ArticlePublished: 15 May 2026Micah Jonathan Goldrich1,2 na1,Louis Delhaye ORCID: orcid.org/0000-0001-8876-73853,4,5,6 na1,Sarah-Lee Bekaert ORCID: orcid.org/0000-0002-4769-34603,4,5,Bieke Decaesteker3,4,5,Filip Van Nieuwerburgh ORCID: orcid.org/0000-0001-8815-54855,7,Frank Speleman ORCID: orcid.org/0000-0002-6628-85593,4,5,Sven Eyckerman3,5,6,Pieter Mestdagh ORCID: orcid.org/0000-0001-7821-96843,4,5 na2 &…Igor Ulitsky ORCID: orcid.org/0000-0003-0555-65611,2 na2 Nature Biotechnology (2026) Cite this articleSubjectsLong non-coding RNAsRNATranscriptomicsAbstractSeveral high-throughput sequencing methods have been used to study the genome-wide chromatin occupancy of long noncoding RNAs (lncRNAs), including ChIRP-seq, CHART-seq and RAP-seq. Many of the datasets obtained with these methods contain thousands of binding sites, which appears to be in contradiction with the low abundance of the interrogated lncRNAs. Here, we study the chromatin interactome of NESPR lncRNA in cells with varying levels of endogenous expression and perform a meta-analysis using dozens of RNA–chromatin interaction datasets in human and mouse cells. We demonstrate that thousands of regions reported to bind lncRNAs most likely arise from the spurious recovery of DNA elements, where the ends of the recovered DNA fragments exhibit partial complementarity with the probes used for the pulldown. In addition, crucial controls were rarely used in previous studies. Therefore, most chromatin regions reported as bound by trans-acting RNAs in recent studies in mammalian cells appear to be technical artifacts. We provide suggestions for assessing the quality of RNA–chromatin datasets and their improvement.This is a preview of subscription content, access via your institutionAccess optionsAccess Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription27,99 € / 30 dayscancel any timeLearn moreSubscribe to this journalReceive 12 print issues and online access269,00 € per yearonly 22,42 € per issueLearn moreBuy this articlePurchase on SpringerLinkInstant access to the full article PDF.39,95 €Prices may be subject to local taxes which are calculated during checkoutFig. 1: ChIRP-seq suggests a trans-acting function for NESPR.The alternative text for this image may have been generated using AI.Fig. 2: Stringent experimental controls demonstrate genomic DNA enrichment independent of NESPR RNA enrichment.The alternative text for this image may have been generated using AI.Fig. 3: Peak distributions and properties across human samples, including matched replicates.The alternative text for this image may have been generated using AI.Fig. 4: Peaks recovered in chromatin–RNA interaction studies often share k-mers with the probes.The alternative text for this image may have been generated using AI.Fig. 5: Longest k-mers and association of k-mers with read ends.The alternative text for this image may have been generated using AI.Fig. 6: CHART-seq and RAP-seq method comparison.The alternative text for this image may have been generated using AI.Data availabilityThe ChIRP-sequencing data generated in this study were deposited to the National Center for Biotechnology Information GEO under accession number GSE307444).Code availabilityThe full pipeline for our analyses is available from GitHub (https://github.com/IgorUlitsky/RNAChromatin/).ReferencesCabili, M. 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This work was supported by Fonds Wetenschappelijk Onderzoek (1275923N to L.D., G0D8619N to P.M.), Bijzonder Onderzoeksfond Universiteit Gent (BOF22/PDO/024 to L.D., BOF16/GOA/023 and BOF.GOA.2022.003.03 to F.S. and S.E.), Stichting Tegen Kanker (FAF-F/2018/1176 to P.M.), the European Research Council (Consolidator Grant lncIMPACT to I.U.), the Abisch-Frenkel RNA Therapeutics Center at the Weizmann Institute (M.J.G. and I.U.) and the Israeli Ministry of Health as part of the ERA4Health joint call, RECREATE consortium (grant number 3-19415).Author informationAuthor notesThese authors contributed equally: Micah Jonathan Goldrich, Louis Delhaye.These authors jointly supervised this work: Pieter Mestdagh, Igor Ulitsky.Authors and AffiliationsDepartment of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, IsraelMicah