Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors.
Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1005176 |
| _version_ | 1818735073566916608 |
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| author | Saeed Omidi Mihaela Zavolan Mikhail Pachkov Jeremie Breda Severin Berger Erik van Nimwegen |
| author_facet | Saeed Omidi Mihaela Zavolan Mikhail Pachkov Jeremie Breda Severin Berger Erik van Nimwegen |
| author_sort | Saeed Omidi |
| collection | DOAJ |
| description | Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts. Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites. We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform 'motif finding', i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT 'dilogo' motifs. |
| first_indexed | 2024-12-18T00:15:27Z |
| format | Article |
| id | doaj.art-aa3dd7e6d78d40c9a5134f79b59fa0ed |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-18T00:15:27Z |
| publishDate | 2017-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-aa3dd7e6d78d40c9a5134f79b59fa0ed2022-12-21T21:27:32ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-07-01137e100517610.1371/journal.pcbi.1005176Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors.Saeed OmidiMihaela ZavolanMikhail PachkovJeremie BredaSeverin BergerErik van NimwegenGene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts. Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites. We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform 'motif finding', i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT 'dilogo' motifs.https://doi.org/10.1371/journal.pcbi.1005176 |
| spellingShingle | Saeed Omidi Mihaela Zavolan Mikhail Pachkov Jeremie Breda Severin Berger Erik van Nimwegen Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. PLoS Computational Biology |
| title | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. |
| title_full | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. |
| title_fullStr | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. |
| title_full_unstemmed | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. |
| title_short | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. |
| title_sort | automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
| url | https://doi.org/10.1371/journal.pcbi.1005176 |
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