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. 2022 Jun 7;23:426. doi: 10.1186/s12864-022-08628-z

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© The Author(s) 2022

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. 3 — Iterative assembly refinement. (A) Multiple polished draft assemblies are generated (B-D), collected (E) and benchmarked for completeness and contiguity (F) to determine the optimal read overlap for the long leads [LR]. (G) Optimal draft assembly is screened for potential contaminants. (H) The repeats are detected and soft masked. The splice-aware alignments (I) of the RNAseq datasets [RNA] are used for gene prediction (J), and then assessed for completeness (K). Annotations of the non-protein coding RNAs (L) are added to form the final structural annotation (M)