Jonathan Goldrich & Igor UlitskyDepartment of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, IsraelMicah Jonathan Goldrich & Igor UlitskyDepartment of Biomolecular Medicine, Ghent University, Ghent, BelgiumLouis Delhaye, Sarah-Lee Bekaert, Bieke Decaesteker, Frank Speleman, Sven Eyckerman & Pieter MestdaghCenter for Medical Genetics, Ghent University Hospital, Ghent, BelgiumLouis Delhaye, Sarah-Lee Bekaert, Bieke Decaesteker, Frank Speleman & Pieter MestdaghCancer Research Institute Ghent, Ghent University, Ghent, BelgiumLouis Delhaye, Sarah-Lee Bekaert, Bieke Decaesteker, Filip Van Nieuwerburgh, Frank Speleman, Sven Eyckerman & Pieter MestdaghVIB-UGent Center for Medical Biotechnology, VIB, Ghent, BelgiumLouis Delhaye & Sven EyckermanNXTGNT, Ghent University, Ghent, BelgiumFilip Van NieuwerburghAuthorsMicah Jonathan GoldrichView author publicationsSearch author on:PubMed Google ScholarLouis DelhayeView author publicationsSearch author on:PubMed Google ScholarSarah-Lee BekaertView author publicationsSearch author on:PubMed Google ScholarBieke DecaestekerView author publicationsSearch author on:PubMed Google ScholarFilip Van NieuwerburghView author publicationsSearch author on:PubMed Google ScholarFrank SpelemanView author publicationsSearch author on:PubMed Google ScholarSven EyckermanView author publicationsSearch author on:PubMed Google ScholarPieter MestdaghView author publicationsSearch author on:PubMed Google ScholarIgor UlitskyView author publicationsSearch author on:PubMed Google ScholarContributionsM.J.G., L.D., P.M. and I.U. conceptualized the project. L.D. performed all NESPR experiments and analyzed all data related to NESPR. S.B., B.D. and F.S. assisted in NESPR data processing. S.E. provided shRNA constructs and lentiviral particles. As part of the NXTGNT sequencing core, F.V.N. supervised library preparation of NESPR ChIRP datasets. M.J.G. and I.U. analyzed the public datasets. M.J.G., L.D., P.M. and I.U. prepared the manuscript with input from other authors.Corresponding authorsCorrespondence to Pieter Mestdagh or Igor Ulitsky.Ethics declarationsCompeting interestsThe authors declare no competing interests.Peer reviewPeer review informationNature Biotechnology thanks Nadya Dimitrova and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 NESPR ChIRP-seq peaks are not linked to gene expression changes upon NESPR depletion.a, Colorimetric RNAscope analysis of NESPR in IMR-32 and SHEP cells, scale bar shows 20 μm. b, Normalized NESPR enrichment over input in ChIRP-seq experiments using NESPR and lacZ capture probe sets. c, NESPR ChIRP-seq coverage around the TSS of DEGs following NESPR depletion. d, Distance of NESPR ChIRP-seq peaks to the TSS of DEGs following NESPR depletion; I, input; P, pulldown; DEG, differentially expressed genes; nt, nucleotides; TSS, transcription start site.Extended Data Fig. 2 NESPR enrichment in ChIRP-seq experiments in IMR-32 and SHEP cells.a, NESPR enrichment over GAPDH in ChIRP-seq fractions in IMR-32 (with and without RNase treatment) and SHEP. Dark blue, IMR-32 cells (high NESPR expression); orange, SHEP cells (no NESPR expression).Extended Data Fig. 3 Peak distributions across mouse samples, and affect of read depth on peak numbers.a, As in Fig. 3b for mouse samples. b, Scatter plots of total sequencing reads versus number of identified peaks in human (left) and mouse (right). Vertical dotted lines: median read count. Green shaded areas: median reads ± MAD. Horizontal dashed lines: upper and lower quartiles of peak counts. Points are color-coded by method; black outlines denote subsampled libraries. Pearson correlation coefficients R are shown for pooled data and method-specific cohorts. c, Total number of identified peaks as a function of the subsampling fraction (percentage of total reads) for selected human (left) and mouse (right) samples. d, Percentage of peaks detected in subsampling relative to the full, non-subsampled dataset as a function of the subsampling fraction.Extended Data Fig. 4 Peak distributions across mouse matched samples.a, An example of a region in the mouse genome containing a very common peak and a common peak, with the peaks from the