---
_id: '8645'
abstract:
- lang: eng
text: 'Epistasis, the context-dependence of the contribution of an amino acid substitution
to fitness, is common in evolution. To detect epistasis, fitness must be measured
for at least four genotypes: the reference genotype, two different single mutants
and a double mutant with both of the single mutations. For higher-order epistasis
of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional
hypercube in genotype space forming a ‘combinatorially complete dataset’. So far,
only a handful of such datasets have been produced by manual curation. Concurrently,
random mutagenesis experiments have produced measurements of fitness and other
phenotypes in a high-throughput manner, potentially containing a number of combinatorially
complete datasets. We present an effective recursive algorithm for finding all
hypercube structures in random mutagenesis experimental data. To test the algorithm,
we applied it to the data from a recent HIS3 protein dataset and found all 199
847 053 unique combinatorially complete genotype combinations of dimensionality
ranging from 2 to 12. The algorithm may be useful for researchers looking for
higher-order epistasis in their high-throughput experimental data.'
acknowledgement: 'This work was supported by the European Research Council under the
European Union’s Seventh Framework Programme (FP7/2007-2013, ERC grant agreement
335980_EinME) and Startup package to the Ivankov laboratory at Skolkovo Institute
of Science and Technology. The work was started at the School of Molecular and Theoretical
Biology 2017 supported by the Zimin Foundation. N.S.B. was supported by the Woman
Scientists Support Grant in Centre for Genomic Regulation (CRG). '
article_processing_charge: No
article_type: original
author:
- first_name: Laura A
full_name: Esteban, Laura A
last_name: Esteban
- first_name: Lyubov R
full_name: Lonishin, Lyubov R
last_name: Lonishin
- first_name: Daniil M
full_name: Bobrovskiy, Daniil M
last_name: Bobrovskiy
- first_name: Gregory
full_name: Leleytner, Gregory
last_name: Leleytner
- first_name: Natalya S
full_name: Bogatyreva, Natalya S
last_name: Bogatyreva
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: 'Dmitry N '
full_name: 'Ivankov, Dmitry N '
last_name: Ivankov
citation:
ama: 'Esteban LA, Lonishin LR, Bobrovskiy DM, et al. HypercubeME: Two hundred million
combinatorially complete datasets from a single experiment. Bioinformatics.
2020;36(6):1960-1962. doi:10.1093/bioinformatics/btz841'
apa: 'Esteban, L. A., Lonishin, L. R., Bobrovskiy, D. M., Leleytner, G., Bogatyreva,
N. S., Kondrashov, F., & Ivankov, D. N. (2020). HypercubeME: Two hundred million
combinatorially complete datasets from a single experiment. Bioinformatics.
Oxford Academic. https://doi.org/10.1093/bioinformatics/btz841'
chicago: 'Esteban, Laura A, Lyubov R Lonishin, Daniil M Bobrovskiy, Gregory Leleytner,
Natalya S Bogatyreva, Fyodor Kondrashov, and Dmitry N Ivankov. “HypercubeME:
Two Hundred Million Combinatorially Complete Datasets from a Single Experiment.”
Bioinformatics. Oxford Academic, 2020. https://doi.org/10.1093/bioinformatics/btz841.'
ieee: 'L. A. Esteban et al., “HypercubeME: Two hundred million combinatorially
complete datasets from a single experiment,” Bioinformatics, vol. 36, no.
6. Oxford Academic, pp. 1960–1962, 2020.'
ista: 'Esteban LA, Lonishin LR, Bobrovskiy DM, Leleytner G, Bogatyreva NS, Kondrashov
F, Ivankov DN. 2020. HypercubeME: Two hundred million combinatorially complete
datasets from a single experiment. Bioinformatics. 36(6), 1960–1962.'
mla: 'Esteban, Laura A., et al. “HypercubeME: Two Hundred Million Combinatorially
Complete Datasets from a Single Experiment.” Bioinformatics, vol. 36, no.
