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Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data
Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific bin...
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Published in: | Genome biology 2024-10, Vol.25 (1), p.284-284, Article 284 |
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description | Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed.
We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts.
Our work provides new strategies for predicting the functional impact of non-coding variants. |
doi_str_mv | 10.1186/s13059-024-03424-2 |
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We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts.
Our work provides new strategies for predicting the functional impact of non-coding variants.</description><identifier>ISSN: 1474-760X</identifier><identifier>EISSN: 1474-760X</identifier><identifier>DOI: 10.1186/s13059-024-03424-2</identifier><identifier>PMID: 39482734</identifier><language>eng</language><publisher>England: BMC</publisher><subject>Allele-specific binding ; Alleles ; Benchmarking ; Binding Sites ; binomial distribution ; ChIP-seq, ChIP-exo, CUT&Tag ; chromatin immunoprecipitation ; Chromatin Immunoprecipitation Sequencing ; CTCF, EBF1, PU.1/SPI1 ; DNA ; DNA - genetics ; DNA - metabolism ; DNA fragmentation ; Gene expression regulation ; genome ; heterozygosity ; Humans ; Non-coding variants ; prediction ; probability ; Protein Binding ; Transcription factors ; Transcription Factors - genetics ; Transcription Factors - metabolism</subject><ispartof>Genome biology, 2024-10, Vol.25 (1), p.284-284, Article 284</ispartof><rights>2024. The Author(s).</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c462t-b52d9161b8ed494fd58770e7b5ef231918f2f36aa843af36157f48a04d2ee3f63</cites><orcidid>0000-0002-7563-7554 ; 0000-0002-7274-5277 ; 0000-0003-4938-7587</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906,36994</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39482734$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Xiaoting</creatorcontrib><creatorcontrib>Melo, Lucas A N</creatorcontrib><creatorcontrib>Bussemaker, Harmen J</creatorcontrib><title>Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data</title><title>Genome biology</title><addtitle>Genome Biol</addtitle><description>Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed.
We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts.
Our work provides new strategies for predicting the functional impact of non-coding variants.</description><subject>Allele-specific binding</subject><subject>Alleles</subject><subject>Benchmarking</subject><subject>Binding Sites</subject><subject>binomial distribution</subject><subject>ChIP-seq, ChIP-exo, CUT&Tag</subject><subject>chromatin immunoprecipitation</subject><subject>Chromatin Immunoprecipitation Sequencing</subject><subject>CTCF, EBF1, PU.1/SPI1</subject><subject>DNA</subject><subject>DNA - genetics</subject><subject>DNA - metabolism</subject><subject>DNA fragmentation</subject><subject>Gene expression regulation</subject><subject>genome</subject><subject>heterozygosity</subject><subject>Humans</subject><subject>Non-coding variants</subject><subject>prediction</subject><subject>probability</subject><subject>Protein Binding</subject><subject>Transcription factors</subject><subject>Transcription Factors - genetics</subject><subject>Transcription Factors - metabolism</subject><issn>1474-760X</issn><issn>1474-760X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqNUU1v1DAUjBAVLYU_wAHlyCXgbyfH0haoVJULSNysF_t5cfHGi50ceuw_x80uK45c3huPZkbPmqZ5Q8l7Snv1oVBO5NARJjrCRZ3sWXNGhRadVuTH83_wafOylHtC6CCYetGc8kH0THNx1jx-xMn-3EL-FaZNC5NrxyVE9_S4urtoxzCtGLwPU5gf2m1yGEu7lJWNESN2ZYc2-GBX-4GDzZTKXLk5w1RsDrs5pKn1YOeUj7EOZnjVnHiIBV8f9nnz_dP1t8sv3e3XzzeXF7edFYrN3SiZG6iiY49ODMI72WtNUI8SPeN0oL1nniuAXnCogErtRQ9EOIbIveLnzc0-1yW4N7sc6qcfTIJgViLljYFcL45oGOGjAuu45EJIpcFTUEgdR6nR8qFmvdtn7XL6vWCZzTYUizHChGkphlMpaC81p_8hZZxoQQSpUraX2pxKyeiPV1Jinho3-8ZNbdysjRtWTW8P-cu4RXe0_K2Y_wHl5Ke1</recordid><startdate>20241031</startdate><enddate>20241031</enddate><creator>Li, Xiaoting</creator><creator>Melo, Lucas A N</creator><creator>Bussemaker, Harmen J</creator><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-7563-7554</orcidid><orcidid>https://orcid.org/0000-0002-7274-5277</orcidid><orcidid>https://orcid.org/0000-0003-4938-7587</orcidid></search><sort><creationdate>20241031</creationdate><title>Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data</title><author>Li, Xiaoting ; Melo, Lucas A N ; Bussemaker, Harmen J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-b52d9161b8ed494fd58770e7b5ef231918f2f36aa843af36157f48a04d2ee3f63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Allele-specific binding</topic><topic>Alleles</topic><topic>Benchmarking</topic><topic>Binding Sites</topic><topic>binomial distribution</topic><topic>ChIP-seq, ChIP-exo, CUT&Tag</topic><topic>chromatin immunoprecipitation</topic><topic>Chromatin Immunoprecipitation Sequencing</topic><topic>CTCF, EBF1, PU.1/SPI1</topic><topic>DNA</topic><topic>DNA - genetics</topic><topic>DNA - metabolism</topic><topic>DNA fragmentation</topic><topic>Gene expression regulation</topic><topic>genome</topic><topic>heterozygosity</topic><topic>Humans</topic><topic>Non-coding variants</topic><topic>prediction</topic><topic>probability</topic><topic>Protein Binding</topic><topic>Transcription factors</topic><topic>Transcription Factors - genetics</topic><topic>Transcription Factors - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xiaoting</creatorcontrib><creatorcontrib>Melo, Lucas A N</creatorcontrib><creatorcontrib>Bussemaker, Harmen J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Genome biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xiaoting</au><au>Melo, Lucas A N</au><au>Bussemaker, Harmen J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data</atitle><jtitle>Genome biology</jtitle><addtitle>Genome Biol</addtitle><date>2024-10-31</date><risdate>2024</risdate><volume>25</volume><issue>1</issue><spage>284</spage><epage>284</epage><pages>284-284</pages><artnum>284</artnum><issn>1474-760X</issn><eissn>1474-760X</eissn><abstract>Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed.
We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts.
Our work provides new strategies for predicting the functional impact of non-coding variants.</abstract><cop>England</cop><pub>BMC</pub><pmid>39482734</pmid><doi>10.1186/s13059-024-03424-2</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-7563-7554</orcidid><orcidid>https://orcid.org/0000-0002-7274-5277</orcidid><orcidid>https://orcid.org/0000-0003-4938-7587</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Allele-specific binding Alleles Benchmarking Binding Sites binomial distribution ChIP-seq, ChIP-exo, CUT&Tag chromatin immunoprecipitation Chromatin Immunoprecipitation Sequencing CTCF, EBF1, PU.1/SPI1 DNA DNA - genetics DNA - metabolism DNA fragmentation Gene expression regulation genome heterozygosity Humans Non-coding variants prediction probability Protein Binding Transcription factors Transcription Factors - genetics Transcription Factors - metabolism |
title | Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data |
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