Science & Nature
Data availability
Next-generation sequencing data have been deposited to the Gene Expression Omnibus with accession code GSE219035 (ref. 64). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with identifier PXD038502 (ref. 65). Source data are provided with this paper.
Code availability
The workflow to perform ({K_{mathrm{d}}^{mathrm{App}}}) determination from raw count files is available at https://github.com/HNeikes/BANCseq.
References
Slattery, M. et al. Absence of a simple code: how transcription factors read the genome. Trends Biochem. Sci. 39, 381–399 (2014).
Lappalainen, T. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Res. 25, 1427–1431 (2015).
Serebreni, L. & Stark, A. Insights into gene regulation: from regulatory genomic elements to DNA–protein and protein–protein interactions. Curr. Opin. Cell Biol. 70, 58–66 (2021).
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).
Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).
Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).
Makowski, M. M. et al. Global profiling of protein–DNA and protein–nucleosome binding affinities using quantitative mass spectrometry. Nat. Commun. 9, 1653 (2018).
Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).
Cheng, C. et al. Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Res. 22, 1658–1667 (2012).
Zhu, F. et al. The interaction landscape between transcription factors and the nucleosome. Nature 562, 76–81 (2018).
Zaret, K. S. & Mango, S. E. Pioneer transcription factors, chromatin dynamics, and cell fate control. Curr. Opin. Genet. Dev. 37, 76–81 (2016).
Wunderlich, Z. & Mirny, L. A. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet. 25, 434–440 (2009).
Keilwagen, J., Posch, S. & Grau, J. Accurate prediction of cell type-specific transcription factor binding. Genome Biol. 20, 9 (2019).
Cirillo, L. A. et al. Opening of compacted chromatin by early developmental transcription factors HNF3 (FoxA) and GATA-4. Mol. Cell 9, 279–289 (2002).
Stormo, G. D. & Zhao, Y. Determining the specificity of protein–DNA interactions. Nat. Rev. Genet. 11, 751–760 (2010).
Fried, M. & Crothers, D. M. Equilibria and kinetics of lac repressor–operator interactions by polyacrylamide gel electrophoresis. Nucleic Acids Res. 9, 6505–6525 (1981).
Maerkl, S. J. & Quake, S. R. A systems approach to measuring the binding energy landscapes of transcription factors. Science 315, 233–237 (2007).
Quang, D. & Xie, X. FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods 166, 40–47 (2019).
Rube, H. T. et al. Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning. Nat. Biotechnol. 40, 1520–1527 (2022).
Geertz, M. & Maerkl, S. J. Experimental strategies for studying transcription factor–DNA binding specificities. Brief. Funct. Genomics 9, 362–373 (2010).
Weintraub, A. S. et al. YY1 is a structural regulator of enhancer–promoter loops. Cell 171, 1573–1588 (2017).
Golebiowski, F. M. et al. An investigation of the affinities, specificity and kinetics involved in the interaction between the Yin Yang 1 transcription factor and DNA. FEBS J. 279, 3147–3158 (2012).
Houbaviy, H. B. & Burley, S. K. Thermodynamic analysis of the interaction between YY1 and the AAV P5 promoter initiator element. Chem. Biol. 8, 179–187 (2001).
Lace, M. J. et al. Cellular factor YY1 downregulates the human papillomavirus 16 E6/E7 promoter, P97, in vivo and in vitro from a negative element overlapping the transcription-initiation site. J. Gen. Virol. 90, 2402–2412 (2009).
Usheva, A. & Shenk, T. YY1 transcriptional initiator: protein interactions and association with a DNA site containing unpaired strands. Proc. Natl Acad. Sci. USA 93, 13571–13576 (1996).
Belak, Z. R. & Ovsenek, N. Assembly of the Yin Yang 1 transcription factor into messenger ribonucleoprotein particles requires direct RNA binding activity. J. Biol. Chem. 282, 37913–37920 (2007).
Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).
Maity, S. N. & de Crombrugghe, B. Role of the CCAAT-binding protein CBF/NF-Y in transcription. Trends Biochem. Sci 23, 174–178 (1998).
Seachrist, D. D., Anstine, L. J. & Keri, R. A. FOXA1: a pioneer of nuclear receptor action in breast cancer. Cancers 13, 5205 (2021).
Fu, X. et al. FOXA1 upregulation promotes enhancer and transcriptional reprogramming in endocrine-resistant breast cancer. Proc. Natl Acad. Sci. USA 116, 26823–26834 (2019).
