---
_id: '11187'
abstract:
- lang: eng
text: During the COVID-19 pandemic, genomics and bioinformatics have emerged as
essential public health tools. The genomic data acquired using these methods have
supported the global health response, facilitated the development of testing methods
and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually
unlimited potential for rapid generation and analysis of genomic data is also
coupled with unique technical, scientific and organizational challenges. Here,
we discuss the application of genomic and computational methods for efficient
data-driven COVID-19 response, the advantages of the democratization of viral
sequencing around the world and the challenges associated with viral genome data
collection and processing.
acknowledgement: 'Our paper is dedicated to all freedom-loving people around the world,
and to the people of Ukraine who fight for our freedom. We thank William M. Switzer
and Ellsworth M. Campbell from the Division of HIV/AIDS Prevention, Centers for
Disease Control and Prevention (CDC), Atlanta, GA, USA, for discussions and suggestions.
We thank Jason Ladner from the Pathogen and Microbiome Institute, Northern Arizona
University, Flagstaff, AZ, for providing suggestions and feedback. S.M. was partially
supported by National Science Foundation grants 2041984. T.L. is supported by the
NSFC Excellent Young Scientists Fund (Hong Kong and Macau; 31922087), Research Grants
Council (RGC) Collaborative Research Fund (C7144-20GF), RGC Research Impact Fund
(R7021-20), Innovation and Technology Commission’s InnoHK funding (D24H) and Health
and Medical Research Fund (COVID190223). P.S. was supported by US National Institutes
of Health (NIH) grant 1R01EB025022 and National Science Foundation (NSF) grant 2047828.
M.A. acknowledges King Abdulaziz City for Science and Technology and the Saudi Human
Genome Project for technical and financial support (https://shgp.kacst.edu.sa) N.W.
was supported by US NIH grants R00 AI139445, DP2 AT011966 and R01 AI167910. A.S.
acknowledge funding from NSF grant no. 2029025. A.Z. has been partially supported
by NIH grants 1R01EB025022-01 and 1R21CA241044-01A1. S. Knyazev has been partly
supported by Molecular Basis of Disease at Georgia State University and NIH awards
R01 HG009120, R01 MH115676, R01 AI153827 and U01 HG011715. A.W. has been supported
by the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-061). R.K. was supported
by NSF project 2038509, RAPID: Improving QIIME 2 and UniFrac for Viruses to Respond
to COVID-19, CDC project 30055281 with Scripps led by Kristian Andersen, Genomic
sequencing of SARS-CoV-2 to investigate local and cross-border emergence and spread.
J.O.W. was supported by NIH–National Institute of Allergy and Infectious Diseases
(NIAID) R01 AI135992 and receives funding from the CDC unrelated to this work. T.I.V.
is supported by the Branco Weiss Fellowship. Y.P. was supported by the Ministry
of Science and Higher Education of the Russian Federation within the framework of
state support for the creation and development of World-Class Research Centers “Digital
biodesign and personalized healthcare” N◦075-15-2020-926. E.B. was supported by
a US National Institute of General Medical Sciences IDeA Alaska INBRE (P20GM103395)
and NIAID CEIRR (75N93019R00028). C.E.M. thanks Testing for America (501c3), OpenCovidScreen
Foundation, Igor Tulchinsky and the WorldQuant Foundation, Bill Ackman and Olivia
Flatto and the Pershing Square Foundation, Ken Griffin and Citadel, the US National
Institutes of Health (R01AI125416, R01AI151059, R21AI129851, U01DA053941), and the
Alfred P. Sloan Foundation (G-2015-13964). C.Y.C. is supported by US CDC Epidemiology
and Laboratory Capacity (ELC) for Infectious Diseases grant 6NU50CK000539 to the
California Department of Public Health, the Innovative Genomics Institute (IGI)
at the University of California, Berkeley, and University of California, San Francisco,
NIH grant R33AI12945 and US CDC contract 75D30121C10991. A.K. was partly supported
by RFBR grant 20-515-80017. P.L. acknowledges support from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation program
(grant agreement no. ~725422 - ReservoirDOCS), the Wellcome Trust through project
206298/Z/17/Z (Artic Network) and NIH grants R01 AI153044 and U19 AI135995. K.C.
acknowledges support from the US NSF award EEID-IOS-2109688. F.K.’s work was supported
by an ERC Consolidator grant to F.K. (771209–CharFL).'
article_processing_charge: No
article_type: letter_note
author:
- first_name: Sergey
full_name: Knyazev, Sergey
last_name: Knyazev
- first_name: Karishma
full_name: Chhugani, Karishma
last_name: Chhugani
- first_name: Varuni
full_name: Sarwal, Varuni
last_name: Sarwal
- first_name: Ram
full_name: Ayyala, Ram
last_name: Ayyala
- first_name: Harman
full_name: Singh, Harman
last_name: Singh
- first_name: Smruthi
full_name: Karthikeyan, Smruthi
last_name: Karthikeyan
- first_name: Dhrithi
full_name: Deshpande, Dhrithi
last_name: Deshpande
- first_name: Pelin Icer
full_name: Baykal, Pelin Icer
last_name: Baykal
- first_name: Zoia
full_name: Comarova, Zoia
last_name: Comarova
- first_name: Angela
full_name: Lu, Angela
last_name: Lu
- first_name: Yuri
full_name: Porozov, Yuri
last_name: Porozov
- first_name: Tetyana I.
full_name: Vasylyeva, Tetyana I.
last_name: Vasylyeva
- first_name: Joel O.
full_name: Wertheim, Joel O.
last_name: Wertheim
- first_name: Braden T.
full_name: Tierney, Braden T.
last_name: Tierney
- first_name: Charles Y.
full_name: Chiu, Charles Y.
last_name: Chiu
- first_name: Ren
full_name: Sun, Ren
last_name: Sun
- first_name: Aiping
full_name: Wu, Aiping
last_name: Wu
- first_name: Malak S.
