---
_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:
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checksum: d68cd1596bb9fd819b750fe47c8a138a
content_type: application/pdf
creator: dernst
date_created: 2022-08-05T06:08:24Z
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file_id: '11727'
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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:
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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'
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file_size: 43740796
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content_type: application/pdf
creator: mlukacisin
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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
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title: Sequence-specific thermodynamic properties of nucleic acids influence both
transcriptional pausing and backtracking in yeast
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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: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:
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doi: 10.15479/AT:ISTA:45
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keyword:
- transcription
- pausing
- backtracking
- polymerase
- RNA
- NET-seq
- nucleosome
- basepairing
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title: MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic
Acids Influence Both Transcriptional Pausing and Backtracking in Yeast'
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