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. 2021 May 25;17:54. doi: 10.1186/s13007-021-00754-1

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© The Author(s) 2021

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. 1 — Overview of processes used to create GOMAP Annotations Top: data inputs are shown as a white box. Sequence similarity components are shown in lime green. InterProScan domain-based annotations are shown in orange. For the CAFA processes (Argot2.5 and PANNZER; shown in tan boxes) pre-processing steps are shown in purple and blue, respectively. The CAFA process FANN-GO does not require preprocessing (red). Once each annotation type is produced, these are combined, duplicates and redundancies are removed, and the aggregate dataset is assembled (turquoise)