MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data

Zhang, Shuangquan; Yang, Lili; Wu, Xiaotian; Sheng, Nan; Fu, Yuan; Ma, Anjun; Wang, Yan

doi:10.1093/bioinformatics/btac572

Cited by 8 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, just four other tools exist intended to generate de novo motifs from ATAC-seq data, namely BindVAE 15 and MMGraph 16 (both using machine learning in combination with k-mers), CEMIG 17 (which utilizes De Bruijin graphs created on k-mers), and the RSAT peak-motifs pipeline 18 (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. 2024

Sci Rep

View full text Add to dashboard Cite

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 (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) 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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Schultheis,

Bentsen,

Heger

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…For each dataset, we regard all its peaks as positive sequences with label '1'. We generate negative sequences with label '0' by randomly shuffling all bases within a positive sequence 22 . The negative sequences do not contain TFBSs but possess the same GC contents as the positive ones.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The MEME-ChIP framework has adopted the STREME algorithm, which was recently published. The comparison of performance for sequence classification is evaluated by the Area Under Receiver Operating Characteristic curve (AUC), Accuracy (ACC), Matthews Correlation Coefficient (MCC), and area under the Precision-Recall Curve (PRC) 2,22 . We downloaded 20 ChIP-exo datasets of E. coli from the proChIPdb database 19 , which are composed of a wide array of peak numbers (25-1,630).…”

Section: Benchmarking Motif Discovery On Chip-exo Datasetsmentioning

confidence: 99%

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

Wang

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Identifying precise transcription factor binding sites (TFBS) or regulatory DNA motifs plays a fundamental role in researching transcriptional regulatory mechanisms in cells and in helping construct regulatory networks. Current algorithms developed for motif searching focus on the analysis of ChIP-enriched peaks but are not able to integrate the ChIP signal in nucleotide resolution. We present a weighted two-stage alignment tool (TESA). Our framework implements an analysis workflow from experimental datasets to TFBS prediction results. It employs a binomial distribution model and graph searching model with ChIP-exonuclease (ChIP-exo) reads depth and sequence data. TESA can effectively measure the possibility for each position to be an actual TFBS in a given promoter sequence and predict statistically significant TFBS sequence segments. The algorithm substantially improves prediction accuracy and extends the scope of applicability of existing approaches. We apply the framework to a collection of 20 ChIP-exo datasets of E. coli from proChIPdb and evaluate the prediction performance through comparison with three existing programs. The performance evaluation against the compared programs indicates that TESA is more accurate for identifying regulatory motifs in prokaryotic genomes.

show abstract

MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data

Cited by 8 publications

References 6 publications

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

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

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

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

Contact Info

Product

Resources

About