full_name: Abedalthagafi, Malak S.
last_name: Abedalthagafi
- first_name: Victoria M.
full_name: Pak, Victoria M.
last_name: Pak
- first_name: Shivashankar H.
full_name: Nagaraj, Shivashankar H.
last_name: Nagaraj
- first_name: Adam L.
full_name: Smith, Adam L.
last_name: Smith
- first_name: Pavel
full_name: Skums, Pavel
last_name: Skums
- first_name: Bogdan
full_name: Pasaniuc, Bogdan
last_name: Pasaniuc
- first_name: Andrey
full_name: Komissarov, Andrey
last_name: Komissarov
- first_name: Christopher E.
full_name: Mason, Christopher E.
last_name: Mason
- first_name: Eric
full_name: Bortz, Eric
last_name: Bortz
- first_name: Philippe
full_name: Lemey, Philippe
last_name: Lemey
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Niko
full_name: Beerenwinkel, Niko
last_name: Beerenwinkel
- first_name: Tommy Tsan Yuk
full_name: Lam, Tommy Tsan Yuk
last_name: Lam
- first_name: Nicholas C.
full_name: Wu, Nicholas C.
last_name: Wu
- first_name: Alex
full_name: Zelikovsky, Alex
last_name: Zelikovsky
- first_name: Rob
full_name: Knight, Rob
last_name: Knight
- first_name: Keith A.
full_name: Crandall, Keith A.
last_name: Crandall
- first_name: Serghei
full_name: Mangul, Serghei
last_name: Mangul
citation:
ama: Knyazev S, Chhugani K, Sarwal V, et al. Unlocking capacities of genomics for
the COVID-19 response and future pandemics. Nature Methods. 2022;19(4):374-380.
doi:10.1038/s41592-022-01444-z
apa: Knyazev, S., Chhugani, K., Sarwal, V., Ayyala, R., Singh, H., Karthikeyan,
S., … Mangul, S. (2022). Unlocking capacities of genomics for the COVID-19 response
and future pandemics. Nature Methods. Springer Nature. https://doi.org/10.1038/s41592-022-01444-z
chicago: Knyazev, Sergey, Karishma Chhugani, Varuni Sarwal, Ram Ayyala, Harman Singh,
Smruthi Karthikeyan, Dhrithi Deshpande, et al. “Unlocking Capacities of Genomics
for the COVID-19 Response and Future Pandemics.” Nature Methods. Springer
Nature, 2022. https://doi.org/10.1038/s41592-022-01444-z.
ieee: S. Knyazev et al., “Unlocking capacities of genomics for the COVID-19
response and future pandemics,” Nature Methods, vol. 19, no. 4. Springer
Nature, pp. 374–380, 2022.
ista: Knyazev S, Chhugani K, Sarwal V, Ayyala R, Singh H, Karthikeyan S, Deshpande
D, Baykal PI, Comarova Z, Lu A, Porozov Y, Vasylyeva TI, Wertheim JO, Tierney
BT, Chiu CY, Sun R, Wu A, Abedalthagafi MS, Pak VM, Nagaraj SH, Smith AL, Skums
P, Pasaniuc B, Komissarov A, Mason CE, Bortz E, Lemey P, Kondrashov F, Beerenwinkel
N, Lam TTY, Wu NC, Zelikovsky A, Knight R, Crandall KA, Mangul S. 2022. Unlocking
capacities of genomics for the COVID-19 response and future pandemics. Nature
Methods. 19(4), 374–380.
mla: Knyazev, Sergey, et al. “Unlocking Capacities of Genomics for the COVID-19
Response and Future Pandemics.” Nature Methods, vol. 19, no. 4, Springer
Nature, 2022, pp. 374–80, doi:10.1038/s41592-022-01444-z.
short: S. Knyazev, K. Chhugani, V. Sarwal, R. Ayyala, H. Singh, S. Karthikeyan,
D. Deshpande, P.I. Baykal, Z. Comarova, A. Lu, Y. Porozov, T.I. Vasylyeva, J.O.
Wertheim, B.T. Tierney, C.Y. Chiu, R. Sun, A. Wu, M.S. Abedalthagafi, V.M. Pak,
S.H. Nagaraj, A.L. Smith, P. Skums, B. Pasaniuc, A. Komissarov, C.E. Mason, E.
Bortz, P. Lemey, F. Kondrashov, N. Beerenwinkel, T.T.Y. Lam, N.C. Wu, A. Zelikovsky,
R. Knight, K.A. Crandall, S. Mangul, Nature Methods 19 (2022) 374–380.
date_created: 2022-04-17T22:01:48Z
date_published: 2022-04-08T00:00:00Z
date_updated: 2023-08-03T06:46:09Z
day: '08'
department:
- _id: FyKo
doi: 10.1038/s41592-022-01444-z
ec_funded: 1
external_id:
isi:
- '000781199600011'
pmid:
- '35396471'
intvolume: ' 19'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41592-022-01444-z
month: '04'
oa: 1
oa_version: Published Version
page: 374-380
pmid: 1
project:
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
publication: Nature Methods
publication_identifier:
eissn:
- 1548-7105
issn:
- 1548-7091
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unlocking capacities of genomics for the COVID-19 response and future pandemics
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 19
year: '2022'
...
---
_id: '11447'
abstract:
- lang: eng
text: Empirical essays of fitness landscapes suggest that they may be rugged, that
is having multiple fitness peaks. Such fitness landscapes, those that have multiple
peaks, necessarily have special local structures, called reciprocal sign epistasis
(Poelwijk et al. in J Theor Biol 272:141–144, 2011). Here, we investigate the
quantitative relationship between the number of fitness peaks and the number of
reciprocal sign epistatic interactions. Previously, it has been shown (Poelwijk
et al. in J Theor Biol 272:141–144, 2011) that pairwise reciprocal sign epistasis
is a necessary but not sufficient condition for the existence of multiple peaks.
