Biology subjects

Aksu, E. D.

Publications and source records attributed to Aksu, E. D..

6 recordsLinked to original sources

bioRxiv2026

Boosting Single-Cell Methods Significantly Enhances Cell-type Classification

Single-cell sequencing drives biological discovery by revealing key cellular processes, cell types, and cell states. Recent technological advances have accelerated atlas-scale data generation across laboratories. With the increasing amount of single-cell RNA-seq (scRNA-seq) data, new challenges emerge, including effective batch correction, cell-type annotation, rare cell-type and cell-state detection, as well as biological pathway discovery. Inspired by methods for boosting weak learners such as AdaBoost, here we present a novel paradigm, scBoost, that combines single-cell analysis methods using stacked ensembles to boost task performance beyond that of individual methods. We show that scBoost pretrained on Tabula Sapiens 2.0 dataset performs significantly better than existing methods in cell-type classification on the real-world Human Fetal Heart Map and Plaque Atlas. We also demonstrate that scBoost substantially improves the performance of cell-type classification and ranks at the top of the leaderboard on the OpenProblems benchmark, a standard-in-the-field. scBoost is modular and is adaptive to new single-cell methods, which will enable it to continue to serve as a resource to the community in the future.

bioinformatics↗

bioRxiv2026

Systematic promoter perturbation reveals limitations of AlphaGenome gene expression predictions

Sequence-to-function (S2F) models promise to facilitate the interpretation of noncoding variants by predicting their effects on gene regulation directly from genomic sequence. However, whether these models recapitulate fundamental principles of transcriptional biology remains unclear. Here, we tested whether AlphaGenome recognizes the expected consequences of promoter disruption on predicted gene expression. We systematically perturbed annotated promoter elements in silico for 2,111 promoter-gene pairs using dinucleotide shuffling, GC-adjusted random base substitution, and N-masking. We then compared AlphaGenome-predicted CAGE and RNA-sequencing signals across 20 tissues for reference and promoter-disrupted sequences. While CAGE and RNA-sequencing predictions were strongly correlated for reference sequences, promoter disruption markedly reduced predicted CAGE activity, with N masking producing reductions of approximately three orders of magnitude. By contrast, the predicted RNA-sequencing signals were substantially less affected, resulting in an uncoupling of two molecular readouts of gene expression. Exon-level analyses showed that AlphaGenome predicted reduced RNA-sequencing signal when exonic sequences are within the disrupted region, but frequently failed to propagate this effect to downstream exons. This divergence between the CAGE and RNA-sequencing readouts was apparent from the second exon onward and was more pronounced in longer genes. Although alternative transcription initiation and shared downstream exons may explain individual cases, it is unlikely to account for the systematic discordance between CAGE and RNA-sequencing predictions. These findings indicate that AlphaGenome can recognize the local loss of promoter activity without consistently capturing its consequences across the corresponding transcript. More broadly, our results show that predictions from individual S2F output tracks do not necessarily reflect a coherent model of transcription. Systematic biological benchmarking and experimental validation therefore remain essential before such predictions can be used to prioritize or classify noncoding variants in research or diagnostic settings.

genomics↗

bioRxiv2026

BatchRefiner: fast, significant improvement in batch integration of single-cell embeddings with ensemble refinement

Data from single-cell RNA sequencing (scRNA-seq) and the Assay for Transposase-Accessible Chromatin (scATAC-seq) are high-dimensional, sparse, and undesirably capture technical variability between experiments or batches. Many analysis methods thus seek to produce a low-dimensional cell-by-feature embedding space that groups together biologically similar cells across batches while distancing dissimilar cells. Here, we introduce ensemble refinement for scRNA-seq and scATAC-seq embeddings, inspired by ensemble methods from statistical machine learning, and implement BatchRefiner, a fast post-processing tool to enhance batch integration. We extensively benchmark widely-used scRNA-seq embedding methods on both batch integration and biological conservation over a wide range of datasets, before and after the addition of BatchRefiner. We extend these benchmarking approaches to provide the first comprehensive benchmark of batch integration for scATAC-seq embedding methods, including BatchRefiner. Importantly, we formalize a significance statistic, which we use to demonstrate BatchRefiner's significant improvement in batch integration across a wide range of embedding methods, atlas-scale datasets, and established metrics.