Bergsland, M., Werme, M., Malewicz, M., Perlmann, T. & Muhr, J. The establishment of neuronal properties is controlled by Sox4 and Sox11. Genes Dev. 20, 3475–3486 (2006).
Rivera-Mulia, J. C. et al. Allele-specific control of replication timing and genome organization during development. Genome Res. 28, 800–811 (2018).
Deplancke, B., Alpern, D. & Gardeux, V. The genetics of transcription factor DNA binding variation. Cell 166, 538–554 (2016).
Phair, R. D. et al. Global nature of dynamic protein–chromatin interactions in vivo: three-dimensional genome scanning and dynamic interaction networks of chromatin proteins. Mol. Cell. Biol. 24, 6393–6402 (2004).
Papaneophytou, C. P., Grigoroudis, A. I., McInnes, C. & Kontopidis, G. Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein–ligand binding affinity. ACS Med. Chem. Lett. 5, 931–936 (2014).
Banerjee, A., Hu, J. & Goss, D. J. Thermodynamics of protein–protein interactions of cMyc, Max, and Mad: effect of polyions on protein dimerization. Biochemistry 45, 2333–2338 (2006).
Kyung, C. J., Ho, S. R., Chi, H. P. & Yang, C. H. Determination of the dissociation constants for recombinant c-Myc, Max, and DNA complexes: the inhibitory effect of linoleic acid on the DNA-binding step. Biochem. Biophys. Res. Commun. 334, 269–275 (2005).
Fujioka, A. et al. Dynamics of the Ras/ERK MAPK cascade as monitored by fluorescent probes. J. Biol. Chem. 281, 8917–8926 (2006).
Smits, A. H. et al. Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs. Nucleic Acids Res. 42, 9880–9891 (2014).
Lindeboom, R. G. et al. Integrative multi‐omics analysis of intestinal organoid differentiation. Mol. Syst. Biol. 14, e8227 (2018).
Bonnet, J. et al. Quantification of proteins and histone marks in Drosophila embryos reveals stoichiometric relationships impacting chromatin regulation. Dev. Cell 51, 632–644 (2019).
Article
CAS
PubMed
Google ScholarSchwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
Makowski, M. M. et al. An interaction proteomics survey of transcription factor binding at recurrent TERT promoter mutations. Proteomics 16, 417–426 (2016).
Zeller, P. et al. Single-cell sortChIC identifies hierarchical chromatin dynamics during hematopoiesis. Nat. Genet. 55, 333–345 (2022).
Artegiani, B. et al. Probing the tumor suppressor function of BAP1 in CRISPR-engineered human liver organoids. Cell Stem Cell 24, 927–943 (2019).
Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).
Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Chawla, K., Tripathi, S., Thommesen, L., Lægreid, A. & Kuiper, M. TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors. Bioinformatics 29, 2519–2520 (2013).
van der Sande, M. et al. seq2science. Zenodo https://doi.org/10.5281/ZENODO.5788729 (2021).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Gräwe, C., Makowski, M. M. & Vermeulen, M. PAQMAN: protein–nucleic acid affinity quantification by mass spectrometry in nuclear extracts. Methods 184, 70–77 (2020).
Elzhov, T. v., Mullen, K., Spiess, A. & Bolker, B. minpack.lm: R interface to the Levenberg–Marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds. rdrr.io https://rdrr.io/cran/minpack.lm/ (2015).
Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T. & Flicek, P. R. The ensembl regulatory build. Genome Biol. 16, 56 (2015).
Khan, A. & Mathelier, A. Intervene: a tool for intersection and visualization of multiple gene or genomic region sets. BMC Bioinformatics 18, 287 (2017).
van der Auwera, G. A. & O’Connor, B. D. Genomics in the cloud (O’Reilly Media, Inc., 2020).
Bruse, N. & van Heeringen, S. J. GimmeMotifs: an analysis framework for transcription factor motif analysis. Preprint at bioRxiv https://doi.org/10.1101/474403 (2018).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Santos-Barriopedro, I., van Mierlo, G. & Vermeulen, M. Off-the-shelf proximity biotinylation for interaction proteomics. Nat. Commun. 12, 5015 (2021).
Neikes, H. K. et al. BANC-seq for determination of genome-wide apparent transcription factor binding affinities. NCBI https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE219035 (2023).
