---
_id: '13059'
abstract:
- lang: eng
  text: "This dataset contains a GitHub repository containing all the data, analysis,
    Nextflow workflows and Jupyter notebooks to replicate the manuscript titled \"Fast
    and accurate large multiple sequence alignments with a root-to-leaf regressive
    method\".\r\nIt also contains the Multiple Sequence Alignments (MSAs) generated
    and well as the main figures and tables from the manuscript.\r\nThe repository
    is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`.\r\nFor
    details on how to use the regressive alignment algorithm, see the T-Coffee software
    suite (https://github.com/cbcrg/tcoffee)."
article_processing_charge: No
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. Fast and accurate large multiple sequence
    alignments with a root-to-leaf regressive method. 2018. doi:<a href="https://doi.org/10.5281/ZENODO.2025846">10.5281/ZENODO.2025846</a>
  apa: Garriga, E., di Tommaso, P., Magis, C., Erb, I., Mansouri, L., Baltzis, A.,
    … Notredame, C. (2018). Fast and accurate large multiple sequence alignments with
    a root-to-leaf regressive method. Zenodo. <a href="https://doi.org/10.5281/ZENODO.2025846">https://doi.org/10.5281/ZENODO.2025846</a>
  chicago: Garriga, Edgar, Paolo di Tommaso, Cedrik Magis, Ionas Erb, Leila Mansouri,
    Athanasios Baltzis, Hafid Laayouni, Fyodor Kondrashov, Evan Floden, and Cedric
    Notredame. “Fast and Accurate Large Multiple Sequence Alignments with a Root-to-Leaf
    Regressive Method.” Zenodo, 2018. <a href="https://doi.org/10.5281/ZENODO.2025846">https://doi.org/10.5281/ZENODO.2025846</a>.
  ieee: E. Garriga <i>et al.</i>, “Fast and accurate large multiple sequence alignments
    with a root-to-leaf regressive method.” Zenodo, 2018.
  ista: Garriga E, di Tommaso P, Magis C, Erb I, Mansouri L, Baltzis A, Laayouni H,
    Kondrashov F, Floden E, Notredame C. 2018. Fast and accurate large multiple sequence
    alignments with a root-to-leaf regressive method, Zenodo, <a href="https://doi.org/10.5281/ZENODO.2025846">10.5281/ZENODO.2025846</a>.
  mla: Garriga, Edgar, et al. <i>Fast and Accurate Large Multiple Sequence Alignments
    with a Root-to-Leaf Regressive Method</i>. Zenodo, 2018, doi:<a href="https://doi.org/10.5281/ZENODO.2025846">10.5281/ZENODO.2025846</a>.
  short: E. Garriga, P. di Tommaso, C. Magis, I. Erb, L. Mansouri, A. Baltzis, H.
    Laayouni, F. Kondrashov, E. Floden, C. Notredame, (2018).
date_created: 2023-05-23T16:08:20Z
date_published: 2018-12-07T00:00:00Z
date_updated: 2025-07-10T11:54:19Z
day: '07'
ddc:
- '570'
department:
- _id: FyKo
doi: 10.5281/ZENODO.2025846
fulldoi: https://doi.org/10.5281/ZENODO.2025846
main_file_link:
- open_access: '1'
  url: https://doi.org/10.5281/zenodo.3271452
month: '12'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
  record:
  - id: '7181'
    relation: used_in_publication
    status: public
status: public
title: Fast and accurate large multiple sequence alignments with a root-to-leaf regressive
  method
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...