CEMIG: Prediction of the<i>cis</i>-regulatory motif using the De Bruijn graph from ATAC-seq

Wang, Yizhong; Li, Yang; Wang, Cankun; Ma, Qin; Liu, Bingqiang

doi:10.1101/2023.05.26.542440

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…To the best of our knowledge, just four other tools exist intended to generate de novo motifs from ATAC-seq data, namely BindVAE 11 and MMGraph 12 (both using machine learning in combination with k-mers), CEMIG 13 (which utilizes De Bruijin graphs created on k-mers), and the RSAT peak-motifs pipeline 14 (a pipeline intended for ChIP-seq, which is also applicable to ATAC-seq data). However, BindVAE, CEMIG and RSAT solely operate on the sequences of complete ATAC-seq peaks, therefore these tools lack precision compared to a FP based tool.…”

Section: Resultsmentioning

confidence: 99%

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Schultheis,

Bentsen,

Heger

et al. 2023

Preprint

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Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ∼700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality i) to isolate FPs without binding motifs, ii) to perform de novo motif generation and iii) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

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Section: Resultsmentioning

confidence: 99%

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Schultheis,

Bentsen,

Heger

et al. 2023

Preprint

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“…Inspired by CEMIG [31], we employed dHICA to discern cell-specific HM peak sites in K562 and GM12878 cells. For each HM, we delineated K562-specific, GM12878-specific and shared peaks(detailed in supplementary Text S2).…”

Section: Performance Evaluation Across Different Cell Lines Tissues A...mentioning

confidence: 99%

Discriminative histone imputation using chromatin accessibility

Wen,

Zhong,

Zhang

et al. 2024

Preprint

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Motivation: Histone modifications (HMs) play a crucial role in various biological processes, including transcription, replication, and DNA repair, and they have a significant impact on chromatin structure. However, annotating HMs across different cell types using experimental approaches alone is impractical due to cost and time constraints. Therefore, computational techniques are valuable for predicting HMs, complementing experimental methods, and enhancing our understanding of DNA characteristics and gene expression patterns. Results: In this study, we introduce a deep learning model called discriminative histone imputation using chromatin accessibility (dHICA). dHICA combines extensive DNA sequence information with chromatin accessibility data, utilizing the Transformer architecture. This allows dHICA to have a large receptive field, and more cell-type specific information, which can not only incorporate distal information across the entire genome, but also improve predictive accuracy and interpretability. Comparing performance with other models, we found that dHICA exhibits superior performance in both peak calling and signal predictions, especially in biologically salient regions such as gene elements. dHICA serves as a crucial tool for advancing our understanding of chromatin dynamics, offering enhanced predictive capabilities and interpretability, particularly in critical biological regions such as gene elements.

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GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site

Chuah,

He,

Huang

2024

Sci Rep

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The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

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CEMIG: Prediction of thecis-regulatory motif using the De Bruijn graph from ATAC-seq

Cited by 4 publications

References 54 publications

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Discriminative histone imputation using chromatin accessibility

GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site

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