Neikes, H. K. et al. BANC-seq to identify genome-wide transcription factor binding affinities to native chromatin. ProteomeXchange http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD038502 (2023).
Acknowledgements
We thank M. M. Makowski, G. van Mierlo and all members of the Vermeulen laboratory for fruitful discussions. We thank the laboratory of J. Gribnau for sharing the hybrid mESCs for this study. Furthermore, we thank S. Kefalopoulou and P. Zeller of the Hubrecht Institute for technical support with the CUT&RUN protocol. The Vermeulen laboratory is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society (KWF). Furthermore, work in the Vermeulen laboratory is supported by an ERC Consolidator Grant (SysOrganoid; 771059).
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Competing interests
In the past 3 years, S.A.T. has consulted for Genentech and Roche and sits on Scientific Advisory Boards for Qiagen, Foresite Labs, Biogen and GlaxoSmithKline and is a co-founder and equity holder of Transition Bio. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Quality controls for BANC-seq experiments.
(a) Heatmap showing copy numbers per cell or nucleus of detected transcription factors before and after nuclear isolation. (b) Anti-FLAG western blot of FLAG-YY1 in nuclei or protein incubation buffer, immediately after nuclear permeabilization and after 10 minutes of incubation, with or without nuclear permeabilization by pulse sonication. The experiment was repeated twice with similar results. (c) Recovery (as percentage (%) of input chromatin) at the human PTBP1 promoter and a random genomic site by ChIP-qPCR per time point of incubating 1000 nM FLAG-YY1 in MCF-7 nuclei. Bars represent the median recovery, individual dots represent three measurements (n = 3) of each individual titration point. (d) Recovery (as percentage (%) of input chromatin) at the human PTBP1 promoter and a random genomic site by ChIP-qPCR per titration point of FLAG-YY1 in MCF-7 nuclei. Bars represent the median recovery, individual dots represent three measurements (n = 3) of each individual titration point. (e) Heatmap showing copy numbers per nucleus of detected transcription factors in MCF-7 nuclei (left heatmap) or nuclei of F121 mESCs, R1 NPCs or R1 mESCs (right heatmap), in triplicate. (f) Table depicting the average copy numbers per nucleus for each cell type and transcription factor tested in this study. (g) Box plots representing the (K_d^{Apps}) for MYC/MAX from BANC-seq performed in MCF-7 nuclei with different titration ranges (Left: Six titration points ranging from 0 to 500 nM FLAG-MYC/MAX complex, n = 623, right: Five titration points ranging from 0 to 1000 nM FLAG-MYC/MAX complex, n = 203), with or without addition of His-tagged MYC at a constant concentration of 250 nM (left) or 1000 nM (right). p-values of a two-sided Wilcoxon test are reported. Box plots were drawn with the center line as the median, the hinges as the first and third quartiles, and with the whiskers extending to the lowest and highest values that were within 1.5 × interquartile range.
Extended Data Fig. 2 Overview of results of additional BANC-seq experiments.
(a) Venn diagrams of overlap between sites with fitted high affinity (K_d^{Apps}) by BANC-seq and endogenous ChIP-seq peaks of the respective transcription factor in the respective organism. (b) Distribution of (K_d^{Apps}) of MYC and YY1 in MCF-7 nuclei. BANC-seq experiments were performed using 5 titration points, and YY1 apparent binding affinities were probed either with the ChIP-seq or CUT&RUN-based protocol. Dotted lines indicate the tested concentrations per experiment. (c) Heatmap representing spike-in normalized sequencing reads relative to the highest signal for the same experiments as in (b). Each row represents one transcription factor binding site. The overlap of each binding site with peaks from endogenous ChIP-seq experiments of the same transcription factor is shown to the left of each heatmap, while (K_d^{Apps}) to the right. (d) Distance (bp) of identified transcription factor binding sites relative to the nearest transcription start site (TSS).
Extended Data Fig. 3 BANC-seq derived (K_d^{Apps}) comparison with other methods.