Applying discrete Morse theory, which to our knowledge has never been used in
this context, we extend this result by giving the minimal number of reciprocal
sign epistatic interactions required to create a given number of peaks.
acknowledgement: We are grateful to Herbert Edelsbrunner and Jeferson Zapata for helpful
discussions. Open access funding provided by Austrian Science Fund (FWF). Partially
supported by the ERC Consolidator (771209–CharFL) and the FWF Austrian Science Fund
(I5127-B) grants to FAK.
article_number: '74'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Raimundo J
full_name: Saona Urmeneta, Raimundo J
id: BD1DF4C4-D767-11E9-B658-BC13E6697425
last_name: Saona Urmeneta
orcid: 0000-0001-5103-038X
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Kseniia
full_name: Khudiakova, Kseniia
id: 4E6DC800-AE37-11E9-AC72-31CAE5697425
last_name: Khudiakova
orcid: 0000-0002-6246-1465
citation:
ama: Saona Urmeneta RJ, Kondrashov F, Khudiakova K. Relation between the number
of peaks and the number of reciprocal sign epistatic interactions. Bulletin
of Mathematical Biology. 2022;84(8). doi:10.1007/s11538-022-01029-z
apa: Saona Urmeneta, R. J., Kondrashov, F., & Khudiakova, K. (2022). Relation
between the number of peaks and the number of reciprocal sign epistatic interactions.
Bulletin of Mathematical Biology. Springer Nature. https://doi.org/10.1007/s11538-022-01029-z
chicago: Saona Urmeneta, Raimundo J, Fyodor Kondrashov, and Kseniia Khudiakova.
“Relation between the Number of Peaks and the Number of Reciprocal Sign Epistatic
Interactions.” Bulletin of Mathematical Biology. Springer Nature, 2022.
https://doi.org/10.1007/s11538-022-01029-z.
ieee: R. J. Saona Urmeneta, F. Kondrashov, and K. Khudiakova, “Relation between
the number of peaks and the number of reciprocal sign epistatic interactions,”
Bulletin of Mathematical Biology, vol. 84, no. 8. Springer Nature, 2022.
ista: Saona Urmeneta RJ, Kondrashov F, Khudiakova K. 2022. Relation between the
number of peaks and the number of reciprocal sign epistatic interactions. Bulletin
of Mathematical Biology. 84(8), 74.
mla: Saona Urmeneta, Raimundo J., et al. “Relation between the Number of Peaks and
the Number of Reciprocal Sign Epistatic Interactions.” Bulletin of Mathematical
Biology, vol. 84, no. 8, 74, Springer Nature, 2022, doi:10.1007/s11538-022-01029-z.
short: R.J. Saona Urmeneta, F. Kondrashov, K. Khudiakova, Bulletin of Mathematical
Biology 84 (2022).
date_created: 2022-06-17T16:16:15Z
date_published: 2022-06-17T00:00:00Z
date_updated: 2023-08-03T07:20:53Z
day: '17'
ddc:
- '510'
- '570'
department:
- _id: GradSch
- _id: NiBa
- _id: JaMa
doi: 10.1007/s11538-022-01029-z
ec_funded: 1
external_id:
isi:
- '000812509800001'
file:
- access_level: open_access
checksum: 05a1fe7d10914a00c2bca9b447993a65
content_type: application/pdf
creator: dernst
date_created: 2022-06-20T07:51:32Z
date_updated: 2022-06-20T07:51:32Z
file_id: '11455'
file_name: 2022_BulletinMathBiology_Saona.pdf
file_size: 463025
relation: main_file
success: 1
file_date_updated: 2022-06-20T07:51:32Z
has_accepted_license: '1'
intvolume: ' 84'
isi: 1
issue: '8'
keyword:
- Computational Theory and Mathematics
- General Agricultural and Biological Sciences
- Pharmacology
- General Environmental Science
- General Biochemistry
- Genetics and Molecular Biology
- General Mathematics
- Immunology
- General Neuroscience
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
- _id: c098eddd-5a5b-11eb-8a69-abe27170a68f
grant_number: I05127
name: Evolutionary analysis of gene regulation
publication: Bulletin of Mathematical Biology
publication_identifier:
eissn:
- 1522-9602
issn:
- 0092-8240
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1007/s11538-022-01118-z
scopus_import: '1'
status: public
title: Relation between the number of peaks and the number of reciprocal sign epistatic
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: 84
year: '2022'
...
---
_id: '11448'
abstract:
- lang: eng
text: Studies of protein fitness landscapes reveal biophysical constraints guiding
protein evolution and empower prediction of functional proteins. However, generalisation
of these findings is limited due to scarceness of systematic data on fitness landscapes
of proteins with a defined evolutionary relationship. We characterized the fitness
peaks of four orthologous fluorescent proteins with a broad range of sequence
divergence. While two of the four studied fitness peaks were sharp, the other
two were considerably flatter, being almost entirely free of epistatic interactions.
Mutationally robust proteins, characterized by a flat fitness peak, were not optimal
templates for machine-learning-driven protein design – instead, predictions were
more accurate for fragile proteins with epistatic landscapes. Our work paves insights
for practical application of fitness landscape heterogeneity in protein engineering.
acknowledged_ssus:
- _id: LifeSc
- _id: Bio
acknowledgement: "We thank Ondřej Draganov, Rodrigo Redondo, Bor Kavčič, Mia Juračić
and Andrea Pauli for discussion and technical advice. We thank Anita Testa Salmazo
for advice on resin protein purification, Dmitry Bolotin and the Milaboratory (milaboratory.com)
for access to computing and storage infrastructure, and Josef Houser and Eva Fujdiarova
for technical assistance and data interpretation. Core facility Biomolecular Interactions
and Crystallization of CEITEC Masaryk University is gratefully acknowledged for
the obtaining of the scientific data presented in this paper. This research was
supported by the Scientific Service Units (SSU) of IST-Austria\r\nthrough resources
provided by the Bioimaging Facility (BIF), and the Life Science Facility (LSF).
