---
_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. <i>Nature</i>. 2022;605:113-118. doi:<a href="https://doi.org/10.1038/s41586-022-04633-0">10.1038/s41586-022-04633-0</a>
  apa: Lukacisin, M., Espinosa-Cantú, A., &#38; Bollenbach, M. T. (2022). Intron-mediated
    induction of phenotypic heterogeneity. <i>Nature</i>. Springer Nature. <a href="https://doi.org/10.1038/s41586-022-04633-0">https://doi.org/10.1038/s41586-022-04633-0</a>
  chicago: Lukacisin, Martin, Adriana Espinosa-Cantú, and Mark Tobias Bollenbach.
    “Intron-Mediated Induction of Phenotypic Heterogeneity.” <i>Nature</i>. Springer
    Nature, 2022. <a href="https://doi.org/10.1038/s41586-022-04633-0">https://doi.org/10.1038/s41586-022-04633-0</a>.
  ieee: M. Lukacisin, A. Espinosa-Cantú, and M. T. Bollenbach, “Intron-mediated induction
    of phenotypic heterogeneity,” <i>Nature</i>, 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.”
    <i>Nature</i>, vol. 605, Springer Nature, 2022, pp. 113–18, doi:<a href="https://doi.org/10.1038/s41586-022-04633-0">10.1038/s41586-022-04633-0</a>.
  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:
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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. <i>Cell Systems</i>. 2019;9(5):423-433.e1-e3.
    doi:<a href="https://doi.org/10.1016/j.cels.2019.10.004">10.1016/j.cels.2019.10.004</a>
  apa: Lukacisin, M., &#38; Bollenbach, M. T. (2019). Emergent gene expression responses
    to drug combinations predict higher-order drug interactions. <i>Cell Systems</i>.
    Cell Press. <a href="https://doi.org/10.1016/j.cels.2019.10.004">https://doi.org/10.1016/j.cels.2019.10.004</a>
  chicago: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression
    Responses to Drug Combinations Predict Higher-Order Drug Interactions.” <i>Cell
    Systems</i>. Cell Press, 2019. <a href="https://doi.org/10.1016/j.cels.2019.10.004">https://doi.org/10.1016/j.cels.2019.10.004</a>.
  ieee: M. Lukacisin and M. T. Bollenbach, “Emergent gene expression responses to
    drug combinations predict higher-order drug interactions,” <i>Cell Systems</i>,
    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.” <i>Cell Systems</i>,
    vol. 9, no. 5, Cell Press, 2019, pp. 423-433.e1-e3, doi:<a href="https://doi.org/10.1016/j.cels.2019.10.004">10.1016/j.cels.2019.10.004</a>.
  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:<a href="https://doi.org/10.15479/AT:ISTA:6392">10.15479/AT:ISTA:6392</a>
  apa: Lukacisin, M. (2019). <i>Quantitative investigation of gene expression principles
    through combinatorial drug perturbation and theory</i>. IST Austria. <a href="https://doi.org/10.15479/AT:ISTA:6392">https://doi.org/10.15479/AT:ISTA:6392</a>
  chicago: Lukacisin, Martin. “Quantitative Investigation of Gene Expression Principles
    through Combinatorial Drug Perturbation and Theory.” IST Austria, 2019. <a href="https://doi.org/10.15479/AT:ISTA:6392">https://doi.org/10.15479/AT:ISTA:6392</a>.
  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. <i>Quantitative Investigation of Gene Expression Principles
    through Combinatorial Drug Perturbation and Theory</i>. IST Austria, 2019, doi:<a
    href="https://doi.org/10.15479/AT:ISTA:6392">10.15479/AT:ISTA:6392</a>.
  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'
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language:
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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:<a href="https://doi.org/10.15479/AT:ISTA:64">10.15479/AT:ISTA:64</a>
  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. <a href="https://doi.org/10.15479/AT:ISTA:64">https://doi.org/10.15479/AT:ISTA:64</a>
  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. <a href="https://doi.org/10.15479/AT:ISTA:64">https://doi.org/10.15479/AT:ISTA:64</a>.
  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, <a href="https://doi.org/10.15479/AT:ISTA:64">10.15479/AT:ISTA:64</a>.
  mla: Lukacisin, Martin. <i>MATLAB Analysis Code for “Sequence-Specific Thermodynamic
    Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking
    in Yeast.”</i> Institute of Science and Technology Austria, 2017, doi:<a href="https://doi.org/10.15479/AT:ISTA:64">10.15479/AT:ISTA:64</a>.
  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
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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.
    <i>PLoS One</i>. 2017;12(3). doi:<a href="https://doi.org/10.1371/journal.pone.0174066">10.1371/journal.pone.0174066</a>
  apa: Lukacisin, M., Landon, M., &#38; Jajoo, R. (2017). Sequence-specific thermodynamic
    properties of nucleic acids influence both transcriptional pausing and backtracking
    in yeast. <i>PLoS One</i>. Public Library of Science. <a href="https://doi.org/10.1371/journal.pone.0174066">https://doi.org/10.1371/journal.pone.0174066</a>
  chicago: Lukacisin, Martin, Matthieu Landon, and Rishi Jajoo. “Sequence-Specific
    Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing
    and Backtracking in Yeast.” <i>PLoS One</i>. Public Library of Science, 2017.
    <a href="https://doi.org/10.1371/journal.pone.0174066">https://doi.org/10.1371/journal.pone.0174066</a>.
  ieee: M. Lukacisin, M. Landon, and R. Jajoo, “Sequence-specific thermodynamic properties
    of nucleic acids influence both transcriptional pausing and backtracking in yeast,”
    <i>PLoS One</i>, 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.” <i>PLoS
    One</i>, vol. 12, no. 3, e0174066, Public Library of Science, 2017, doi:<a href="https://doi.org/10.1371/journal.pone.0174066">10.1371/journal.pone.0174066</a>.
  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: 2024-03-25T23:30:03Z
day: '16'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1371/journal.pone.0174066
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publication: PLoS One
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publisher: Public Library of Science
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title: Sequence-specific thermodynamic properties of nucleic acids influence both
  transcriptional pausing and backtracking in yeast
tmp:
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...
---
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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"
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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:<a href="https://doi.org/10.15479/AT:ISTA:45">10.15479/AT:ISTA:45</a>
  apa: Lukacisin, M., Landon, M., &#38; 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.
    <a href="https://doi.org/10.15479/AT:ISTA:45">https://doi.org/10.15479/AT:ISTA:45</a>
  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. <a href="https://doi.org/10.15479/AT:ISTA:45">https://doi.org/10.15479/AT:ISTA:45</a>.
  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, <a href="https://doi.org/10.15479/AT:ISTA:45">10.15479/AT:ISTA:45</a>.
  mla: Lukacisin, Martin, et al. <i>MATLAB Analysis Code for “Sequence-Specific Thermodynamic
    Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking
    in Yeast.”</i> Institute of Science and Technology Austria, 2016, doi:<a href="https://doi.org/10.15479/AT:ISTA:45">10.15479/AT:ISTA:45</a>.
  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
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  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
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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
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type: research_data
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...