(a) Spike-in normalized sequencing reads per site and titration point of FLAG-SP1 in MCF-7 relative to the highest signal for the BBC3 and KLF3 regulatory element (dotted line indicating the (K_d^{Apps})). (b) Top: EMSA of 1 nM biotinylated dsDNA binding to recombinant FLAG-SP1 in a concentration range between 5-4000 nM for the BBC3 and KLF3 regulatory element. The experiments were performed thrice with similar results, and one representative image was chosen for visualization. Bottom: Quantification of immunoblotting results, with FLAG-SP1 concentration shown in the logarithmic scale and the bound fraction determined by the ImageJ software. Data are presented as mean ± s.d. c) Overview of detected (K_d^{Apps}) by the different methods for the sites depicted in (a). (d) Relative quantification by PAQMAN of SP1 binding in MCF-7 nuclear lysate to the same sequences as in (b). Data are presented as mean ± SEM of two experiments (n = 2). (e) Venn diagram depicting the overlapping and unique proteins that bind to the tested sequences from (d) with high affinity in PAQMAN. (f) Spike-in normalized sequencing reads per site and titration point of FLAG-SP1 in MCF-7 relative to the highest signal for the MEMO1 and RNF223 regulatory element (dotted line indicating the (K_d^{Apps})). (g) Top: EMSA of 1 nM biotinylated dsDNA binding to recombinant FLAG-SP1 in a concentration range between 5-4000 nM for the MEMO1 and RNF223 regulatory element. The experiments were performed thrice with similar results, and one representative image was chosen for visualization. Bottom: Quantification of immunoblotting results, with FLAG-SP1 concentration shown in the logarithmic scale and the bound fraction determined by the ImageJ software. Data are presented as mean ± s.d. (h) Overview of detected (K_d^{Apps}) by the different methods for the sites depicted in (f).
Extended Data Fig. 4 Regulatory elements show differences in motif distribution and strength.
(a) Left: Bar plots representing the genome wide distribution of regulatory elements (top), or accessible regulatory elements (bottom). Right top and bottom; per tested transcription factor: Bar plots representing the distribution of regulatory elements at sites with the respective binding motif of each factor (first bar plot), at sites with the motif that are accessible (second bar plot), at sites with the motif and detected high confidence (K_d^{App}) (bound, third bar plot) and at sites with the motif that are accessible and detected high confidence (K_d^{App}) for the respective transcription factor. (b) Box plots representing z-scores of motif strength for the respective transcription factors per regulatory element. Numbers at the bottom of each plot represent the number of sites in each group. p-values of a two-sided Wilcoxon test are reported. Box plots were drawn with the center line as the median, the hinges as the first and third quartiles, and with the whiskers extending to the lowest and highest values that were within 1.5 × interquartile range.
Extended Data Fig. 5 Transcription factor specific motifs versus generic motifs in high versus low affinity binding sites.
(a) Blue and red bar plot representing the overlap between promoters bound by YY1, MYC or SP1, separately for promoters assigned to be 20% highest or lowest affinity binding sites for all possible combinations of the three transcription factors. Grey bar plot to the left representing the total size of each promoter set. (b) Bar plot representing p-values of two-tailed hypergeometric tests for enrichment (-log10) of top motifs per transcription factor for either high or low affinity binding sites. Motif logos are depicted on the left of the plot, names of associated transcription factors (if known) on the right.
Extended Data Fig. 6 Overview of the chromatin context and correlation with (K_d^{Apps}) for all transcription factors.
Boxplots representing log2 fold change of ATAC-seq (a), H3K4me1 ChIP-seq (b) or H3K4me3 ChIP-seq (c) signal over the mean signal of matched control tracks or z-scores of the representative motifs (d) for all tested transcription factors at sites with high confidence (K_d^{Apps}) fitted. Sites are ranked by (K_d^{App}) and divided into quintiles based on (K_d^{Apps}) per experiment. Rho (r) and p-value from Spearman correlation of the respective epigenome signal and (K_d^{Apps}) are included above the boxplots. Box plots were drawn with the center line as the median, the hinges as the first and third quartiles, and with the whiskers extending to the lowest and highest values that were within 1.5 × interquartile range. Spearman correlation coefficient and two-tailed p-value comparing affinities and epigenomic signal or motif are reported.
Extended Data Fig. 7 FOXA1 binds hyperaccessible promoters with low affinity upon overexpression in MCF-7.