MiSeq and HiSeq NGS sequencing was performed by the Next Generation Sequencing Facility
at Vienna BioCenter Core Facilities (VBCF), member of the Vienna BioCenter (VBC),
Austria. FACS was performed at the BioOptics Facility of the Institute of Molecular
Pathology (IMP), Austria. We also thank the Biomolecular Crystallography Facility
in the Vanderbilt University Center for Structural Biology. We are grateful to Joel
M Harp for help with X-ray data collection. This work was supported by the ERC Consolidator
grant to FAK (771209—CharFL). KSS acknowledges support by President’s Grant МК–5405.2021.1.4,
the Imperial College Research Fellowship and the MRC London Institute of Medical
Sciences (UKRI MC-A658-5QEA0).\r\nAF is supported by the Marie Skłodowska-Curie
Fellowship (H2020-MSCA-IF-2019, Grant Agreement No. 898203, Project acronym \"FLINDIP\").
Experiments were partially carried out using equipment provided by the Institute
of Bioorganic Chemistry of the Russian Academy of Sciences Сore Facility (CKP IBCH).
This work was supported by a Russian Science Foundation grant 19-74-10102.This project
has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 665,385."
article_number: '75842'
article_processing_charge: No
article_type: original
author:
- first_name: Louisa
full_name: Gonzalez Somermeyer, Louisa
id: 4720D23C-F248-11E8-B48F-1D18A9856A87
last_name: Gonzalez Somermeyer
orcid: 0000-0001-9139-5383
- first_name: Aubin
full_name: Fleiss, Aubin
last_name: Fleiss
- first_name: Alexander S
full_name: Mishin, Alexander S
last_name: Mishin
- first_name: Nina G
full_name: Bozhanova, Nina G
last_name: Bozhanova
- first_name: Anna A
full_name: Igolkina, Anna A
last_name: Igolkina
- first_name: Jens
full_name: Meiler, Jens
last_name: Meiler
- first_name: Maria-Elisenda
full_name: Alaball Pujol, Maria-Elisenda
last_name: Alaball Pujol
- first_name: Ekaterina V
full_name: Putintseva, Ekaterina V
last_name: Putintseva
- first_name: Karen S
full_name: Sarkisyan, Karen S
last_name: Sarkisyan
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
citation:
ama: Gonzalez Somermeyer L, Fleiss A, Mishin AS, et al. Heterogeneity of the GFP
fitness landscape and data-driven protein design. eLife. 2022;11. doi:10.7554/elife.75842
apa: Gonzalez Somermeyer, L., Fleiss, A., Mishin, A. S., Bozhanova, N. G., Igolkina,
A. A., Meiler, J., … Kondrashov, F. (2022). Heterogeneity of the GFP fitness landscape
and data-driven protein design. ELife. eLife Sciences Publications. https://doi.org/10.7554/elife.75842
chicago: Gonzalez Somermeyer, Louisa, Aubin Fleiss, Alexander S Mishin, Nina G Bozhanova,
Anna A Igolkina, Jens Meiler, Maria-Elisenda Alaball Pujol, Ekaterina V Putintseva,
Karen S Sarkisyan, and Fyodor Kondrashov. “Heterogeneity of the GFP Fitness Landscape
and Data-Driven Protein Design.” ELife. eLife Sciences Publications, 2022.
https://doi.org/10.7554/elife.75842.
ieee: L. Gonzalez Somermeyer et al., “Heterogeneity of the GFP fitness landscape
and data-driven protein design,” eLife, vol. 11. eLife Sciences Publications,
2022.
ista: Gonzalez Somermeyer L, Fleiss A, Mishin AS, Bozhanova NG, Igolkina AA, Meiler
J, Alaball Pujol M-E, Putintseva EV, Sarkisyan KS, Kondrashov F. 2022. Heterogeneity
of the GFP fitness landscape and data-driven protein design. eLife. 11, 75842.
mla: Gonzalez Somermeyer, Louisa, et al. “Heterogeneity of the GFP Fitness Landscape
and Data-Driven Protein Design.” ELife, vol. 11, 75842, eLife Sciences
Publications, 2022, doi:10.7554/elife.75842.
short: L. Gonzalez Somermeyer, A. Fleiss, A.S. Mishin, N.G. Bozhanova, A.A. Igolkina,
J. Meiler, M.-E. Alaball Pujol, E.V. Putintseva, K.S. Sarkisyan, F. Kondrashov,
ELife 11 (2022).
date_created: 2022-06-18T09:06:59Z
date_published: 2022-05-05T00:00:00Z
date_updated: 2023-08-03T07:20:15Z
day: '05'
ddc:
- '570'
department:
- _id: GradSch
- _id: FyKo
doi: 10.7554/elife.75842
ec_funded: 1
external_id:
isi:
- '000799197200001'
file:
- access_level: open_access
checksum: 7573c28f44028ab0cc81faef30039e44
content_type: application/pdf
creator: dernst
date_created: 2022-06-20T07:44:19Z
date_updated: 2022-06-20T07:44:19Z
file_id: '11454'
file_name: 2022_eLife_Somermeyer.pdf
file_size: 5297213
relation: main_file
success: 1
file_date_updated: 2022-06-20T07:44:19Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
keyword:
- General Immunology and Microbiology
- General Biochemistry
- Genetics and Molecular Biology
- General Medicine
- General Neuroscience
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: eLife
publication_identifier:
issn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Heterogeneity of the GFP fitness landscape and data-driven protein design
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: 11
year: '2022'
...
