MULTIPLE SEQUENCE ALIGNMENT AS A SEQUENCE-TO-SEQUENCE LEARNING PROBLEM

Edo Dotan; Yonatan Belinkov; Oren Avram; Elya Wygoda; Noa Ecker; Michael Alburquerque; Omri Keren; Gil Loewenthal; Tal Pupko

MULTIPLE SEQUENCE ALIGNMENT AS A SEQUENCE-TO-SEQUENCE LEARNING PROBLEM

Edo Dotan, Yonatan Belinkov^*, Oren Avram, Elya Wygoda, Noa Ecker, Michael Alburquerque, Omri Keren, Gil Loewenthal, Tal Pupko^*

^*Corresponding author for this work

The Shmunis School of Biomedicine and Cancer Research

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE.

Original language	English
State	Published - 2023
Event	11th International Conference on Learning Representations, ICLR 2023 - Kigali, Rwanda Duration: 1 May 2023 → 5 May 2023

Conference

Conference	11th International Conference on Learning Representations, ICLR 2023
Country/Territory	Rwanda
City	Kigali
Period	1/05/23 → 5/05/23

Funding

Funders	Funder number
Technion-Israel Institute of Technology
Azrieli Foundation
Tel Aviv University
Edmond J. Safra Center for Bioinformatics
Israel Science Foundation	2818/21, 448/20

Cite this

@conference{4ceaab159902481ead6f8cfe69d40251,

title = "MULTIPLE SEQUENCE ALIGNMENT AS A SEQUENCE-TO-SEQUENCE LEARNING PROBLEM",

abstract = "The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE.",

author = "Edo Dotan and Yonatan Belinkov and Oren Avram and Elya Wygoda and Noa Ecker and Michael Alburquerque and Omri Keren and Gil Loewenthal and Tal Pupko",

note = "Publisher Copyright: {\textcopyright} 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.; 11th International Conference on Learning Representations, ICLR 2023 ; Conference date: 01-05-2023 Through 05-05-2023",

year = "2023",

language = "אנגלית",

}

TY - CONF

T1 - MULTIPLE SEQUENCE ALIGNMENT AS A SEQUENCE-TO-SEQUENCE LEARNING PROBLEM

AU - Dotan, Edo

AU - Belinkov, Yonatan

AU - Avram, Oren

AU - Wygoda, Elya

AU - Ecker, Noa

AU - Alburquerque, Michael

AU - Keren, Omri

AU - Loewenthal, Gil

AU - Pupko, Tal

PY - 2023

Y1 - 2023

N2 - The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE.

AB - The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE.

UR - http://www.scopus.com/inward/record.url?scp=85199894991&partnerID=8YFLogxK

M3 - ???researchoutput.researchoutputtypes.contributiontoconference.paper???

AN - SCOPUS:85199894991

T2 - 11th International Conference on Learning Representations, ICLR 2023

Y2 - 1 May 2023 through 5 May 2023

ER -

MULTIPLE SEQUENCE ALIGNMENT AS A SEQUENCE-TO-SEQUENCE LEARNING PROBLEM

Abstract

Conference

Funding

Other files and links

Fingerprint

Cite this