bioinformatics↗

bioRxiv2026

Towards Principled Evaluation of Single-Cell Perturbation Prediction Models

Single-cell perturbation experiments measure how interventions alter cellular phenotypes. However, the number of possible perturbations and biological contexts far exceeds what can be tested experimentally. Motivated by this constraint, predictive models aim to extrapolate cellular responses to unseen conditions. Despite substantial efforts in model development, benchmark studies have reached inconsistent conclusions about the capabilities of current perturbation-response models. A major challenge is that evaluation protocols vary widely across studies, making results difficult to compare. Furthermore, the lack of consensus on evaluation hampers progress because it is unclear which predictive capabilities new models should prioritize. To help build consensus on evaluation principles, we develop a taxonomy that decomposes evaluation protocols into their representation, metric, score transformation, and reporting strategies. We characterize how these choices determine which aspects of prediction quality a benchmark measures and discuss criteria for selecting and assessing protocols in relation to specific benchmarking goals. We additionally provide scPertEval, a Python package with reference implementations of selected evaluation protocols, and use it to assess protocol behavior across seven publicly available single-cell perturbation datasets. By making evaluation choices and their underlying trade-offs explicit, we aim to stimulate a community discussion about developing more comparable and task-aligned evaluation protocols. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/740433v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@bd1e3corg.highwire.dtl.DTLVardef@c26c2org.highwire.dtl.DTLVardef@1c49afdorg.highwire.dtl.DTLVardef@9b988c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Context-aware sequence-to-activity model of human gene regulation

Sequence-to-function models have been very successful in predicting gene expression, chromatin accessibility, and epigenetic marks from DNA sequences alone. However, current state-of-the-art models have a fundamental limitation: they cannot extrapolate beyond the cell types and conditions included in their training dataset. Here, we introduce a new approach that is designed to overcome this limitation: Corgi, a new context-aware sequence-to-function model that accurately predicts genome-wide gene expression and epigenetic signals, even in previously unseen cell types. We designed an architecture that strives to emulate the cell: Corgi integrates DNA sequence and trans-regulator expression to predict the coverage of multiple assays including chromatin accessibility, histone modifications, and gene expression. We define trans-regulators as transcription factors, histone modifiers, transcriptional coactivators, and RNA binding proteins, which directly modulate chromatin states, gene expression, and mRNA decay. Trained on a diverse set of bulk and single cell human datasets, Corgi has robust predictive performance, approaching experimental-level accuracy in gene expression predictions in previously unseen cell types, while also setting a new state-of-the-art level for joint cross-sequence and cross-cell type epigenetic track prediction. Corgi can be used in practice to impute context-specific assays such as DNA accessibility and histone ChIP-seq, using only RNA-seq data.

genomics↗

bioRxiv2024

Transfer learning and DNA language models enhance transcription factor binding predictions

Identification of in vivo transcription factor (TF) binding sites is crucial to understand gene regulatory networks, but the lack of scalability in the methods for their experimental identification directs researchers towards computational models. TF binding site prediction models are often specific for a given TF, which also hinders the generalizability of models to previously unseen TFs. Here, we present an approach to predict in vivo TF binding sites using DNA accessibility, TF RNA expression and TF binding motifs. Our novel method leverages DNA language model embeddings and transfer learning to improve its accuracy and generalizability, achieving a mean area under the precision-recall curve (AUPR) of 0.51 in held-out cell types and chromosomes in the ENCODE-DREAM in vivo TFBS prediction challenge, outperforming the top-ranked methods. Furthermore, we show that prediction accuracy increases when TFs are highly active and exhibit cell-type specific expression. We finally test our models in an independent dataset on previously unseen TFs, and report a mean AUPR of 0.36, which is state-of-the-art in a cross-TF, cross-cell type and cross-chromosomal setting.

bioinformatics↗