP motifs in flowering plants. For example, it increases the number of Arabidopsis thaliana RBP motifs 7-fold, from 14 to 105. EuPRI also has broad utility for inferring post-transcriptional function and evolutionary relationships. We demonstrate this by predicting a role for 12 Arabidopsis thaliana RBPs in RNA stability and identifying rapid and recent evolution of post-transcriptional regulatory networks in worms and plants. In contrast, the vertebrate RNA motif set has remained relatively stable after its drastic expansion between the metazoan and vertebrate ancestors. EuPRI represents a powerful resource for the study of gene regulation across eukaryotes.

genomics↗

bioRxiv2023

Quick and effective approximation of in silico saturation mutagenesis experiments with first-order Taylor expansion

To understand the decision process of genomic sequence-to-function models, various explainable AI algorithms have been proposed. These methods determine the importance of each nucleotide in a given input sequence to the models predictions, and enable discovery of cis regulatory motif grammar for gene regulation. The most commonly applied method is in silico saturation mutagenesis (ISM) because its per-nucleotide importance scores can be intuitively understood as the computational counterpart to in vivo saturation mutagenesis experiments. While ISM is highly interpretable, it is computationally challenging to perform, because it requires computing three forward passes for every nucleotide in the given input sequence; these computations add up when analyzing a large number of sequences, and become prohibitive as the length of the input sequences and size of the model grows. Here, we show how to use the first-order Taylor approximation to compute ISM, which reduces its computation cost to a single forward pass for an input sequence. We use our theoretical derivation to connect ISM with the gradient of the model and show how this approximation is related to a recently suggested correction of the models gradients for genomic sequence analysis. We show that the Taylor ISM (TISM) approximation is robust across different model ablations, random initializations, training parameters, and data set sizes.

bioinformatics↗

bioRxiv2023

Impact of random 50-base sequences inserted into an intron on splicing in Saccharomyces cerevisiae

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing - 5 and 3 splice sites and a branch point - are especially well- characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nucleotide elements (N50) individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20% compared to the intron control. N50 features, including higher GC content, presence of GU repeats and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases - such as the GU repeats - with other bases specifically within the reporter pre-mRNA analyzed. However, neither convolutional neural network nor linear models were able to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex non-linear interactions in RNA structures are not accurately captured by RNA structure prediction methods given the limited number of variants. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing.

synthetic biology↗

bioRxiv2023

How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?

Introductory Paragraph Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

bioinformatics↗

bioRxiv2020

Doxorubicin induces cardiotoxicity in a pluripotent stem cell model of aggressive B cell lymphoma cancer patients

Cancer therapies have been shown to induce cardiovascular complications. The aims of this study were to establish an in vitro induced pluripotent stem cell model (iPSC) of anthracycline-induced cardiotoxicity (ACT) from patients with an aggressive form of cancer. ACT-iPSC-CM generated from individuals with CD20+ B-cell lymphoma cancer who had received high doses of DOX and suffered cardiac dysfunction were observed to be persistently more susceptible to DOX toxicity compared to control-iPSC-CM. ACT-iPSC-CM exhibited increased DOX-dependent disorganized myofilament structure and cell death, as well as higher reactive oxygen species (ROS) compared to controls. Importantly, analysis of engineered heart muscle (EHM) from ACT-iPSC-CM showed an impaired DOX-dependent mechanical functionality. Transcriptome profiles of EHM are in line with a disturbed adjustment to DOX-dependent alteration of Ca2+ homeostasis in ACT-iPSC-CM. Furthermore, genetic variants in different cardiac key regulators were uncovered. In conclusion, we developed the first human iPSC-CM and EHM model of DOX-induced cardiac dysfunction in patients with B-cell lymphoma. Our results suggest that DOX-related stress resulted in decreased contractile activity and finally in heart failure in ACT patients. Brief summaryDevelopment of the first human iPSC-CM model of DOX-induced cardiac dysfunction in patients with aggressive B cell lymphoma and high-dose DOX treatment.

cell biology↗