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Large multiple sequence alignments with a root-to-leaf regressive method

Nature Biotechnology volume 37, pages 1466–1470 (2019)Cite this article

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Abstract

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 from 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.

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Fig. 1: Regressive algorithm overview.
Fig. 2: Relative performances of alternative MSA algorithm combinations.
Fig. 3: CPU requirements of the regressive algorithm on HomFam datasets containing more than 10,000 sequences.

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Data availability

All data, analyses and results are available from Zenodo (https://doi.org/10.5281/zenodo.3271452).

Code availability

The regressive alignment algorithm has been implemented in T-Coffee and is available at the T-Coffee website (http://www.tcoffee.org) and on GitHub (https://github.com/cbcrg/tcoffee). A GitHub repository containing the Nextflow workflow25 and Jupyter notebooks26 to replicate the analysis are available at https://github.com/cbcrg/dpa-analysis (release v.1.2).

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Acknowledgements

We thank G. Riddihough for revisions and comments on the manuscript and O. Gascuel for suggestions. This project was supported by the Centre for Genomic Regulation, the Spanish Plan Nacional, the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa’ (E.G., P.T., C.M., I.E., L.M., A.B., F.K., E.F. and C.N.) and an ERC Consolidator Grant from the European Commission, grant agreement no. 771209 ChrFL (F.K.).

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Authors and Affiliations

  1. Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain

    Edgar Garriga, Paolo Di Tommaso, Cedrik Magis, Ionas Erb, Leila Mansouri, Athanasios Baltzis, Evan Floden & Cedric Notredame

  2. Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain

    Hafid Laayouni

  3. Bioinformatics Studies, ESCI-UPF, Barcelona, Spain

    Hafid Laayouni

  4. Institute of Science and Technology, Klosterneuburg, Austria

    Fyodor Kondrashov

  5. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Cedric Notredame

Authors
  1. Edgar Garriga
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  9. Evan Floden
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Contributions

C.N. designed and implemented the algorithm. E.F., E.G., L.M., A.B. and P.D.T designed the validation procedure and carried out the validation. I.E. performed statistical and CCA analyses. E.F., C.N., E.G., C.M., L.M., A.B., P.D.T., I.E., F.K. and H.L. wrote and edited the manuscript.

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Correspondence to Evan Floden or Cedric Notredame.

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Integrated supplementary information

Supplementary Figure 1 Effect of size of N on HomFam average TC score and relative CPU time.

Summary of Total Column score and CPU usage values collected over HomFam datasets of various sizes of N using either (A) ClustalO with mBed trees or (B) Mafft-FFTNS1 with PartTree trees. Four datasets were excluded from the analysis because of incomplete CPU time reports across the runs (mmp, kunitz, hormone_rec and peroxidase). Each combination of alignment method and tree method, n=90 independent MSA samples.

Supplementary Figure 2 Relative performances of alternative MSA algorithm combinations.

(A) The relative accuracy is defined as the difference between the TC score measured on the projection of embedded sequences and the TC score measured on the direct alignment of these same sequences with the considered method. The three alignment protocols all use a PartTree guide-trees combined with the following aligners Fftns1 in non-regressive mode (red), Fftns1 in regressive mode (green) and Gins1 in regressive mode (blue). The envelope is the standard deviation measured on the averaged values. (B) similar comparison between the regressive deployment of Sparsecore using a mBed guide tree (blue) and the default, non-regressive deployment of this same aligner (red). (C) similar comparison on UPP using a mBed guide-tree for the regressive deployment (blue) and UPP default mode for the non-regressive (red). (D) similar display for Fftns1 using a mBed guide-tree for the regressive (blue) and non-regressive deployment (red). (E) similar analysis using ClustalO as an aligner and PartTree guide-trees in regressive (blue) and non-regressive modes (red). Each combination of alignment method and tree method, n=94 independent MSA samples.

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Supplementary Figs. 1 and 2, Notes 1 and 2 and Tables 1–7.

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Garriga, E., Di Tommaso, P., Magis, C. et al. Large multiple sequence alignments with a root-to-leaf regressive method. Nat Biotechnol 37, 1466–1470 (2019). https://doi.org/10.1038/s41587-019-0333-6

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