{"date_updated":"2023-09-06T14:32:52Z","main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894943/"}],"publication":"Nature Biotechnology","_id":"7181","doi":"10.1038/s41587-019-0333-6","issue":"12","page":"1466-1470","publication_status":"published","external_id":{"pmid":["31792410"],"isi":["000500748900021"]},"day":"01","publisher":"Springer Nature","volume":37,"year":"2019","language":[{"iso":"eng"}],"oa":1,"related_material":{"record":[{"relation":"research_data","status":"public","id":"13059"}]},"scopus_import":"1","quality_controlled":"1","pmid":1,"project":[{"_id":"26580278-B435-11E9-9278-68D0E5697425","grant_number":"771209","call_identifier":"H2020","name":"Characterizing the fitness landscape on population and global scales"}],"abstract":[{"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.","lang":"eng"}],"isi":1,"intvolume":"        37","citation":{"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. <i>Nature Biotechnology</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41587-019-0333-6\">https://doi.org/10.1038/s41587-019-0333-6</a>","mla":"Garriga, Edgar, et al. “Large Multiple Sequence Alignments with a Root-to-Leaf Regressive Method.” <i>Nature Biotechnology</i>, vol. 37, no. 12, Springer Nature, 2019, pp. 1466–70, doi:<a href=\"https://doi.org/10.1038/s41587-019-0333-6\">10.1038/s41587-019-0333-6</a>.","ieee":"E. Garriga <i>et al.</i>, “Large multiple sequence alignments with a root-to-leaf regressive method,” <i>Nature Biotechnology</i>, vol. 37, no. 12. Springer Nature, pp. 1466–1470, 2019.","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.” <i>Nature Biotechnology</i>. Springer Nature, 2019. <a href=\"https://doi.org/10.1038/s41587-019-0333-6\">https://doi.org/10.1038/s41587-019-0333-6</a>.","ama":"Garriga E, Di Tommaso P, Magis C, et al. Large multiple sequence alignments with a root-to-leaf regressive method. <i>Nature Biotechnology</i>. 2019;37(12):1466-1470. doi:<a href=\"https://doi.org/10.1038/s41587-019-0333-6\">10.1038/s41587-019-0333-6</a>","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.","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."},"author":[{"first_name":"Edgar","full_name":"Garriga, Edgar","last_name":"Garriga"},{"first_name":"Paolo","last_name":"Di Tommaso","full_name":"Di Tommaso, Paolo"},{"first_name":"Cedrik","last_name":"Magis","full_name":"Magis, Cedrik"},{"full_name":"Erb, Ionas","last_name":"Erb","first_name":"Ionas"},{"full_name":"Mansouri, Leila","last_name":"Mansouri","first_name":"Leila"},{"full_name":"Baltzis, Athanasios","last_name":"Baltzis","first_name":"Athanasios"},{"first_name":"Hafid","full_name":"Laayouni, Hafid","last_name":"Laayouni"},{"full_name":"Kondrashov, Fyodor","last_name":"Kondrashov","first_name":"Fyodor","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8243-4694"},{"first_name":"Evan","full_name":"Floden, Evan","last_name":"Floden"},{"first_name":"Cedric","last_name":"Notredame","full_name":"Notredame, Cedric"}],"department":[{"_id":"FyKo"}],"status":"public","ec_funded":1,"title":"Large multiple sequence alignments with a root-to-leaf regressive method","publication_identifier":{"issn":["10870156"],"eissn":["15461696"]},"type":"journal_article","month":"12","date_published":"2019-12-01T00:00:00Z","oa_version":"Submitted Version","date_created":"2019-12-15T23:00:43Z","article_type":"original","article_processing_charge":"No","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1"}