--- _id: '11341' abstract: - lang: eng text: Intragenic regions that are removed during maturation of the RNA transcript—introns—are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein genes that differ primarily in their introns2,3. Although studies have shed light on the role of ribosomal protein introns under stress and starvation4,5,6, understanding the contribution of introns to ribosome regulation remains challenging. Here, by combining isogrowth profiling7 with single-cell protein measurements8, we show that introns can mediate inducible phenotypic heterogeneity that confers a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, which is mediated by an intron in the 5′ untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low levels of Rps22B protein result in prolonged survival under sustained starvation, whereas high levels of Rps22B enable cells to grow faster after transient starvation. Furthermore, yeasts growing at high concentrations of sugar, similar to those in ripe grapes, exhibit bimodal expression of Rps22B when approaching the stationary phase. Differential intron-mediated regulation of ribosomal protein genes thus provides a way to diversify the population when starvation threatens in natural environments. Our findings reveal a role for introns in inducing phenotypic heterogeneity in changing environments, and suggest that duplicated ribosomal protein genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness9. acknowledged_ssus: - _id: LifeSc - _id: M-Shop - _id: Bio acknowledgement: We thank the IST Austria Life Science Facility, the Miba Machine Shop and M. Lukačišinová for support with the liquid handling robot; the Bioimaging Facility at IST Austria, J. Power and B. Meier at the University of Cologne, and C. Göttlinger at the FACS Analysis Facility at the Institute for Genetics, University of Cologne, for support with flow cytometry experiments; L. Horst for the development of the automated experimental methods in Cologne; J. Parenteau, S. Abou Elela, G. Stormo, M. Springer and M. Schuldiner for providing us with yeast strains; B. Fernando, T. Fink, G. Ansmann and G. Chevreau for technical support; H. Köver, G. Tkačik, N. Barton, A. Angermayr and B. Kavčič for support during laboratory relocation; D. Siekhaus, M. Springer and all the members of the Bollenbach group for support and discussions; and K. Mitosch, M. Lukačišinová, G. Liti and A. de Luna for critical reading of our manuscript. This work was supported in part by an Austrian Science Fund (FWF) standalone grant P 27201-B22 (to T.B.), HFSP program Grant RGP0042/2013 (to T.B.), EU Marie Curie Career Integration Grant No. 303507, and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). A.E.-C. was supported by a Georg Forster fellowship from the Alexander von Humboldt Foundation. article_processing_charge: No article_type: original author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Adriana full_name: Espinosa-Cantú, Adriana last_name: Espinosa-Cantú - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisin M, Espinosa-Cantú A, Bollenbach MT. Intron-mediated induction of phenotypic heterogeneity. Nature. 2022;605:113-118. doi:10.1038/s41586-022-04633-0 apa: Lukacisin, M., Espinosa-Cantú, A., & Bollenbach, M. T. (2022). Intron-mediated induction of phenotypic heterogeneity. Nature. Springer Nature. https://doi.org/10.1038/s41586-022-04633-0 chicago: Lukacisin, Martin, Adriana Espinosa-Cantú, and Mark Tobias Bollenbach. “Intron-Mediated Induction of Phenotypic Heterogeneity.” Nature. Springer Nature, 2022. https://doi.org/10.1038/s41586-022-04633-0. ieee: M. Lukacisin, A. Espinosa-Cantú, and M. T. Bollenbach, “Intron-mediated induction of phenotypic heterogeneity,” Nature, vol. 605. Springer Nature, pp. 113–118, 2022. ista: Lukacisin M, Espinosa-Cantú A, Bollenbach MT. 2022. Intron-mediated induction of phenotypic heterogeneity. Nature. 605, 113–118. mla: Lukacisin, Martin, et al. “Intron-Mediated Induction of Phenotypic Heterogeneity.” Nature, vol. 605, Springer Nature, 2022, pp. 113–18, doi:10.1038/s41586-022-04633-0. short: M. Lukacisin, A. Espinosa-Cantú, M.T. Bollenbach, Nature 605 (2022) 113–118. date_created: 2022-05-01T22:01:42Z date_published: 2022-05-05T00:00:00Z date_updated: 2023-08-03T06:44:50Z day: '05' ddc: - '570' doi: 10.1038/s41586-022-04633-0 ec_funded: 1 external_id: isi: - '000784934100003' pmid: - '35444278' file: - access_level: open_access checksum: d68cd1596bb9fd819b750fe47c8a138a content_type: application/pdf creator: dernst date_created: 2022-08-05T06:08:24Z date_updated: 2022-08-05T06:08:24Z file_id: '11727' file_name: 2022_Nature_Lukacisin.pdf