---
_id: '9905'
abstract:
- lang: eng
text: Vaccines are thought to be the best available solution for controlling the
ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains
may come too rapidly for current vaccine developments to alleviate the health,
economic and social consequences of the pandemic. To quantify and characterize
the risk of such a scenario, we created a SIR-derived model with initial stochastic
dynamics of the vaccine-resistant strain to study the probability of its emergence
and establishment. Using parameters realistically resembling SARS-CoV-2 transmission,
we model a wave-like pattern of the pandemic and consider the impact of the rate
of vaccination and the strength of non-pharmaceutical intervention measures on
the probability of emergence of a resistant strain. As expected, we found that
a fast rate of vaccination decreases the probability of emergence of a resistant
strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions
happened at a time when most individuals of the population have already been vaccinated
the probability of emergence of a resistant strain was greatly increased. Consequently,
we show that a period of transmission reduction close to the end of the vaccination
campaign can substantially reduce the probability of resistant strain establishment.
Our results suggest that policymakers and individuals should consider maintaining
non-pharmaceutical interventions and transmission-reducing behaviours throughout
the entire vaccination period.
acknowledgement: We thank Alexey Kondrashov, Nick Machnik, Raimundo Julian Saona Urmeneta,
Gasper Tkacik and Nick Barton for fruitful discussions. We also thank participants
of EvoLunch seminar at IST Austria and the internal seminar at the Banco de España
for useful comments. The opinions expressed in this document are exclusively of
the authors and, therefore, do not necessarily coincide with those of the Banco
de España or the Eurosystem. ETD is supported by the Swiss National Science and
Louis Jeantet Foundation. The work of FAK was in part supported by the ERC Consolidator
Grant (771209-CharFL).
article_number: '15729'
article_processing_charge: Yes
article_type: original
author:
- first_name: Simon
full_name: Rella, Simon
id: B4765ACA-AA38-11E9-AC9A-0930E6697425
last_name: Rella
- first_name: Yuliya A.
full_name: Kulikova, Yuliya A.
last_name: Kulikova
- first_name: Emmanouil T.
full_name: Dermitzakis, Emmanouil T.
last_name: Dermitzakis
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
citation:
ama: Rella S, Kulikova YA, Dermitzakis ET, Kondrashov F. Rates of SARS-CoV-2 transmission
and vaccination impact the fate of vaccine-resistant strains. Scientific Reports.
2021;11(1). doi:10.1038/s41598-021-95025-3
apa: Rella, S., Kulikova, Y. A., Dermitzakis, E. T., & Kondrashov, F. (2021).
Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant
strains. Scientific Reports. Springer Nature. https://doi.org/10.1038/s41598-021-95025-3
chicago: Rella, Simon, Yuliya A. Kulikova, Emmanouil T. Dermitzakis, and Fyodor
Kondrashov. “Rates of SARS-CoV-2 Transmission and Vaccination Impact the Fate
of Vaccine-Resistant Strains.” Scientific Reports. Springer Nature, 2021.
https://doi.org/10.1038/s41598-021-95025-3.
ieee: S. Rella, Y. A. Kulikova, E. T. Dermitzakis, and F. Kondrashov, “Rates of
SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains,”
Scientific Reports, vol. 11, no. 1. Springer Nature, 2021.
ista: Rella S, Kulikova YA, Dermitzakis ET, Kondrashov F. 2021. Rates of SARS-CoV-2
transmission and vaccination impact the fate of vaccine-resistant strains. Scientific
Reports. 11(1), 15729.
mla: Rella, Simon, et al. “Rates of SARS-CoV-2 Transmission and Vaccination Impact
the Fate of Vaccine-Resistant Strains.” Scientific Reports, vol. 11, no.
1, 15729, Springer Nature, 2021, doi:10.1038/s41598-021-95025-3.
short: S. Rella, Y.A. Kulikova, E.T. Dermitzakis, F. Kondrashov, Scientific Reports
11 (2021).
date_created: 2021-08-15T22:01:26Z
date_published: 2021-07-30T00:00:00Z
date_updated: 2023-08-11T10:42:58Z
day: '30'
ddc:
- '570'
- '610'
department:
- _id: FyKo
doi: 10.1038/s41598-021-95025-3
ec_funded: 1
external_id:
isi:
- '000683329100001'
pmid:
- '34330988'
file:
- access_level: open_access
checksum: ac86892ed17e6724c7251844da5cef5c
content_type: application/pdf
creator: asandaue
date_created: 2021-08-16T11:36:49Z
date_updated: 2021-08-16T11:36:49Z
file_id: '9927'
file_name: 2021_ScientificReports_Rella.pdf
file_size: 3432001
relation: main_file
success: 1
file_date_updated: 2021-08-16T11:36:49Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
issue: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
publication: Scientific Reports
publication_identifier:
eissn:
- '20452322'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- description: News on IST Website
relation: press_release
url: https://ist.ac.at/en/news/counterintuitive-dynamics-threaten-the-end-of-the-pandemic/
scopus_import: '1'
status: public
title: Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant
strains
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: 11
year: '2021'
...
---
_id: '9910'
abstract:
- lang: eng
text: Adult height inspired the first biometrical and quantitative genetic studies
and is a test-case trait for understanding heritability. The studies of height
led to formulation of the classical polygenic model, that has a profound influence
on the way we view and analyse complex traits. An essential part of the classical
model is an assumption of additivity of effects and normality of the distribution
of the residuals. However, it may be expected that the normal approximation will
become insufficient in bigger studies. Here, we demonstrate that when the height
of hundreds of thousands of individuals is analysed, the model complexity needs
to be increased to include non-additive interactions between sex, environment
and genes. Alternatively, the use of log-normal approximation allowed us to still
use the additive effects model. These findings are important for future genetic
and methodologic studies that make use of adult height as an exemplar trait.
acknowledgement: "We are grateful to Marianna Bevova and Pavel Borodin for fruitful
discussion and help with conceptualising our findings and to Lennart C. Karssen
for help with handling the UK Biobank data.\r\n\r\nFunding\r\nThis research has
been conducted using the UK Biobank Resource (project # 41601, “Non-additive effects
in control of complex human traits”). The work of SAS, IAK, and TIS were supported
by Russian Ministry of Science and Education under the 5–100 Excellence Programme.
