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A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization

Pietro Demurtas, Ferdinando Zanchetta, Giovanni Perini, Rita Fioresi

Abstract

Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most current approaches for TF binding site prediction focus on individual TFs and binary classification tasks, without a full analysis of the possible interactions among various TFs. In this paper we investigate DNA TF binding site recognition as a multi-label classification problem, achieving reliable predictions for multiple TFs on DNA sequences retrieved in public repositories. Our deep learning models are based on Temporal Convolutional Networks (TCNs), which are able to predict multiple TF binding profiles, capturing correlations among TFs andtheir cooperative regulatory mechanisms. Our results suggest that multi-label learning leading to reliable predictive performances can reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, while also suggesting novel relationships and cooperation among TFs.

A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization

Abstract

Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most current approaches for TF binding site prediction focus on individual TFs and binary classification tasks, without a full analysis of the possible interactions among various TFs. In this paper we investigate DNA TF binding site recognition as a multi-label classification problem, achieving reliable predictions for multiple TFs on DNA sequences retrieved in public repositories. Our deep learning models are based on Temporal Convolutional Networks (TCNs), which are able to predict multiple TF binding profiles, capturing correlations among TFs andtheir cooperative regulatory mechanisms. Our results suggest that multi-label learning leading to reliable predictive performances can reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, while also suggesting novel relationships and cooperation among TFs.
Paper Structure (15 sections, 5 equations, 10 figures, 7 tables)

This paper contains 15 sections, 5 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Datasets for multi-label TFBS recognition
  4. Dataset for binary TFBS recognition
  5. Deep Learning Architectures
  6. Attribution Methods
  7. Implementation
  8. Results and discussion
  9. Binary classification as benchmarking
  10. Multi-label classification results
  11. Attributions
  12. Conclusions
  13. Data availability
  14. Code availability
  15. Conflict of interest

Figures (10)

  • Figure 1: E2F4-DP2-DNA complex zheng1999structural
  • Figure 2: Multiple TFs binding to DNA
  • Figure 3: Causal convolutions by the use of padding; on the left 1D convolution with "valid" padding, on the right 1D convolution with left padding enforcing causality kondratyuk2021movinets.
  • Figure 4: A residual block
  • Figure 5: Temporal Convolutional Networks as proposed by Bai et al.bai2018empirical.
  • ...and 5 more figures