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. 2016 Apr 11;17:282. doi: 10.1186/s12864-016-2610-9

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© Thiriet-Rupert et al. 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

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Fig. 1 — Identification pipeline. The pipeline is divided into three steps. Step One uses two strategies: i) a similarity search against an algae-based self-built database of known TFs with BLAST software; ii) functional domain annotation with InterProScan and HMMER software. The protein list obtained is the subject of the Step Two: the filtration of false positives according to specific parameters (see Methods). The last step consists in the classification of the putative TF list obtained in Step Two using a homemade perl script followed by manual curation for specific cases (see Methods)