individual studies overlapping it. b, As in Fig. 3c for mouse samples.Extended Data Fig. 5 Signal-over-background profiles of peak k-mers in human samples.a, Of the peaks that have a kmer match of the indicated length, the color indicates that fraction that occured in peaks that overlap the GOI. b, Signal-over-background values are plotted for k-mers of length 7 to 20 across grouped human samples. Sample groups are ordered by the average number of peaks per group, arranged from left to right and top to bottom. Groupings were defined based on identical experimental conditions, probe target, and biological replicate number. When applicable, both “even” and “odd” probe sets were included; otherwise, single-probe sets (“single”) were used.Extended Data Fig. 6 Signal-over-background profiles of peak k-mers in mouse samples.Signal-over-background values are plotted for k-mers of length 7 to 20 across grouped mouse samples. Sample groups are ordered by the average number of peaks per group, arranged from left to right and top to bottom. Groupings were defined based on identical experimental conditions, probe target, and biological replicate number. When applicable, both “even” and “odd” probe sets were included; otherwise, single-probe sets (“single”) were used. The Haunt lncRNA study23 mentioned in the Discussion is shaded.Extended Data Fig. 7 Relative positions of k-mer hit midpoints across peak sets in a dataset for the LED lncRNA.Distributions of the center positions of true (bold lines) and shuffled (dashed lines) k-mer hits are shown across different peak sets, for k-mers of length ≥7, ≥11, ≥15, ≥19, and for the longest k-mer per peak. Only probe peak sets with at least 25 peaks are shown to avoid plotting potentially spurious distributions.Extended Data Fig. 8 Motif coverage along the DINO lncRNA transcript.a, Aligned motifs along “even” and “odd” probe regions in DINO transcript. b, Left: Distribution of motif coverage along the DINO lncRNA transcript. Right: Boxplots of motif coverage stratified by whether the nucleotide overlaps with probe target sites or not. n = 80 for the even and odd probes (4 probes × 20 nt), n = 791 for the rest. The boxplots show the median, 25th and 75th percentiles, and whiskers extend to the most extreme data points that are within 1.5 times the Interquartile Range (IQR).Extended Data Fig. 9 Correspondence between Jpx CHART-seq and RD-SPRITE datasets.a, For each of six different Jpx CHART-seq experiments, each point represents a published peak. The x-axis is the average DNA read coverage from RD-SPRITE clusters that contain Jpx RNA, and the y-axis is the significance calculated by comparing that value to those of 1,000 random shufflings of the peak position, controlling for the chromosome. The number of peaks meeting the indicated significance computed by the empirical test before correction for multiple hypothesis testing (P) and after applying Benjamini-Hochberg correction (Q) is shown. b, as in a, normalizing the DNA read coverage in each peak by the coverage in all the clusters together.Extended Data Fig. 10 Kmer analysis of the NESPR ChIRP-seq data.a, Signal/noise for the presence of kmers of indicated length in NESPR ChIRP-seq peaks from the indicated sample. b, enrichment of the longest kmers near ends of reads from the indicated samples in NESPR ChIRP-seq data.Supplementary informationReporting Summary (download PDF )Peer Review File (download PDF )Supplementary Tables 1–9 (download XLSX )Supplementary Table 1. Processed published studies, including information on the methods, probes and their types and different treatments and controls. Supplementary Table 2. Probes used in the different studies, including for NESPR. Supplementary Table 3. Published peak sets reported in the different studies. Supplementary Table 4. Primers and probes used for studying NESPR. Supplementary Table 5. GOI coordinates. Supplementary Table 6. Very common peaks in the human datasets. Supplementary Table 7. Common peaks in the human datasets. Supplementary Table 8. Very common peaks in the mouse datasets. Supplementary Table 9. Common peaks in the mouse datasets.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this article