The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Thiol-linked alkylation for the metabolic sequencing of RNA
Open accessPreprint — published as 10.1038/nmeth.4435 · Paper page
Abstract
Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady-state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq), an orthogonal chemistry-based epitranscriptomics-sequencing technology that uncovers 4-thiouridine (s 4 U)-incorporation in RNA species at single-nucleotide resolution. In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAM-seq enables rapid access to RNA polymerase II-dependent gene expression dynamics in the context of total RNA. When applied to mouse embryonic stem cells, SLAM-seq provides global and transcript-specific insights into pluripotency-associated gene expression. We validated the method by showing that the RNA-polymerase II-dependent transcriptional output scales with Oct4/Sox2/Nanog-defined enhancer activity; and we provide quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N 6 -methyladenosine. SLAM-seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective, and scalable manner. One Sentence Summary: Chemical nucleotide-analog derivatization provides global insights into transcriptional and post-transcriptional gene regulation
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
MicroRNAs: Target Recognition and Regulatory Functions
Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.01.002 · Cited by 20,068
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Most mammalian mRNAs are conserved targets of microRNAs
Friedman, R. C., Farh, K. K.-H., Burge, C. B., & Bartel, D. P. (2009) · Genome Research · doi:10.1101/gr.082701.108 · Cited by 8,462
deepTools2: a next generation web server for deep-sequencing data analysis
Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw257 · Cited by 8,096
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 3×
“Note, that the ability to determine de novo synthesized transcripts will depend on (1) the s(4)U uptake kinetics of the chosen cellular system, (2) the overall transcriptional activity of the cell type and (3) the library sequencing depth.”
Background
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–500 of 627
Techniques for Analyzing Genome-wide Expression of Non-coding RNA
Onoguchi‐Mizutani, R., Taniue, K., Kawata, K., Yamada, T., & Akimitsu, N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91909-8.00039-6 · Cited by 3
Genetic dependencies associated with transcription factor activities in human cancer cell lines
Thatikonda, V., Supper, V., Wachter, J., Kaya, O., Kombara, A., Bilgilier, C., Ravichandran, M. C., Lipp, J., Sharma, R., Badertscher, L., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114175 · Cited by 3
Fast and furious: insights of back splicing regulation during nascent RNA synthesis
Xue, W., Ma, X.-K., & Yang, L. C. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1881-1 · Cited by 5
Machine Learning: A Tool to Shape the Future of Medicine
Hazapi, O., Lаgopati, N., Pezoulas, V. C., Papayiannis, G. I., Fotiadis, D. I., Skaltsas, D., Vergetis, V., Tsirigos, A., Stratis, I. G., Yannacopoulos, A. N., et al. (2022) · Studies in big data · doi:10.1007/978-981-16-9158-4_12 · Cited by 3
Extremely Rapid and Specific Metabolic Labelling of RNA In Vivo with 4-Thiouracil (Ers4tU)
Barrass, J. D., & Beggs, J. D. (2019) · Journal of Visualized Experiments · doi:10.3791/59952-v · Cited by 3
Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoformsPreprint
Rahmanian, S., Balderrama-Gutierrez, G., Wyman, D., McGill, C. J., Nguyen, K., Spitale, R. C., & Mortazavi, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.01.073296 · Cited by 3
Suresh, V., Hafemeister, C., Konstantinou, A., Grissenberger, S., Sturtzel, C., Cidre‐Aranaz, F., Wenninger-Weinzierl, A., Zylka, M. M., Queiroz, K. C. S., Kurek, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.03.647002 · Cited by 3
Z-Form Stabilization By The Zα Domain Of Adar1p150 Has Subtle Effects On A-To-I EditingPreprint
Nichols, P. J., Riemondy, K. A., Krall, J. B., Ramos, J., Goering, R., Henen, M. A., Taliaferro, J. M., Vögeli, B., & Vicens, Q. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.02.657529 · Cited by 3
Nucleosome stability safeguards cell identity, stress resilience and healthy agingPreprint
Tanaka, H., McCauley, B. S., Guida, C., Lei, X., Li, S., Moreno, T. M., Guillotte, K., Chua, Z. M., Abele, A., Lamba, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.17.676776 · Cited by 2
Expanding toolkit for RNP granule transcriptomics
Shichino, Y., & Iwasaki, S. (2025) · Polymer Journal · doi:10.1038/s41428-025-01035-7 · Cited by 4
Regulation of mRNA metabolism in immune cells
Negi, M. S., Soskic, B., & Legnini, I. (2025) · FEBS Letters · doi:10.1002/1873-3468.70132 · Cited by 2
Doxakis, E., Xue, Y. C., & Savulescu, A. F. (2025) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2025.1716825 · Cited by 2
Viral entry shapes HCMV latency establishment
Kitsberg, Y., Nachshon, A., Arazi, T., Broennimann, K., Fisher, T., Wainstein, A., Finkel, Y., Stern‐Ginossar, N., & Schwartz, M. (2025) · Nature Communications · doi:10.1038/s41467-025-68063-y · Cited by 2
P-bodies act as dynamic control hubs for RNA processing and storage
Feng, M. W., Mossanen-Parsi, A., Stonys, V., Hubbard, S. J., Ashe, M., & Grant, C. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111273 · Cited by 3
Metabolic RNA Labeling-Enabled Time-Resolved Single-Cell RNA Sequencing
Yin, K., Lin, S., & Yang, C. J. (2026) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6c00010 · Cited by 2
A cytomegalovirus-encoded lncRNA blocks cell-cycle progression
Fisher, T., Mizrahi, O., Tai-Schmiedel, J., Nachshon, A., Schwartz, M., Patrick, M., Gluck, A., Aharon, E., Karniely, S., & Stern‐Ginossar, N. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.02.025 · Cited by 2
Global stabilization of the transcriptome in mitotic cells
Khalizeva, E., Latifkar, A., Xiang, K., Bartel, D. P., Ly, J., & Cheeseman, I. M. (2026) · The EMBO Journal · doi:10.1038/s44318-026-00765-5 · Cited by 4
Vargas, J. N. S., Brown, A., Sun, K., Hagemann, C., Geary, B., Villarroel‐Campos, D., Bryce-Smith, S., Zanovello, M., Lombardo, M., Majewski, S., et al. (2026) · Science Signaling · doi:10.1126/scisignal.adx6752 · Cited by 9
Mintcheva, J., Tseng, T.-L., Goumenaki, P., Neuschulz, A., Senoussi, A., Lelek, S., Lim, K. L., Ming, Z., Schäfer, R., Hnatiuk, A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72781-2 · Cited by 2
Omics-Level Approaches to Studying Gammaherpesvirus Infection
Hisam, F., Konakalla, A. R., Berisha, E., Castro, M. D. C. C., Mukherjee, S., Wang, C., Sheirbon, B. R., Delgado, T., & Sanchez, E. L. (2026) · Pathogens · doi:10.3390/pathogens15070713 · Cited by 2
Tani, H. (2026) · IUBMB Life · doi:10.1002/iub.70122 · Cited by 2
Su, L., Zhao, F., Yu, H., Bai, M., Xue, J., Cao, X., Chen, F., & Zhao, Y. (2021) · Chemical Communications · doi:10.1039/d1cc06080e · Cited by 2
Cellular Metabolic Labeling of Nucleic Acids and Its Applications
He, Z., & Zhou, X. (2024) · Israel Journal of Chemistry · doi:10.1002/ijch.202300165 · Cited by 2