The work of YSA and TIA was supported by the Ministry of Education and Science of
the RF via the Institute of Cytology and Genetics SB RAS (project number 0324-2019-0040-C-01/AAAA-A17-117092070032-4).
FAK is supported by the ERC Consolidator Grant (ChrFL: 771209)."
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Sergei A.
full_name: Slavskii, Sergei A.
last_name: Slavskii
- first_name: Ivan A.
full_name: Kuznetsov, Ivan A.
last_name: Kuznetsov
- first_name: Tatiana I.
full_name: Shashkova, Tatiana I.
last_name: Shashkova
- first_name: Georgii A.
full_name: Bazykin, Georgii A.
last_name: Bazykin
- first_name: Tatiana I.
full_name: Axenovich, Tatiana I.
last_name: Axenovich
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Yurii S.
full_name: Aulchenko, Yurii S.
last_name: Aulchenko
citation:
ama: Slavskii SA, Kuznetsov IA, Shashkova TI, et al. The limits of normal approximation
for adult height. European Journal of Human Genetics. 2021;29(7):1082-1091.
doi:10.1038/s41431-021-00836-7
apa: Slavskii, S. A., Kuznetsov, I. A., Shashkova, T. I., Bazykin, G. A., Axenovich,
T. I., Kondrashov, F., & Aulchenko, Y. S. (2021). The limits of normal approximation
for adult height. European Journal of Human Genetics. Springer Nature.
https://doi.org/10.1038/s41431-021-00836-7
chicago: Slavskii, Sergei A., Ivan A. Kuznetsov, Tatiana I. Shashkova, Georgii A.
Bazykin, Tatiana I. Axenovich, Fyodor Kondrashov, and Yurii S. Aulchenko. “The
Limits of Normal Approximation for Adult Height.” European Journal of Human
Genetics. Springer Nature, 2021. https://doi.org/10.1038/s41431-021-00836-7.
ieee: S. A. Slavskii et al., “The limits of normal approximation for adult
height,” European Journal of Human Genetics, vol. 29, no. 7. Springer Nature,
pp. 1082–1091, 2021.
ista: Slavskii SA, Kuznetsov IA, Shashkova TI, Bazykin GA, Axenovich TI, Kondrashov
F, Aulchenko YS. 2021. The limits of normal approximation for adult height. European
Journal of Human Genetics. 29(7), 1082–1091.
mla: Slavskii, Sergei A., et al. “The Limits of Normal Approximation for Adult Height.”
European Journal of Human Genetics, vol. 29, no. 7, Springer Nature, 2021,
pp. 1082–91, doi:10.1038/s41431-021-00836-7.
short: S.A. Slavskii, I.A. Kuznetsov, T.I. Shashkova, G.A. Bazykin, T.I. Axenovich,
F. Kondrashov, Y.S. Aulchenko, European Journal of Human Genetics 29 (2021) 1082–1091.
date_created: 2021-08-15T22:01:28Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2023-08-11T10:33:42Z
day: '01'
ddc:
- '576'
department:
- _id: FyKo
doi: 10.1038/s41431-021-00836-7
ec_funded: 1
external_id:
isi:
- '000625853200001'
pmid:
- '33664501'
file:
- access_level: open_access
checksum: a676d76f91b0dbe0504c63e469129c2a
content_type: application/pdf
creator: asandaue
date_created: 2021-08-16T09:14:36Z
date_updated: 2021-08-16T09:14:36Z
file_id: '9921'
file_name: 2021_EuropeanJournalOfHumanGenetics_Slavskii.pdf
file_size: 1079395
relation: main_file
success: 1
file_date_updated: 2021-08-16T09:14:36Z
has_accepted_license: '1'
intvolume: ' 29'
isi: 1
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 1082-1091
pmid: 1
project:
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
publication: European Journal of Human Genetics
publication_identifier:
eissn:
- '14765438'
issn:
- '10184813'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: The limits of normal approximation for adult height
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: 29
year: '2021'
...
---
_id: '7889'
abstract:
- lang: eng
text: Autoluminescent plants engineered to express a bacterial bioluminescence gene
cluster in plastids have not been widely adopted because of low light output.
We engineered tobacco plants with a fungal bioluminescence system that converts
caffeic acid (present in all plants) into luciferin and report self-sustained
luminescence that is visible to the naked eye. Our findings could underpin development
of a suite of imaging tools for plants.
acknowledgement: "This study was designed, performed and funded by Planta LLC. We
thank K. Wood for assisting in manuscript development. Planta acknowledges support
from the Skolkovo Innovation Centre. We thank D. Bolotin and the Milaboratory (milaboratory.com)
for access to computing and storage infrastructure. We thank S. Shakhov for providing\r\nphotography
equipment. The Synthetic Biology Group is funded by the MRC London Institute of
Medical Sciences (UKRI MC-A658-5QEA0, K.S.S.). K.S.S. is supported by an Imperial
College Research Fellowship. Experiments were partially carried out using equipment
provided by the Institute of Bioorganic Chemistry of the Russian Academy\r\nof Sciences
Сore Facility (CKP IBCH; supported by the Russian Ministry of Education and Science
Grant RFMEFI62117X0018). The F.A.K. lab is supported by ERC grant agreement 771209—CharFL.
