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. 2020 Jul 20;21:497. doi: 10.1186/s12864-020-06892-5

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — An overview of the experimental pipeline of this study. The input is either a set of sequences, or a complete distance matrix. We generate incomplete distance matrix from input sequences or input complete distance matrix by using various missingness mechanisms. Next, we apply various imputation techniques to impute the missing entries in the incomplete distance matrix and thereby, generating (complete) imputed distance matrices. Next, we estimate phylogenetic trees from the imputed distance matrices using FastME. Finally, we compare the estimated trees with the model tree to evaluate the performance of various imputation techniques