Ridenour, J. B., & Donczew, R. (2024) · BMC Methods · doi:10.1186/s44330-024-00008-1 · Cited by 2
Non-redundant roles for the human mRNA decapping cofactor paralogs DCP1a and DCP1b
Vukovic, I., Barnada, S. M., Ruffin, J. W., Karlin, J. E., Lokareddy, R., Cingolani, G., & McMahon, S. (2023) · Life Science Alliance · doi:10.26508/lsa.202402938 · Cited by 2
RNA kinetics influence the response to transcriptional perturbation in leukaemia cell lines
Todorovski, I., Tsang, M., Feran, B., Fan, Z., Gadipally, S., Yoannidis, D., Kong, I. Y., Bjelosevic, S., Rivera, S., Voulgaris, O., et al. (2024) · NAR Cancer · doi:10.1093/narcan/zcae039 · Cited by 2
Halfpipe: a tool for analyzing metabolic labeling RNA-seq data to quantify RNA half-lives
Müller, J. M., Altendorfer, E., Freier, S., Moos, K., Mayer, A., & Tresch, A. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf006 · Cited by 2
Cell size-dependent mRNA transcription drives proteome remodeling
You, D. S., Bohrer, C. H., Rumde, P. H., Sanidas, I., Swaffer, M. P., Larson, D. R., Elias, J. E., Lanz, M. C., & Skotheim, J. M. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117488 · Cited by 7
MYC modulates TOP2A diffusion to promote substrate detection and activity
Cameron, D. P., Jackson, K., Loffreda, A., Möller, C., Kuzin, V., Mazzocca, M., Iliopoulou, E., Kolbeinsdottir, H., Paluda, A., Pavlova, E., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69232-3 · Cited by 2
Chemical Synthesis of Modified RNA
Flemmich, L., Bereiter, R., & Micura, R. (2024) · Angewandte Chemie · doi:10.1002/ange.202403063 · Cited by 2
On the optimal design of metabolic RNA labeling experiments
Uvarovskii, A., Naarmann-de Vries, I. S., & Dieterich, C. (2018) · doi:10.1101/428862 · Cited by 2
Lewis, B. A., & Levens, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.23.917237 · Cited by 2
Recording the age of RNA with deaminationPreprint
Rodriques, S. G., Chen, L. M., Liu, S., Zhong, E. D., Scherrer, J. R., Boyden, E. S., & Chen, F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.08.939983 · Cited by 2
HISAT-3N: a rapid and accurate three-nucleotide sequence alignerPreprint
Zhang, Y., Park, C., Bennett, C. G., Thornton, M. A., & Kim, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.15.422906 · Cited by 2
High quality mapping of chromatin at or near the nuclear lamina for small numbers of cellsPreprint
Tran, J. R., Zheng, X., Adam, S. A., Goldman, R. D., & Zheng, Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.03.425156 · Cited by 2
DNA Processing in The Context of Non-Coding TranscriptionPreprint
Gowthaman, U., García‐Pichardo, D., Jin, Y., Schwarz, I., & Marquardt, S. (2020) · Preprints.org · doi:10.20944/preprints202007.0744.v1 · Cited by 1
Shlyueva, D., Teed-Singer, S., Zhan, Y. A., Koche, R. P., & Helin, K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.18.496661 · Cited by 2
Transcriptional boosting and buffering during neurodevelopment by shifts in mRNA instabilityPreprint
Mufteev, M., Rodrigues, D. C., Bolter, G., Yuki, K. E., Narula, A., Wei, W., Piekna, A., Liu, J., Pasceri, P., Rissland, O. S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.01.530249 · Cited by 2
Poised PABP-RNA hubs implement signal-dependent mRNA decay in developmentPreprint
Modic, M., Kuret, K., Steinhauser, S., Faraway, R., van Genderen, E., Lee, F. C. Y., Mozos, I. R. D. L., Vičič, Ž., Novljan, J., ten Berge, D., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3227673/v1 · Cited by 2
