Machine learning predicts nucleosome binding modes of transcription factors.

ABSTRACT:

Background

Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding, dyad binding, groove binding, and gyre spanning. However, there are substantial experimental challenges in measuring nucleosome binding modes for thousands of TFs in different species.

Results

We present a computational prediction of the binding modes based on TF protein sequences. With a nested cross-validation procedure, our model outperforms several fine-tuned off-the-shelf machine learning (ML) methods in the multi-label classification task. Our binary classifier for the EB mode performs better than these ML methods with the

SUBMITTER: Kishan KC 

PROVIDER: S-EPMC8008688 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature