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. 2018 Feb 6;8:2505. doi: 10.1038/s41598-018-20536-5

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© The Author(s) 2018

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Secreted effector protein prediction in the early life stage transcriptome of H. glycines. Predicted peptides from the transcriptome were put through two separate pipelines to identify candidate effectors. One pipeline utilized prediction of a signal peptide and lack of a predicted transmembrane domain (TMD) while the other utilized N-Preffector, a machine learning algorithm. Numbers shown here are predicted peptides remaining after each step in the pipeline.