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. 2020 Jul 18;21:316. doi: 10.1186/s12859-020-03650-y

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© The Author(s) 2020

Open AccessThis 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. 2 — Schematic diagram of strain-based alignment approach. Influenza viral sequences are aligned by FluCS as follows: a The FluSeed Dataset is constructed by quality-checked and rearranged viral sequences. Blocks in different colors represent ten viral segments. The size of each block corresponds to the length of the viral sequence originally retrieved. Blocks in any color tagged with the same Arabic numbers are identified as the same strain. b Rearranged viral sequences are sorted into 11 protein clusters based on gene segments and well aligned within the cluster. Aligned sequences are subjected correct ORF into PB2, PB1, PA, HA, NP, NA, M1, NS1, and PB1-F2. M2 and NS2 are alternatively spliced proteins from M and NS ORF mRNAs, and respectively. c Delineated viral amino acid sequences are easily aligned based on standard influenza viral nomenclature. The analysis platform provides benefits for multi-layer subgrouping based on epidemiological significance