6, Oxford Academic, 2020, pp. 1960–62, doi:10.1093/bioinformatics/btz841.'
short: L.A. Esteban, L.R. Lonishin, D.M. Bobrovskiy, G. Leleytner, N.S. Bogatyreva,
F. Kondrashov, D.N. Ivankov, Bioinformatics 36 (2020) 1960–1962.
date_created: 2020-10-11T22:01:14Z
date_published: 2020-03-15T00:00:00Z
date_updated: 2023-08-22T09:57:29Z
day: '15'
ddc:
- '000'
- '570'
department:
- _id: FyKo
doi: 10.1093/bioinformatics/btz841
ec_funded: 1
external_id:
isi:
- '000538696800054'
pmid:
- '31742320'
file:
- access_level: open_access
checksum: 21d6f71839deb3b83e4a356193f72767
content_type: application/pdf
creator: dernst
date_created: 2020-10-12T12:02:09Z
date_updated: 2020-10-12T12:02:09Z
file_id: '8649'
file_name: 2020_Bioinformatics_Esteban.pdf
file_size: 308341
relation: main_file
success: 1
file_date_updated: 2020-10-12T12:02:09Z
has_accepted_license: '1'
intvolume: ' 36'
isi: 1
issue: '6'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 1960-1962
pmid: 1
project:
- _id: 26120F5C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '335980'
name: Systematic investigation of epistasis in molecular evolution
publication: Bioinformatics
publication_identifier:
eissn:
- 1460-2059
issn:
- 1367-4803
publication_status: published
publisher: Oxford Academic
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'HypercubeME: Two hundred million combinatorially complete datasets from a
single experiment'
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 36
year: '2020'
...
---
_id: '279'
abstract:
- lang: eng
text: 'Background: Natural selection shapes cancer genomes. Previous studies used
signatures of positive selection to identify genes driving malignant transformation.
However, the contribution of negative selection against somatic mutations that
affect essential tumor functions or specific domains remains a controversial topic.
Results: Here, we analyze 7546 individual exomes from 26 tumor types from TCGA
data to explore the portion of the cancer exome under negative selection. Although
we find most of the genes neutrally evolving in a pan-cancer framework, we identify
essential cancer genes and immune-exposed protein regions under significant negative
selection. Moreover, our simulations suggest that the amount of negative selection
is underestimated. We therefore choose an empirical approach to identify genes,
functions, and protein regions under negative selection. We find that expression
and mutation status of negatively selected genes is indicative of patient survival.
Processes that are most strongly conserved are those that play fundamental cellular
roles such as protein synthesis, glucose metabolism, and molecular transport.
Intriguingly, we observe strong signals of selection in the immunopeptidome and
proteins controlling peptide exposition, highlighting the importance of immune
surveillance evasion. Additionally, tumor type-specific immune activity correlates
with the strength of negative selection on human epitopes. Conclusions: In summary,
our results show that negative selection is a hallmark of cell essentiality and
immune response in cancer. The functional domains identified could be exploited
therapeutically, ultimately allowing for the development of novel cancer treatments.'
article_number: '67'
article_processing_charge: No
author:
- first_name: Luis
full_name: Zapata, Luis
last_name: Zapata
- first_name: Oriol
full_name: Pich, Oriol
last_name: Pich
- first_name: Luis
full_name: Serrano, Luis
last_name: Serrano
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Stephan
full_name: Ossowski, Stephan
last_name: Ossowski
- first_name: Martin
full_name: Schaefer, Martin
last_name: Schaefer
citation:
ama: Zapata L, Pich O, Serrano L, Kondrashov F, Ossowski S, Schaefer M. Negative
selection in tumor genome evolution acts on essential cellular functions and the
immunopeptidome. Genome Biology. 2018;19. doi:10.1186/s13059-018-1434-0
apa: Zapata, L., Pich, O., Serrano, L., Kondrashov, F., Ossowski, S., & Schaefer,
M. (2018). Negative selection in tumor genome evolution acts on essential cellular
functions and the immunopeptidome. Genome Biology. BioMed Central. https://doi.org/10.1186/s13059-018-1434-0
chicago: Zapata, Luis, Oriol Pich, Luis Serrano, Fyodor Kondrashov, Stephan Ossowski,
and Martin Schaefer. “Negative Selection in Tumor Genome Evolution Acts on Essential
Cellular Functions and the Immunopeptidome.” Genome Biology. BioMed Central,
2018. https://doi.org/10.1186/s13059-018-1434-0.
