Predmoter—cross-species prediction of plant promoter and enhancer regions Article Swipe
Felicitas Kindel
,
Sebastian Triesch
,
Urte Schlüter
,
Laura Alexandra Randarevitch
,
Vanessa Reichel-Deland
,
Andreas P.M. Weber
,
Alisandra K. Denton
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioadv/vbae074
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioadv/vbae074
Motivation Identifying cis-regulatory elements (CREs) is crucial for analyzing gene regulatory networks. Next generation sequencing methods were developed to identify CREs but represent a considerable expenditure for targeted analysis of few genomic loci. Thus, predicting the outputs of these methods would significantly cut costs and time investment. Results We present Predmoter, a deep neural network that predicts base-wise Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) and histone Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) read coverage for plant genomes. Predmoter uses only the DNA sequence as input. We trained our final model on 21 species for 13 of which ATAC-seq data and for 17 of which ChIP-seq data was publicly available. We evaluated our models on Arabidopsis thaliana and Oryza sativa. Our best models showed accurate predictions in peak position and pattern for ATAC- and histone ChIP-seq. Annotating putatively accessible chromatin regions provides valuable input for the identification of CREs. In conjunction with other in silico data, this can significantly reduce the search space for experimentally verifiable DNA–protein interaction pairs. Availability and implementation The source code for Predmoter is available at: https://github.com/weberlab-hhu/Predmoter. Predmoter takes a fasta file as input and outputs h5, and optionally bigWig and bedGraph files.
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- Language
- en
- Landing Page
- https://doi.org/10.1093/bioadv/vbae074
- https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbae074/57890195/vbae074.pdf
- OA Status
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- Cited By
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- References
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- Related Works
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All OpenAlex metadata
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Predmoter—cross-species prediction of plant promoter and enhancer regionsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
- Authors
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Felicitas Kindel, Sebastian Triesch, Urte Schlüter, Laura Alexandra Randarevitch, Vanessa Reichel-Deland, Andreas P.M. Weber, Alisandra K. DentonList of authors in order
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https://doi.org/10.1093/bioadv/vbae074Publisher landing page
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https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbae074/57890195/vbae074.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbae074/57890195/vbae074.pdfDirect OA link when available
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10Other works algorithmically related by OpenAlex
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