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. 2019 Apr 5;9:5685. doi: 10.1038/s41598-019-42107-y

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© The Author(s) 2019

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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Included data sets and analyses in the study. The SEQC, CCLE and TARGET data sets were used to determine the NB lincRNome. The abundance of the robustly expressed lincRNAs in the lincRNome was used to compare expression between neuroblast and NB samples, primary tumor samples containing CNVs and samples without gains or deletions and NB tumors with and without mutations, amplifications or associations with NB driver genes. The regulation of lincRNAs correlated with NB driver genes was assessed in cellular perturbation models. To determine the involvement of lincRNAs in modulating the effect or regulation of these driver genes, we made use of a state-of-the-art algorithm called LongHorn. Combined, these analyses allow us to arrive at a core set of predicted NB associated lincRNAs.