file_size: 25360311 relation: main_file success: 1 file_date_updated: 2022-08-05T06:08:24Z has_accepted_license: '1' intvolume: ' 605' isi: 1 language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '05' oa: 1 oa_version: Published Version page: 113-118 pmid: 1 project: - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions publication: Nature publication_identifier: eissn: - 1476-4687 issn: - 0028-0836 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Intron-mediated induction of phenotypic heterogeneity tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 605 year: '2022' ... --- _id: '7026' abstract: - lang: eng text: Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper’s transparent peer review process is included in the Supplemental Information. acknowledged_ssus: - _id: LifeSc article_processing_charge: No article_type: original author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisin M, Bollenbach MT. Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. 2019;9(5):423-433.e1-e3. doi:10.1016/j.cels.2019.10.004 apa: Lukacisin, M., & Bollenbach, M. T. (2019). Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2019.10.004 chicago: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems. Cell Press, 2019. https://doi.org/10.1016/j.cels.2019.10.004. ieee: M. Lukacisin and M. T. Bollenbach, “Emergent gene expression responses to drug combinations predict higher-order drug interactions,” Cell Systems, vol. 9, no. 5. Cell Press, pp. 423-433.e1-e3, 2019. ista: Lukacisin M, Bollenbach MT. 2019. Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. 9(5), 423-433.e1-e3. mla: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems, vol. 9, no. 5, Cell Press, 2019, pp. 423-433.e1-e3, doi:10.1016/j.cels.2019.10.004. short: M. Lukacisin, M.T. Bollenbach, Cell Systems 9 (2019) 423-433.e1-e3. date_created: 2019-11-15T10:51:42Z date_published: 2019-11-27T00:00:00Z date_updated: 2023-08-30T07:24:58Z day: '27' ddc: - '570' department: - _id: ToBo doi: 10.1016/j.cels.2019.10.004 external_id: isi: - '000499495400003' file: - access_level: open_access checksum: 7a11d6c2f9523d65b049512d61733178 content_type: application/pdf creator: dernst date_created: 2019-11-15T10:57:42Z date_updated: 2020-07-14T12:47:48Z file_id: '7027' file_name: 2019_CellSystems_Lukacisin.pdf file_size: 4238460 relation: main_file file_date_updated: 2020-07-14T12:47:48Z has_accepted_license: '1' intvolume: ' 9' isi: 1 issue: '5' language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 423-433.e1-e3 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Cell Systems publication_identifier: issn: - 2405-4712 publication_status: published publisher: Cell Press quality_controlled: '1' scopus_import: '1' status: public title: Emergent gene expression responses to drug combinations predict higher-order drug interactions tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 9 year: '2019' ... --- _id: '6392' abstract: - lang: eng text: "The regulation of gene expression is one of the most fundamental processes in living systems. In recent years, thanks to advances in sequencing technology and automation, it has become possible to study gene expression quantitatively, genome-wide and in high-throughput. This leads to the possibility of exploring changes in gene expression in the context of many external perturbations and their combinations, and thus of characterising the basic principles governing gene regulation. In this thesis, I present quantitative experimental approaches to studying transcriptional and protein level changes in response to combinatorial drug treatment, as well as a theoretical data-driven approach to analysing thermodynamic principles guiding transcription of protein coding genes. \r\nIn the first part of this work, I present a novel methodological framework for quantifying gene expression changes in drug combinations, termed isogrowth profiling. External perturbations through small molecule drugs influence the growth rate of the cell, leading to wide-ranging changes in cellular physiology and gene expression. This confounds the gene expression changes specifically elicited by the particular drug. Combinatorial perturbations, owing to the increased stress they exert, influence the growth rate even more strongly and hence suffer the convolution problem to a greater extent when measuring gene expression changes. Isogrowth profiling is a way to experimentally abstract non-specific, growth rate related changes, by performing the measurement using varying ratios of two drugs at such concentrations that the overall inhibition rate is constant. Using a robotic setup for