Marinov, G. K., Chen, X., Swaffer, M. P., Xiang, T., Grossman, A. R., & Greenleaf, W. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.18.558303 · Cited by 2
Incorporating extrinsic noise into mechanistic modelling of single-cell transcriptomicsPreprint
Öcal, K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.30.560282 · Cited by 2
Lyu, J., Xu, X., & Chen, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.22.609222 · Cited by 2
VanBelzen, J., Sakelaris, B. G., Brickner, D. G., Marcou, N., Riecke, H., Mangan, N. M., & Brickner, J. H. (2024) · eLife · doi:10.7554/elife.100764 · Cited by 3
Virus Entry is a Major Determinant of HCMV Latency in MonocytesPreprint
Kitsberg, Y., Nachshon, A., Arazi, T., Broennimann, K., Fisher, T., Wainstein, A., Finkel, Y., Stern‐Ginossar, N., & Schwartz, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.26.619803 · Cited by 2
Developmental dynamics and axonal transport of cerebral cortex projection neuron mRNAs in vivoPreprint
Veeraraghavan, P., Tillman, D. E., Ross, M. I., & Macklis, J. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.23.650263 · Cited by 2
Single-cell RNA-seq using UltraMarathonRT expands the known transcriptomePreprint
Chou, C.-L., Grinko, A., Guo, L., Leipold, A. M., Brunets, S. I., Rummel, T., Erhard, F., Pyle, A. M., & Saliba, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.06.680646 · Cited by 2
Shang, R., Popitsch, N. P., Lee, S., Ameres, S. L., & Lai, E. C. (2026) · Genes & Development · doi:10.1101/gad.353316.125 · Cited by 2
Self-citationExploring transcription modalities from bimodal, single-cell RNA sequencing data
Regényi, E., Mashreghi, M., Schütte, C., & Sunkara, V. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae179 · Cited by 1
Ogura, Y., Sun, X., Zhang, Z., Kawata, K., Wu, J., Matsubara, R., Ozeki, A. N., Taniue, K., Onoguchi‐Mizutani, R., Adachi, S., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-91828-w · Cited by 1
NaRaDa: A comprehensive nascent RNA database
Mai, Z., Li, D., Tang, P., Zhou, C., Zhao, Y., Chen, J., & Bao, X. (2025) · The Innovation Life · doi:10.59717/j.xinn-life.2025.100143 · Cited by 2
Chemical decoding strategies for detecting RNA modifications, dynamics and structures
Shu, X., & Li, W. (2025) · Chemical Communications · doi:10.1039/d5cc02984h · Cited by 1
Hu, Q., Li, M., Parida, M., Spector, B. M., Santana, J. F., Zandvakili, A., Price, D. H., & Meier, J. L. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1013374 · Cited by 1
Martinez, M. B., Williamson, C. J., Cazares, T. A., Tang, C., Debets, M. F., & Gangras, P. (2025) · Nucleic Acid Therapeutics · doi:10.1177/21593337251377561 · Cited by 1
TDP-43 directly inhibits mRNA accumulation in neurites through modulation of mRNA stability
Moffatt, C., Arora, A., Vaeth, K. F., Guzmán, B. B., Bhardwaj, G., Hoelscher, A., Gifford, L. B., Russ, H. A., Dominguez, D., & Taliaferro, J. (2025) · EMBO Journal · doi:10.1038/s44318-025-00653-4 · Cited by 2
Hu, Y., Wang, Y., Ye, M., He, Y., Ouyang, Z., Ren, C., Miao, T., Wang, S., Zhong, O., Liu, L., et al. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag173 · Cited by 2
Li-BrU-seq: A Low-Input and Simplified Metabolic Labeling Method for Nascent RNA Sequencing
Huang, Y., He, J.-T., Lan, Y.-L., Zhang, Z., Wu, F., Wang, C.-L., Wang, Q.-Y., Luo, G., & Zhang, Z. (2026) · ACS Chemical Biology · doi:10.1021/acschembio.5c01011 · Cited by 1
Exclusion of Notch from the contact site during efferocytosis restricts anticancer immunity
Li, Z., Xu, B., Sharma, P., Guy, C. S., Boada-Romero, E., Verbist, K., Guo, A., Mari, L., Poudel, S., Yang, M., et al. (2026) · Nature Immunology · doi:10.1038/s41590-026-02452-3 · Cited by 2
