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. 2021 Jan 13;10(1):giaa154. doi: 10.1093/gigascience/giaa154

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© The Author(s) 2021. Published by Oxford University Press GigaScience.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 1: — An overview of BiG-SLiCE's GCF analysis workflow. Taking an input of region/cluster GenBank files from antiSMASH and MIBiG, (A) BiG-SLiCE converts BGCs into numerical feature vectors, which are used to (B) construct the GCF models (cluster centroids) and (C) calculate BGC-to-GCF membership values. Processed data and results are all stored in a file-based SQL database (using SQLite3 [42]), which can then be used (D) to perform further analysis (via external scripts) or to visualize the result in a user-interactive application.