(a) Heatmap showing the matched epigenome dynamics at sites with high-confidence (K_d^{Apps}) fitted for FOXA1 at either gained or retained sites after FOXA1 overexpression. Signal of ChIP-seq and ATAC-seq tracks for MCF-7 is shown as log2 fold change over the mean signal in matched control tracks, sites are ranked by apparent binding affinity (second column), and assigned regulatory features are depicted in the first column to the left. (b) Overlap of gained or retained FOXA1 binding sites with known regulatory features. (c – e) Boxplots representing the log2 fold change of ATAC-seq, H3K4me1 ChIP-seq or H3K4me3 ChIP-seq signal over the mean signal in matched control tracks, separated by sites being gained and retained sites after FOXA1 overexpression. n = numbers of gained or retained sites overlapping with FOXA1 high confidence sites. p-values of a two-sided Wilcoxon test are reported. (f) Boxplots representing the FOXA1 motif z-score at gained or retained sites after FOXA1 overexpression. p-values of a two-sided Wilcoxon test are reported. (g) Distance (bp) of gained or retained sites to the nearest transcription start site (TSS). Box plots were drawn with the center line as the median, the hinges as the first and third quartiles, and with the whiskers extending to the lowest and highest values that were within 1.5 × interquartile range.
Extended Data Fig. 8 NPC specific (K_d^{Apps}) are associated with neuronal-specific gene sets.
(a) Snapshot of R1 NPC culture. NPCs were cultured in the same way at least three times, showing similar morphology. Scale bar: 0.1 mm. (b) Relative expression of pluripotency (Klf4, Nanog) and NPC specific (Nestin, Sox1, Pax6) marker genes in NPCs as compared to mESCs, normalized for the expression of a housekeeping gene, determined by qPCR. Bars represent the median value, individual dots represent three measurements (n = 3) of each gene. (c) Scatterplot representing the log2 fold change of cell type specific DNA accessibility (as determined by ATAC-sequencing, y-axis) relative to the log2 fold change of (K_d^{App}) for SP1 (x-axis) in the NPC vs. mESC comparison for each high confidence binding site (colour-coded for NPC specific (pink), ESC specific (green) or shared sites (black)). Rho (r) and two-tailed p-value from Spearman correlation are included in the plot. (d) Spike-in normalized sequencing reads per titration point of FLAG-SP1 at the mouse Garem2 promoter (NPC specific) relative to the highest signal (dotted line indicating the (K_d^{Apps})) in NPC (pink) and ESC (green). (e) Same as (d), but visualized in the UCSC genome browser, with additionally one representative replicate of the DNA accessibility signal (by ATAC-seq) in ESCs (left) and NPCs (right). Pearson correlation coefficient and two-tailed p-value comparing the fitted and observed relative signal are reported. (f) Same as (d), but for the Sox11 promoter (shared site). (g) Same as (e), but for the Sox11 promoter (shared site). (h) Bar plot representing results of a gene set enrichment analyses results based on the differences in (K_d^{Apps}) between NPCs and ESCs at high confidence shared sites, color coded by p-value. A negative normalized enrichment score (NES) represents gene sets associated with lower (K_d^{Apps}) (that is higher transcription factor binding affinity) for SP1 in NPCs as compared to ESCs and vice versa. Permutation based two-sided p values are shown as color-coding.
Extended Data Fig. 9 Affinity dependent binding of transcription factor target genes.
(a) Pie charts representing the proportions of significantly enriched gene sets (FDR < 0.05) per Molecular Signatures Database collection for the different transcription factors. (b-d) Heatmaps representing enrichment of genes from various gene sets over the range of (K_d^{Apps}) for SP1, FOXA1 and MYC/MAX complex in MCF-7. Sites are ranked by (K_d^{Apps}) (top heatmap per experiment) and gaussian kernel density estimates of the density of highly significant gene sets (FDR < 0.001) over the ranked (K_d^{Apps}) values are visualized to show that some gene sets are enriched at certain transcription factor (K_d^{Apps}).
Extended Data Fig. 10 Minor sequence variations in and near the consensus motif of YY1 fine-tune apparent binding affinities.
(a) Spike-in normalized sequencing reads per allele and titration point of FLAG-YY1 in F121 mESCs relative to the highest signal at the Qars promoter (Pink: Castaneus, green: 129/Sv). Vertical lines indicating the (K_d^{Apps}). Pearson correlation coefficients and two-tailed p-values comparing the fitted and observed relative signal are reported. (b) Binding ratios (log2 scale) of proteins identified by DNA-pulldown followed by mass spec with oligonucleotides identical to the sequences depicted in (a). Blue dot and arrow indicate Yy1.
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Neikes, H.K., Kliza, K.W., Gräwe, C. et al. Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq.
Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01715-w
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DOI: https://doi.org/10.1038/s41587-023-01715-w
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Hannah K. Neikes