Reframing ME/CFS: toward a unified mechanistic model of chronic post-infectious diseases
Watton, P. N., & Prusty, B. K. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08319-3 · Cited by 1
Coordinated regulation of mRNA translation and stability by ZC3H7A and ZC3H7B RNA-binding proteins
Snell, P. H., Naeli, P., Garzia, A., Chen, Y., Waldron, J. A., Chatterjee, S., McGirr, T., Kilmartin, A., Almomani, E. S. M., Kelsall, I. R., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117511 · Cited by 1
Intron location and sequence modulate gene expression in Yarrowia lipolytica
Qi, Q., da Silva, P. T., Vangalis, V., Dockx, S., Steensels, J., Voordeckers, K., Gagneur, J., & Verstrepen, K. J. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag650 · Cited by 1
Qi, Y., Chen, Y., Guo, H., Guo, H., Hang, R., Jia, G., Li, Y., Mo, B., Wang, Z., Wu, L., et al. (2026) · Science China Life Sciences · doi:10.1007/s11427-026-3331-5 · Cited by 2
The Half-Life of a Mark: Dwell Time and the Missing Time Axis of the Epitranscriptome
Tani, H. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169944 · Cited by 1
Fujikawa, Y., Imai, H., Onuki, T., Hoshino, K., Velasco, M. A. D., Matsuo, K., Kurosawa, H., Aoyagi, K., Hirose, H., Nakamura, Y., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02892-1 · Cited by 1
Global Identification of Human Exosome Substrates Using RNA Interference and RNA Sequencing
Lloret-Llinares, M., & Jensen, T. H. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9822-7_7 · Cited by 1
Sodium Sulfite-Mediated s4U Conversion for m6A Profiling of Nascent Transcriptome-Wide RNA
Wei, Q., Han, S., Chen, Y., & Zhou, X. (2021) · 化学学报 · doi:10.6023/a20100477 · Cited by 1
Two-Step Parameter Estimation for Read Feature Models
Erhard, F. (2024) · Künstliche Intell. · doi:10.1007/s13218-023-00821-w · Cited by 1
Guidelines and important considerations for ‘omics-level studies
Luca, F., & Pai, A. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-817218-6.00013-9 · Cited by 1
Transcriptome dynamics of the BHK21 cell line in response to human coronavirus OC43 infection
Li, M., Yang, Y., Wang, P., Que, W., Zhong, L., Cai, Z., Liu, Y., Yang, L., & Liu, Y. (2024) · Microbiological Research · doi:10.1016/j.micres.2024.127750 · Cited by 1
Splice_sim: a nucleotide conversion-enabled RNA-seq simulation and evaluation framework
Popitsch, N. P., Neumann, T., von Haeseler, A., & Ameres, S. L. (2024) · Genome biology · doi:10.1186/s13059-024-03313-8 · Cited by 1
Self-citationAn expanded molecular and systems toolbox for imaging, mapping, and controlling local translation
Onchan, W., Attakitbancha, C., & Uttamapinant, C. (2024) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2024.102523 · Cited by 1
Nanoscale imaging of DNA-RNA identifies transcriptional plasticity at heterochromatin
Guillermier, C., Kumar, N. V. G., Bracken, R. C., Álvarez, D., O’Keefe, J., Gurkar, A. U., Brown, J. D., & Steinhauser, M. L. (2024) · Life Science Alliance · doi:10.26508/lsa.202402849 · Cited by 1
The extra-terminal domain drives the role of BET proteins in transcription
Pasionek, I., Ridenour, J. B., Machowska, A., Donczew, M., Kinter, M. T., Boyd, K. A., & Donczew, R. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag717 · Cited by 1
Zhang, Z., Luo, Z., Liu, Y., Sahoo, S. S., Dembele, V., Wu, M., Bardeesy, N. M., Huang, F., Kazemian, M., Cottrell, K. A., et al. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c07631 · Cited by 1
Imaging the cytoplasmic lives of single mRNAs.