automated high-throughput re-dilution culture of Saccharomyces cerevisiae, the budding yeast, I investigate all pairwise interactions of four small molecule drugs through sequencing RNA along a growth isobole. Through principal component analysis, I demonstrate here that isogrowth profiling can uncover drug-specific as well as drug-interaction-specific gene expression changes. I show that drug-interaction-specific gene expression changes can be used for prediction of higher-order drug interactions. I propose a simplified generalised framework of isogrowth profiling, with few measurements needed for each drug pair, enabling the broad application of isogrowth profiling to high-throughput screening of inhibitors of cellular growth and beyond. Such high-throughput screenings of gene expression changes specific to pairwise drug interactions will be instrumental for predicting the higher-order interactions of the drugs.\r\n\r\nIn the second part of this work, I extend isogrowth profiling to single-cell measurements of gene expression, characterising population heterogeneity in the budding yeast in response to combinatorial drug perturbation while controlling for non-specific growth rate effects. Through flow cytometry of strains with protein products fused to green fluorescent protein, I discover multiple proteins with bi-modally distributed expression levels in the population in response to drug treatment. I characterize more closely the effect of an ionic stressor, lithium chloride, and find that it inhibits the splicing of mRNA, most strongly affecting ribosomal protein transcripts and leading to a bi-stable behaviour of a small ribosomal subunit protein Rps22B. Time-lapse microscopy of a microfluidic culture system revealed that the induced Rps22B heterogeneity leads to preferential survival of Rps22B-low cells after long starvation, but to preferential proliferation of Rps22B-high cells after short starvation. Overall, this suggests that yeast cells might use splicing of ribosomal genes for bet-hedging in fluctuating environments. I give specific examples of how further exploration of cellular heterogeneity in yeast in response to external perturbation has the potential to reveal yet-undiscovered gene regulation circuitry.\r\n\r\nIn the last part of this thesis, a re-analysis of a published sequencing dataset of nascent elongating transcripts is used to characterise the thermodynamic constraints for RNA polymerase II (RNAP) elongation. Population-level data on RNAP position throughout the transcribed genome with single nucleotide resolution are used to infer the sequence specific thermodynamic determinants of RNAP pausing and backtracking. This analysis reveals that the basepairing strength of the eight nucleotide-long RNA:DNA duplex relative to the basepairing strength of the same sequence when in DNA:DNA duplex, and the change in this quantity during RNA polymerase movement, is the key determinant of RNAP pausing. This is true for RNAP pausing while elongating, but also of RNAP pausing while backtracking and of the backtracking length. The quantitative dependence of RNAP pausing on basepairing energetics is used to infer the increase in pausing due to transcriptional mismatches, leading to a hypothesis that pervasive RNA polymerase II pausing is due to basepairing energetics, as an evolutionary cost for increased RNA polymerase II fidelity.\r\n\r\nThis work advances our understanding of the general principles governing gene expression, with the goal of making computational predictions of single-cell gene expression responses to combinatorial perturbations based on the individual perturbations possible. This ability would substantially facilitate the design of drug combination treatments and, in the long term, lead to our increased ability to more generally design targeted manipulations to any biological system. " acknowledged_ssus: - _id: LifeSc - _id: M-Shop - _id: Bio alternative_title: - IST Austria Thesis author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 citation: ama: Lukacisin M. Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. 2019. doi:10.15479/AT:ISTA:6392 apa: Lukacisin, M. (2019). Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. IST Austria. https://doi.org/10.15479/AT:ISTA:6392 chicago: Lukacisin, Martin. “Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory.” IST Austria, 2019. https://doi.org/10.15479/AT:ISTA:6392. ieee: M. Lukacisin, “Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory,” IST Austria, 2019. ista: Lukacisin M. 2019. Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. IST Austria. mla: Lukacisin, Martin. Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory. IST Austria, 2019, doi:10.15479/AT:ISTA:6392. short: M. Lukacisin, Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory, IST Austria, 2019. date_created: 2019-05-09T19:53:00Z date_published: 2019-05-09T00:00:00Z date_updated: 2023-09-22T09:19:41Z day: '09' ddc: - '570' department: - _id: ToBo doi: 10.15479/AT:ISTA:6392 extern: '1' file: - access_level: closed checksum: 829bda074444857c7935171237bb7c0c content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: mlukacisin date_created: 2019-05-10T13:51:49Z date_updated: 2020-07-14T12:47:29Z embargo_to: open_access file_id: '6409' file_name: Thesis_Draft_v3.4Final.docx file_size: 43740796 relation: hidden - access_level: open_access checksum: 56cb5e97f5f8fc41692401b53832d8e0 content_type: application/pdf creator: mlukacisin date_created: 2019-05-10T14:13:42Z date_updated: 2021-02-11T11:17:16Z embargo: 2020-04-17 file_id: '6410' file_name: Thesis_Draft_v3.4FinalA.pdf file_size: 35228388 relation: main_file file_date_updated: 2021-02-11T11:17:16Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '103' publication_identifier: isbn: - 978-3-99078-001-5 issn: - 2663-337X publication_status: published publisher: IST Austria related_material: record: - id: '1029' relation: part_of_dissertation status: public status: public supervisor: - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X title: Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory type: dissertation user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2019' ... --- _id: '5563' abstract: - lang: eng text: "MATLAB code and processed datasets available for reproducing the results in: \r\nLukačišin, M.*, Landon, M.*, Jajoo, R*. (2016) Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.\r\n*equal contributions" article_processing_charge: No author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 citation: ama: Lukacisin M. MATLAB analysis code for “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” 2017. doi:10.15479/AT:ISTA:64 apa: Lukacisin, M. (2017). MATLAB analysis code for “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:64 chicago: Lukacisin, Martin. “MATLAB Analysis Code for ‘Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.’” Institute of Science and Technology Austria, 2017. https://doi.org/10.15479/AT:ISTA:64. ieee: M. Lukacisin, “MATLAB analysis code for ‘Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.’” Institute of Science and Technology Austria, 2017. ista: Lukacisin M. 2017. MATLAB analysis code for ‘Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast’, Institute of Science and Technology Austria, 10.15479/AT:ISTA:64. mla: Lukacisin, Martin. MATLAB Analysis Code for “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” Institute of Science and Technology Austria, 2017, doi:10.15479/AT:ISTA:64. short: M. Lukacisin, (2017). datarep_id: '64' date_created: 2018-12-12T12:31:33Z date_published: 2017-03-20T00:00:00Z date_updated: 2024-02-21T13:46:47Z day: '20' ddc: - '571' department: - _id: ToBo doi: 10.15479/AT:ISTA:64 file: - access_level: open_access checksum: ee697f2b1ade4dc14d6ac0334dd832ab content_type: application/zip creator: system date_created: 2018-12-12T13:02:37Z date_updated: 2020-07-14T12:47:03Z file_id: '5602' file_name: IST-2016-45-v1+1_PaperCode.zip file_size: 296722548 relation: main_file file_date_updated: 2020-07-14T12:47:03Z has_accepted_license: '1' license: https://creativecommons.org/licenses/by-sa/4.0/ month: '03' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria status: public title: MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast' tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2017' ... --- _id: '1029' abstract: - lang: eng text: RNA Polymerase II pauses and backtracks during transcription, with many consequences for gene expression and cellular physiology. Here, we show that the energy required to melt double-stranded nucleic acids in the transcription bubble predicts pausing in Saccharomyces cerevisiae far more accurately than nucleosome roadblocks do. In addition, the same energy difference also determines when the RNA polymerase backtracks instead of continuing to move forward. This data-driven model corroborates—in a genome wide and quantitative manner—previous evidence that sequence-dependent thermodynamic features of nucleic acids influence both transcriptional pausing and backtracking. article_number: e0174066 article_processing_charge: Yes author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Matthieu full_name: Landon, Matthieu last_name: Landon - first_name: Rishi full_name: Jajoo, Rishi last_name: Jajoo citation: ama: Lukacisin M, Landon M, Jajoo R. Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast. PLoS One. 2017;12(3). doi:10.1371/journal.pone.0174066 apa: Lukacisin, M., Landon, M., & Jajoo, R. (2017). Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0174066 chicago: Lukacisin, Martin, Matthieu Landon, and Rishi Jajoo. “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” PLoS One. Public Library of Science, 2017. https://doi.org/10.1371/journal.pone.0174066. ieee: M. Lukacisin, M. Landon, and R. Jajoo, “Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast,” PLoS One, vol. 12, no. 3. Public Library of Science, 2017. ista: Lukacisin M, Landon M, Jajoo R. 2017. Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast. PLoS One. 12(3), e0174066. mla: Lukacisin, Martin, et al. “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” PLoS One, vol. 12, no. 3, e0174066, Public Library of Science, 2017, doi:10.1371/journal.pone.0174066. short: M. Lukacisin, M. Landon, R. Jajoo, PLoS One 12 (2017). date_created: 2018-12-11T11:49:46Z date_published: 2017-03-16T00:00:00Z date_updated: 2024-03-27T23:30:05Z day: '16' ddc: - '570' department: - _id: ToBo doi: 10.1371/journal.pone.0174066 external_id: isi: - '000396318300121' file: - access_level: open_access content_type: application/pdf creator: system date_created: 2018-12-12T10:09:47Z date_updated: 2018-12-12T10:09:47Z file_id: '4772' file_name: IST-2017-800-v1+1_journal.pone.0174066.pdf file_size: 3429381 relation: main_file file_date_updated: 2018-12-12T10:09:47Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '3' language: - iso: eng month: '03' oa: 1 oa_version: Published Version publication: PLoS One publication_identifier: issn: - '19326203' publication_status: published publisher: Public Library of Science publist_id: '6361' pubrep_id: '800' quality_controlled: '1' related_material: record: - id: '5556' relation: popular_science status: public - id: '6392' relation: dissertation_contains status: public scopus_import: '1' status: public title: Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 12 year: '2017' ... --- _id: '5556' abstract: - lang: eng text: "MATLAB code and processed datasets available for reproducing the results in: \r\nLukačišin, M.*, Landon, M.*, Jajoo, R*. (2016) Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.\r\n*equal contributions" article_processing_charge: No author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Matthieu full_name: Landon, Matthieu last_name: Landon - first_name: Rishi full_name: Jajoo, Rishi last_name: Jajoo citation: ama: Lukacisin M, Landon M, Jajoo R. MATLAB analysis code for “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” 2016. doi:10.15479/AT:ISTA:45 apa: Lukacisin, M., Landon, M., & Jajoo, R. (2016). MATLAB analysis code for “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:45 chicago: Lukacisin, Martin, Matthieu Landon, and Rishi Jajoo. “MATLAB Analysis Code for ‘Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.’” Institute of Science and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:45. ieee: M. Lukacisin, M. Landon, and R. Jajoo, “MATLAB analysis code for ‘Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.’” Institute of Science and Technology Austria, 2016. ista: Lukacisin M, Landon M, Jajoo R. 2016. MATLAB analysis code for ‘Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast’, Institute of Science and Technology Austria, 10.15479/AT:ISTA:45. mla: Lukacisin, Martin, et al. MATLAB Analysis Code for “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:45. short: M. Lukacisin, M. Landon, R. Jajoo, (2016). datarep_id: '45' date_created: 2018-12-12T12:31:31Z date_published: 2016-08-25T00:00:00Z date_updated: 2024-02-21T13:51:53Z day: '25' ddc: - '571' department: - _id: ToBo doi: 10.15479/AT:ISTA:45 file: - access_level: open_access checksum: ee697f2b1ade4dc14d6ac0334dd832ab content_type: application/zip creator: system date_created: 2018-12-12T13:02:58Z date_updated: 2020-07-14T12:47:02Z file_id: '5616' file_name: IST-2016-45-v1+1_PaperCode.zip file_size: 296722548 relation: main_file file_date_updated: 2020-07-14T12:47:02Z has_accepted_license: '1' keyword: - transcription - pausing - backtracking - polymerase - RNA - NET-seq - nucleosome - basepairing month: '08' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: record: - id: '8431' relation: used_in_publication status: deleted - id: '1029' relation: research_paper status: public status: public title: MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast' tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2016' ...