Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction

TL;DR

This paper introduces Transfer String Kernel (TSK), a novel method for predicting DNA-protein binding sites across different cellular contexts by addressing distribution shifts, outperforming existing tools in cross-organism and peptide binding tasks.

Contribution

The paper presents TSK, a new sequence-based classification method that incorporates sample distribution adaptation for improved cross-context DNA-protein binding prediction.

Findings

TSK outperforms state-of-the-art TFBS tools in cross-organism settings.

TSK demonstrates strong generalization on MHC peptide binding predictions.

TSK is especially effective when binding sequences are not conserved across contexts.

Abstract

Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, therefore, propose a method called "Transfer String Kernel" (TSK) that achieves improved prediction of transcription factor binding site (TFBS) using knowledge transfer via cross-context sample adaptation. TSK maps sequence segments to a high-dimensional feature space using a discriminative mismatch string kernel framework. In this high-dimensional space, labeled examples of the source context are re-weighted so that the revised sample distribution matches the target context more closely. We have experimentally verified TSK for TFBS identifications on fourteen different TFs under a cross-organism setting. We find that TSK consistently outperforms the state-of the-art TFBS tools, especially when working with TFs whose binding sequences are not conserved across contexts. We also demonstrate the generalizability of TSK by showing its cutting-edge performance on a different set of cross-context tasks for the MHC peptide binding predictions.