This project received funding from the European Union’s Horizon 2020 Research and
Innovation Programme under Marie Skłodowska-Curie\r\nGrant Agreement 665385. K.S.S.
acknowledges support by President’s Grant 075-15-2019-411. Design and assembly of
some of the plasmids was supported by Russian Science Foundation grant 19-74-10102.
Imaging experiments were partially supported by Russian Science Foundation grant
17-14-01169p. LC-MS/MS analyses of extracts were\r\nsupported by Russian Science
Foundation grant 16-14-00052p. Design and assembly of plasmids was partially supported
by grant 075-15-2019-1789 from the Ministry of Science and Higher Education of the
Russian Federation allocated to the Center for Precision Genome Editing and Genetic
Technologies for Biomedicine. The authors\r\nwould like to acknowledge the work
of Genomics Core Facility of the Skolkovo Institute of Science and Technology, which
performed the sequencing and bioinformatic analysis."
article_processing_charge: No
article_type: original
author:
- first_name: Tatiana
full_name: Mitiouchkina, Tatiana
last_name: Mitiouchkina
- first_name: Alexander S.
full_name: Mishin, Alexander S.
last_name: Mishin
- first_name: Louisa
full_name: Gonzalez Somermeyer, Louisa
id: 4720D23C-F248-11E8-B48F-1D18A9856A87
last_name: Gonzalez Somermeyer
orcid: 0000-0001-9139-5383
- first_name: Nadezhda M.
full_name: Markina, Nadezhda M.
last_name: Markina
- first_name: Tatiana V.
full_name: Chepurnyh, Tatiana V.
last_name: Chepurnyh
- first_name: Elena B.
full_name: Guglya, Elena B.
last_name: Guglya
- first_name: Tatiana A.
full_name: Karataeva, Tatiana A.
last_name: Karataeva
- first_name: Kseniia A.
full_name: Palkina, Kseniia A.
last_name: Palkina
- first_name: Ekaterina S.
full_name: Shakhova, Ekaterina S.
last_name: Shakhova
- first_name: Liliia I.
full_name: Fakhranurova, Liliia I.
last_name: Fakhranurova
- first_name: Sofia V.
full_name: Chekova, Sofia V.
last_name: Chekova
- first_name: Aleksandra S.
full_name: Tsarkova, Aleksandra S.
last_name: Tsarkova
- first_name: Yaroslav V.
full_name: Golubev, Yaroslav V.
last_name: Golubev
- first_name: Vadim V.
full_name: Negrebetsky, Vadim V.
last_name: Negrebetsky
- first_name: Sergey A.
full_name: Dolgushin, Sergey A.
last_name: Dolgushin
- first_name: Pavel V.
full_name: Shalaev, Pavel V.
last_name: Shalaev
- first_name: Dmitry
full_name: Shlykov, Dmitry
last_name: Shlykov
- first_name: Olesya A.
full_name: Melnik, Olesya A.
last_name: Melnik
- first_name: Victoria O.
full_name: Shipunova, Victoria O.
last_name: Shipunova
- first_name: Sergey M.
full_name: Deyev, Sergey M.
last_name: Deyev
- first_name: Andrey I.
full_name: Bubyrev, Andrey I.
last_name: Bubyrev
- first_name: Alexander S.
full_name: Pushin, Alexander S.
last_name: Pushin
- first_name: Vladimir V.
full_name: Choob, Vladimir V.
last_name: Choob
- first_name: Sergey V.
full_name: Dolgov, Sergey V.
last_name: Dolgov
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Ilia V.
full_name: Yampolsky, Ilia V.
last_name: Yampolsky
- first_name: Karen S.
full_name: Sarkisyan, Karen S.
last_name: Sarkisyan
citation:
ama: Mitiouchkina T, Mishin AS, Gonzalez Somermeyer L, et al. Plants with genetically
encoded autoluminescence. Nature Biotechnology. 2020;38:944-946. doi:10.1038/s41587-020-0500-9
apa: Mitiouchkina, T., Mishin, A. S., Gonzalez Somermeyer, L., Markina, N. M., Chepurnyh,
T. V., Guglya, E. B., … Sarkisyan, K. S. (2020). Plants with genetically encoded
autoluminescence. Nature Biotechnology. Springer Nature. https://doi.org/10.1038/s41587-020-0500-9
chicago: Mitiouchkina, Tatiana, Alexander S. Mishin, Louisa Gonzalez Somermeyer,
Nadezhda M. Markina, Tatiana V. Chepurnyh, Elena B. Guglya, Tatiana A. Karataeva,
et al. “Plants with Genetically Encoded Autoluminescence.” Nature Biotechnology.
Springer Nature, 2020. https://doi.org/10.1038/s41587-020-0500-9.
ieee: T. Mitiouchkina et al., “Plants with genetically encoded autoluminescence,”
Nature Biotechnology, vol. 38. Springer Nature, pp. 944–946, 2020.
ista: Mitiouchkina T, Mishin AS, Gonzalez Somermeyer L, Markina NM, Chepurnyh TV,
Guglya EB, Karataeva TA, Palkina KA, Shakhova ES, Fakhranurova LI, Chekova SV,
Tsarkova AS, Golubev YV, Negrebetsky VV, Dolgushin SA, Shalaev PV, Shlykov D,
Melnik OA, Shipunova VO, Deyev SM, Bubyrev AI, Pushin AS, Choob VV, Dolgov SV,
Kondrashov F, Yampolsky IV, Sarkisyan KS. 2020. Plants with genetically encoded
autoluminescence. Nature Biotechnology. 38, 944–946.
mla: Mitiouchkina, Tatiana, et al. “Plants with Genetically Encoded Autoluminescence.”
Nature Biotechnology, vol. 38, Springer Nature, 2020, pp. 944–46, doi:10.1038/s41587-020-0500-9.
short: T. Mitiouchkina, A.S. Mishin, L. Gonzalez Somermeyer, N.M. Markina, T.V.