Misiaszek, A., & Chao, J. A. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169513 · Cited by 1
Karner, H. M., Mittmann, T., Chen, V., Borah, A. A., Langen, A., Yousefi, H., Fish, L., Zaro, B. W., Navickas, A., & Goodarzi, H. (2026) · Science Advances · doi:10.1126/sciadv.aea9061 · Cited by 1
Shu, H., Donnard, E. R., Liu, B., & Richter, J. D. (2019) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · Cited by 1
Desbrousses, C., Archer, F., Colin, A., Erny, A., Champavère, A., Gros, E., Beurdeley, P., Cruveiller, S., Tardy, F., & Éloit, M. (2020) · Biologicals · doi:10.1016/j.biologicals.2020.03.002 · Cited by 1
Systematic refinement of gene annotations by parsing mRNA 3′ end sequencing datasets
Bhat, P., Burkard, T. R., Herzog, V. A., Pauli, A., & Ameres, S. L. (2021) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2021.03.016 · Cited by 1
Self-citationNon-radioactive In Vivo Labeling of RNA with 4-Thiouracil
Braun, C., Knüppel, R., Pérez-Fernández, J., & Ferreira-Cerca, S. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2501-9_12 · Cited by 1
Kawata, K., Wakida, H., Yamada, T., Taniue, K., Han, H., Seki, M., Suzuki, Y., & Akimitsu, N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.06.980250 · Cited by 1
Epiblast morphogenesis is controlled by selective mRNA decay triggered by LIN28A relocationPreprint
Modic, M., Mozos, I. R. D. L., Steinhauser, S., van Genderen, E., Schirge, S., Bergant, V., Ryan, J., Mulholland, C. B., Faraway, R., Lee, F. C. Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.15.433780 · Cited by 1
Newly synthesized RNA Sequencing Characterizes Transcription Dynamics in Three Pluripotent StatesPreprint
Shao, R., Kumar, B., Lidschreiber, K., Lidschreiber, M., Cramer, P., & Elsässer, S. (2021) · bioRxiv · doi:10.1101/2021.06.11.448016
Synergising single-cell resolution and 4sU labelling boosts inference of transcriptional burstingPreprint
Edwards, D. M., Davies, P., & Hebenstreit, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.08.506961 · Cited by 1
Flux analysis reveals specific regulatory modalities of gene expressionPreprint
Martin, B., & Suter, D. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.25.517929 · Cited by 1
A global, integrated view of the ubiquitylation site occupancy and dynamicsPreprint
Pruś, G., Satpathy, S., Weinert, B. T., Narita, T., & Choudhary, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.19.549470 · Cited by 1
A tRNA modification in Mycobacterium tuberculosis facilitates optimal intracellular growthPreprint
Tomasi, F. G., 木村聡, Rubin, E. J., & Waldor, M. K. (2023) · eLife · doi:10.7554/elife.87146.2 · Cited by 1
The CLAMP GA-binding transcription factor regulates heat stress-induced transcriptional repressionPreprint
Aguilera, J. L., Duan, J. E., Cortez, K., Lee, R. S., Aragon, A., Ray, M., & Larschan, E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.08.561401 · Cited by 1
Ridenour, J. B., & Donczew, R. (2023) · doi:10.17504/protocols.io.n2bvj3dj5lk5/v1 · Cited by 1
Schofield, J. A., & Hahn, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.08.584165 · Cited by 1
Identifying Modulators of Cellular Responses by Heterogeneity-sequencingPreprint
Berg, K., Sakellaridi, L., Rummel, T., Hennig, T., Whisnant, A. W., Lodha, M., Krammer, T., Toussaint, C., Wijs, K. S.-D., Zheng, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.28.620481 · Cited by 1
Shlyakhtina, Y., Bloechl, B., Moran, K. L., Maslakova, A., Johnson, R., Munro, A. F., Carragher, N., & Portal, M. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.08.622637 · Cited by 1