ieee: L. Zapata, O. Pich, L. Serrano, F. Kondrashov, S. Ossowski, and M. Schaefer,
“Negative selection in tumor genome evolution acts on essential cellular functions
and the immunopeptidome,” Genome Biology, vol. 19. BioMed Central, 2018.
ista: Zapata L, Pich O, Serrano L, Kondrashov F, Ossowski S, Schaefer M. 2018. Negative
selection in tumor genome evolution acts on essential cellular functions and the
immunopeptidome. Genome Biology. 19, 67.
mla: Zapata, Luis, et al. “Negative Selection in Tumor Genome Evolution Acts on
Essential Cellular Functions and the Immunopeptidome.” Genome Biology,
vol. 19, 67, BioMed Central, 2018, doi:10.1186/s13059-018-1434-0.
short: L. Zapata, O. Pich, L. Serrano, F. Kondrashov, S. Ossowski, M. Schaefer,
Genome Biology 19 (2018).
date_created: 2018-12-11T11:45:35Z
date_published: 2018-05-31T00:00:00Z
date_updated: 2023-09-13T09:01:32Z
day: '31'
ddc:
- '570'
department:
- _id: FyKo
doi: 10.1186/s13059-018-1434-0
ec_funded: 1
external_id:
isi:
- '000433986200001'
file:
- access_level: open_access
checksum: f3e4922486bd9bf1483271bdbed394a7
content_type: application/pdf
creator: dernst
date_created: 2018-12-17T14:05:01Z
date_updated: 2020-07-14T12:45:47Z
file_id: '5708'
file_name: 2018_GenomeBiology_Zapata.pdf
file_size: 1414722
relation: main_file
file_date_updated: 2020-07-14T12:45:47Z
has_accepted_license: '1'
intvolume: ' 19'
isi: 1
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 26120F5C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '335980'
name: Systematic investigation of epistasis in molecular evolution
publication: Genome Biology
publication_status: published
publisher: BioMed Central
publist_id: '7620'
quality_controlled: '1'
related_material:
record:
- id: '9811'
relation: research_data
status: public
- id: '9812'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Negative selection in tumor genome evolution acts on essential cellular functions
and the immunopeptidome
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: 19
year: '2018'
...
---
_id: '5995'
abstract:
- lang: eng
text: "Motivation\r\nComputational prediction of the effect of mutations on protein
stability is used by researchers in many fields. The utility of the prediction
methods is affected by their accuracy and bias. Bias, a systematic shift of the
predicted change of stability, has been noted as an issue for several methods,
but has not been investigated systematically. Presence of the bias may lead to
misleading results especially when exploring the effects of combination of different
mutations.\r\n\r\nResults\r\nHere we use a protocol to measure the bias as a function
of the number of introduced mutations. It is based on a self-consistency test
of the reciprocity the effect of a mutation. An advantage of the used approach
is that it relies solely on crystal structures without experimentally measured
stability values. We applied the protocol to four popular algorithms predicting
change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant,
and found an inherent bias. For one program, FoldX, we manage to substantially
reduce the bias using additional relaxation by Modeller. Authors using algorithms
for predicting effects of mutations should be aware of the bias described here."
article_processing_charge: No
author:
- first_name: Dinara R
full_name: Usmanova, Dinara R
last_name: Usmanova
- first_name: Natalya S
full_name: Bogatyreva, Natalya S
last_name: Bogatyreva
- first_name: Joan
full_name: Ariño Bernad, Joan
last_name: Ariño Bernad
- first_name: Aleksandra A
full_name: Eremina, Aleksandra A
last_name: Eremina
- first_name: Anastasiya A
full_name: Gorshkova, Anastasiya A
last_name: Gorshkova
- first_name: German M
full_name: Kanevskiy, German M
last_name: Kanevskiy
- first_name: Lyubov R
full_name: Lonishin, Lyubov R
last_name: Lonishin
- first_name: Alexander V
full_name: Meister, Alexander V
last_name: Meister
- first_name: Alisa G
full_name: Yakupova, Alisa G
last_name: Yakupova
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Dmitry
full_name: Ivankov, Dmitry
id: 49FF1036-F248-11E8-B48F-1D18A9856A87
last_name: Ivankov
citation:
ama: Usmanova DR, Bogatyreva NS, Ariño Bernad J, et al. Self-consistency test reveals
systematic bias in programs for prediction change of stability upon mutation.
