Identifying functional targets from transcription factor binding data using SNP perturbation

Xiang, J.; Kim, S.

doi:10.1101/412841

Home/Archive/bioRxiv

bioRxiv · 10.1101/412841

Identifying functional targets from transcription factor binding data using SNP perturbation

Xiang, J.·Kim, S.

View original source ↗Export metadata ↓

Abstract

Transcription factors (TFs) play a key role in transcriptional regulation by binding to DNA to initiate the transcription of target genes. Techniques such as ChIP-seq and DNase-seq provide a genome-wide map of TF binding sites but do not offer direct evidence that those bindings affect gene expression. Thus, these assays are often followed by TF perturbation experiments to determine functional binding that leads to changes in target gene expression. However, such perturbation experiments are costly and time-consuming, and have a well-known limitation that they cannot distinguish between direct and indirect targets. In this study, we propose to use the naturally occurring perturbation of gene expression by genetic variation captured in population SNP and expression data to determine functional targets from TF binding data. We introduce a computational methodology based on probabilistic graphical models for isolating the perturbation effect of each individual SNP, given a large number of SNPs across genomes perturbing the expression of all genes simultaneously. Our computational approach constructs a gene regulatory network over TFs, their functional targets, and further downstream genes, while at the same time identifying the SNPs perturbing this network. Compared to experimental perturbation, our approach has advantages of identifying direct and indirect targets, and leveraging existing data collected for expression quantitative trait locus mapping, a popular approach for studying the genetic architecture of expression. We apply our approach to determine functional targets from the TF binding data for a lymphoblastoid cell line from the ENCODE Project, using SNP and expression data from the HapMap 3 and 1000 Genomes Project samples. Our results show that from TF binding data, functional target genes can be determined by SNP perturbation of various aspects that impact transcriptional regulation, such as TF concentration and TF-DNA binding affinity.

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Xiang, J., Kim, S.. 2018-09-10. Identifying functional targets from transcription factor binding data using SNP perturbation. https://doi.org/10.1101/412841

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cross-modal transfer enables zero-shot prediction of drug responses across cell types

Genetic perturbation responses capture the functional consequences of gene-level interventions, providing functional information for drug-response prediction beyond baseline gene expression. Within the same cellular context, genetic and chemical perturbations engage shared regulatory networks, providing a biological basis for transferring response information between perturbation modalities. Here we introduce GRAFT, a framework that combines genetic-to-chemical transfer with cross-context prediction to infer drug-induced transcriptional responses without drug measurements in the target context. Across benchmarked LINCS and external contexts, GRAFT improved response correlation, directional agreement and magnitude reconstruction over competing methods, particularly for drugs with context-dependent responses. GRAFT-Adapt further refines predictions of unmeasured responses using a small number of drug measurements from the target context. GRAFT-Atlas spans 13 cellular contexts and 7,856 drug catalog entries, comprising 102,128 predictions for drug-context combinations. The atlas captured recognizable pharmacological neighborhoods, shared functional programs, context-dependent transcriptional changes and structural-functional associations supported by molecular docking. Across ten iPSC lines from distinct donors, GRAFT identified candidate inter-donor differences, with functional support for selected candidates from measured genetic perturbation responses. Together, GRAFT enables perturbation-informed prediction and comparison of drug responses across cellular contexts.

bioinformatics↗

bioRxiv2026

Interpreting graph neural networks reveals microbial association patterns in inflammatory bowel diseases

Traditional machine learning models trained on gut microbiome abundance profiles can distinguish individuals with inflammatory bowel disease (IBD) from non-IBD individuals. However, their interpretation typically considers taxa in isolation, making it difficult to understand how associations between taxa contribute to predictions. Here, we inferred individual-specific microbial association networks from longitudinal metagenomics data of 106 individuals in HMP2/iHMP and used them to train and interpret graph neural network (GNN) models, which we compared with abundance-based models such as Random Forest (RF). Although RF achieved higher predictive performance (AUPRC ~0.59 vs 0.446), interpreting the GNNs revealed that their predictions relied on a densely connected module of co-abundant Bacteroidales species. Unlike the taxa prioritized by RF, these species were not differentially abundant between conditions. In an independent cohort, this module carried most of the predictive signal, and removing its associations reduced performance to near chance. These results show that coordinated association structure, rather than abundance shifts alone, is a feature of the IBD gut microbiome, and that interpreting GNNs can uncover such patterns that complement abundance-based analyses.

bioinformatics↗

bioRxiv2026

FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming

Cellular reprogramming supports regenerative medicine, yet the endogenous regulators of a specified source-to-target transition remain incompletely defined. Existing computational methods rank candidate regulators from network topology, trajectory dynamics, or cell-state labels, leaving unused the factors experimentally delivered to induce the transition. We present FateDriver, a weakly supervised dual-view graph neural network that jointly represents cells and genes from single-cell transcriptomes, treats the delivered factors as sparse gene-level labels, and ranks the remaining genes for a specified transition under a positive--unlabeled formulation. Across two OSKM datasets and one GMT dataset, FateDriver ranked first or second among six methods in recovering externally curated reference genes, exceeded a supervision-matched non-graph model despite weaker co-expression with the delivered factors, and achieved the highest recovery of protein targets of pluripotency-inducing chemical cocktails in both OSKM datasets. Holding either the source or the endpoint fixed yielded distinct candidate programs, including a DDX58--interferon axis associated with cells lacking endogenous pluripotency markers. Applying the same framework to candidate source--target pairs prioritized distinct candidate origins for two in vivo partially reprogrammed intestinal populations. FateDriver converts sparse experimental knowledge into transition-specific hypotheses for regulator prioritization from single-cell trajectories.

bioinformatics↗