RNA editing is a molecular clock in unmodified human cellsPreprint
Ghareeb, A. E., Bayne, J., Wagen, A. Z., Abdelhafid, A. M., Miller, D. J., Cubitt, L., Meran, L., Hill, C. S., Cribbs, A. P., Ryten, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.18.624170 · Cited by 1
TDP-43 directly inhibits RNA accumulation in neurites through modulation of RNA stabilityPreprint
Moffatt, C., Arora, A., Vaeth, K. F., Guzmán, B. B., Bhardwaj, G., Hoelscher, A., Gifford, L. B., Russ, H. A., Dominguez, D., & Taliaferro, J. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.25.640092 · Cited by 1
Highly sensitive and scalable time-resolved RNA sequencing in single cells with scNT-seq2Preprint
Qiu, Q., Zhang, H., Gao, W. N. D., Li, F., Liang, D., & Wu, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.03.657745 · Cited by 1
Monziani, A., Unfried, J. P., Cvetanovic, T., & Ulitsky, I. (2025) · eLife · doi:10.7554/elife.106846 · Cited by 1
Global inhibition of deadenylation stabilizes the transcriptome in mitotic cellsPreprint
Khalizeva, E., Latifkar, A., Bartel, D. P., Ly, J., & Cheeseman, I. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.22.666109 · Cited by 1
Amyotrophic Lateral Sclerosis-associated 3′ UTR enhancer embedded within CAV1 risk genePreprint
Harrison, L. J., Moll, T., Cooper‐Knock, J., & Bose, D. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.09.675029
RNA degradation modulates unique aging-related gene expression in naked mole-ratsPreprint
Matsubara, R., Wu, J., Ozeki, A. N., Kawamura, Y., Hamada, M., Miura, K., & Akimitsu, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.28.691058 · Cited by 1
KAS-CUT&Tag for direct mapping of transcription bubblesPreprint
Wu, W., Greene, J. E., Ahmad, K., & Henikoff, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.15.725569 · Cited by 2
Spatio-temporally regulated maternal RNA decay factor essential for zebrafish developmentPreprint
Kushawah, G., Aldas-Bulos, V. D., Amaral, D. B., Hassan, H., Nowotarski, S. H., & Bazzini, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.20.745964 · Cited by 1
Metadata
- Authors
- Herzog, V. A., Reichholf, B., Neumann, T., Rescheneder, P., Bhat, P., Burkard, T. R., Wlotzka, W., von Haeseler, A., Zuber, J., & Ameres, S. L.
- Affiliations
- Institute of Molecular Biotechnology (AT)
- Research Institute of Molecular Pathology (AT)
- Max Perutz Labs (AT)
- Corresponding author
- Ameres, Stefan L.
- ORCID
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2017-08-17
- Volume
- 14
- Issue
- 12
- Pages
- 1198-1204
- ISSN
- 1548-7091 (ISSN-L)
- PMC
- PMC5712218
- OpenAlex
- W2759506905
- Cited by
- 622
- References
- 66
- Citations by year
- 2026: 80 · 2025: 79 · 2024: 117 · 2023: 84 · 2022: 71 · 2021: 73 · 2020: 52 · 2019: 44 · 2018: 21 · 2017: 1
- FWCI
- 13.19
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Österreichischen Akademie der Wissenschaften
- Austrian Science Fund (W12-7-B09, Y-733-B22 START, F4322)
- Wirtschaftsagentur Wien
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- SLAM-seq, RNA turnover, RNA metabolism, RNA polymerase II transcription, gene expression dynamics, microRNA, N6-methyladenosine, post-transcriptional regulation, mouse embryonic stem cells, enhancer activity
- MeSH terms
- Alkylation, RNA, RNA Polymerase II, RNA Processing, Post-Transcriptional, Sulfhydryl Compounds, Thiouridine, Gene Expression Profiling, Gene Regulatory Networks, Embryonic Stem Cells, High-Throughput Screening Assays, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review