Bioinformatics. 2018;34(21):3653-3658. doi:10.1093/bioinformatics/bty340
apa: Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova,
A. A., Kanevskiy, G. M., … Ivankov, D. (2018). Self-consistency test reveals systematic
bias in programs for prediction change of stability upon mutation. Bioinformatics.
Oxford University Press . https://doi.org/10.1093/bioinformatics/bty340
chicago: Usmanova, Dinara R, Natalya S Bogatyreva, Joan Ariño Bernad, Aleksandra
A Eremina, Anastasiya A Gorshkova, German M Kanevskiy, Lyubov R Lonishin, et al.
“Self-Consistency Test Reveals Systematic Bias in Programs for Prediction Change
of Stability upon Mutation.” Bioinformatics. Oxford University Press ,
2018. https://doi.org/10.1093/bioinformatics/bty340.
ieee: D. R. Usmanova et al., “Self-consistency test reveals systematic bias
in programs for prediction change of stability upon mutation,” Bioinformatics,
vol. 34, no. 21. Oxford University Press , pp. 3653–3658, 2018.
ista: Usmanova DR, Bogatyreva NS, Ariño Bernad J, Eremina AA, Gorshkova AA, Kanevskiy
GM, Lonishin LR, Meister AV, Yakupova AG, Kondrashov F, Ivankov D. 2018. Self-consistency
test reveals systematic bias in programs for prediction change of stability upon
mutation. Bioinformatics. 34(21), 3653–3658.
mla: Usmanova, Dinara R., et al. “Self-Consistency Test Reveals Systematic Bias
in Programs for Prediction Change of Stability upon Mutation.” Bioinformatics,
vol. 34, no. 21, Oxford University Press , 2018, pp. 3653–58, doi:10.1093/bioinformatics/bty340.
short: D.R. Usmanova, N.S. Bogatyreva, J. Ariño Bernad, A.A. Eremina, A.A. Gorshkova,
G.M. Kanevskiy, L.R. Lonishin, A.V. Meister, A.G. Yakupova, F. Kondrashov, D.
Ivankov, Bioinformatics 34 (2018) 3653–3658.
date_created: 2019-02-14T12:48:00Z
date_published: 2018-11-01T00:00:00Z
date_updated: 2023-09-19T14:31:13Z
day: '01'
ddc:
- '570'
department:
- _id: FyKo
doi: 10.1093/bioinformatics/bty340
ec_funded: 1
external_id:
isi:
- '000450038900008'
pmid:
- '29722803'
file:
- access_level: open_access
checksum: 7e0495153f44211479674601d7f6ee03
content_type: application/pdf
creator: kschuh
date_created: 2019-02-14T13:00:55Z
date_updated: 2020-07-14T12:47:15Z
file_id: '5997'
file_name: 2018_Oxford_Usmanova.pdf
file_size: 291969
relation: main_file
file_date_updated: 2020-07-14T12:47:15Z
has_accepted_license: '1'
intvolume: ' 34'
isi: 1
issue: '21'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 3653-3658
pmid: 1
project:
- _id: 26120F5C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '335980'
name: Systematic investigation of epistasis in molecular evolution
publication: Bioinformatics
publication_identifier:
issn:
- 1367-4803
- 1460-2059
publication_status: published
publisher: 'Oxford University Press '
quality_controlled: '1'
scopus_import: '1'
status: public
title: Self-consistency test reveals systematic bias in programs for prediction change
of stability upon mutation
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 34
year: '2018'
...