Chepurnyh, E.B. Guglya, T.A. Karataeva, K.A. Palkina, E.S. Shakhova, L.I. Fakhranurova,
S.V. Chekova, A.S. Tsarkova, Y.V. Golubev, V.V. Negrebetsky, S.A. Dolgushin, P.V.
Shalaev, D. Shlykov, O.A. Melnik, V.O. Shipunova, S.M. Deyev, A.I. Bubyrev, A.S.
Pushin, V.V. Choob, S.V. Dolgov, F. Kondrashov, I.V. Yampolsky, K.S. Sarkisyan,
Nature Biotechnology 38 (2020) 944–946.
date_created: 2020-05-25T15:02:00Z
date_published: 2020-04-27T00:00:00Z
date_updated: 2023-09-05T15:30:34Z
day: '27'
ddc:
- '570'
department:
- _id: FyKo
doi: 10.1038/s41587-020-0500-9
ec_funded: 1
external_id:
isi:
- '000529298800003'
pmid:
- '32341562'
file:
- access_level: open_access
checksum: 1b30467500ec6277229a875b06e196d0
content_type: application/pdf
creator: dernst
date_created: 2020-08-28T08:57:07Z
date_updated: 2021-03-02T23:30:03Z
embargo: 2021-03-01
file_id: '8316'
file_name: 2020_NatureBiotech_Mitiouchkina.pdf
file_size: 1180086
relation: main_file
file_date_updated: 2021-03-02T23:30:03Z
has_accepted_license: '1'
intvolume: ' 38'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
page: 944-946
pmid: 1
project:
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
publication: Nature Biotechnology
publication_identifier:
eissn:
- 1546-1696
issn:
- 1087-0156
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s41587-020-0578-0
scopus_import: '1'
status: public
title: Plants with genetically encoded autoluminescence
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 38
year: '2020'
...
---
_id: '7181'
abstract:
- lang: eng
text: Multiple sequence alignments (MSAs) are used for structural1,2 and evolutionary
predictions1,2, but the complexity of aligning large datasets requires the use
of approximate solutions3, including the progressive algorithm4. Progressive MSA
methods start by aligning the most similar sequences and subsequently incorporate
the remaining sequences, from leaf-to-root, based on a guide-tree. Their accuracy
declines substantially as the number of sequences is scaled up5. We introduce
a regressive algorithm that enables MSA of up to 1.4 million sequences on a standard
workstation and substantially improves accuracy on datasets larger than 10,000
sequences. Our regressive algorithm works the other way around to the progressive
algorithm and begins by aligning the most dissimilar sequences. It uses an efficient
divide-and-conquer strategy to run third-party alignment methods in linear time,
regardless of their original complexity. Our approach will enable analyses of
extremely large genomic datasets such as the recently announced Earth BioGenome
Project, which comprises 1.5 million eukaryotic genomes6.
article_processing_charge: No
article_type: original
author:
- first_name: Edgar
full_name: Garriga, Edgar
last_name: Garriga
- first_name: Paolo
full_name: Di Tommaso, Paolo
last_name: Di Tommaso
- first_name: Cedrik
full_name: Magis, Cedrik
last_name: Magis
- first_name: Ionas
full_name: Erb, Ionas
last_name: Erb
- first_name: Leila
full_name: Mansouri, Leila
last_name: Mansouri
- first_name: Athanasios
full_name: Baltzis, Athanasios
last_name: Baltzis
- first_name: Hafid
full_name: Laayouni, Hafid
last_name: Laayouni
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
- first_name: Evan
full_name: Floden, Evan
last_name: Floden
- first_name: Cedric
full_name: Notredame, Cedric
last_name: Notredame
citation:
ama: Garriga E, Di Tommaso P, Magis C, et al. Large multiple sequence alignments
with a root-to-leaf regressive method. Nature Biotechnology. 2019;37(12):1466-1470.
doi:10.1038/s41587-019-0333-6
apa: Garriga, E., Di Tommaso, P., Magis, C., Erb, I., Mansouri, L., Baltzis, A.,
… Notredame, C. (2019). Large multiple sequence alignments with a root-to-leaf
regressive method. Nature Biotechnology. Springer Nature. https://doi.org/10.1038/s41587-019-0333-6
chicago: Garriga, Edgar, Paolo Di Tommaso, Cedrik Magis, Ionas Erb, Leila Mansouri,
Athanasios Baltzis, Hafid Laayouni, Fyodor Kondrashov, Evan Floden, and Cedric
Notredame. “Large Multiple Sequence Alignments with a Root-to-Leaf Regressive
Method.” Nature Biotechnology. Springer Nature, 2019. https://doi.org/10.1038/s41587-019-0333-6.
ieee: E. Garriga et al., “Large multiple sequence alignments with a root-to-leaf
regressive method,” Nature Biotechnology, vol. 37, no. 12. Springer Nature,
pp. 1466–1470, 2019.
ista: Garriga E, Di Tommaso P, Magis C, Erb I, Mansouri L, Baltzis A, Laayouni H,
Kondrashov F, Floden E, Notredame C. 2019. Large multiple sequence alignments
with a root-to-leaf regressive method. Nature Biotechnology. 37(12), 1466–1470.
mla: Garriga, Edgar, et al. “Large Multiple Sequence Alignments with a Root-to-Leaf
Regressive Method.” Nature Biotechnology, vol. 37, no. 12, Springer Nature,
2019, pp. 1466–70, doi:10.1038/s41587-019-0333-6.
short: E. Garriga, P. Di Tommaso, C. Magis, I. Erb, L. Mansouri, A. Baltzis, H.
Laayouni, F. Kondrashov, E. Floden, C. Notredame, Nature Biotechnology 37 (2019)
1466–1470.
date_created: 2019-12-15T23:00:43Z
date_published: 2019-12-01T00:00:00Z
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title: Large multiple sequence alignments with a root-to-leaf regressive method
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