Abstract
Abstract
Background
De novo genome assembly typically produces a set of contigs instead of the complete genome. Thus additional data such as genetic linkage maps, optical maps, or Hi-C data is needed to resolve the complete structure of the genome. Most of the previous work uses the additional data to order and orient contigs.
Results
Here we introduce a framework to guide genome assembly with additional data. Our approach is based on clustering the reads, such that each read in each cluster originates from nearby positions in the genome according to the additional data. These sets are then assembled independently and the resulting contigs are further assembled in a hierarchical manner. We implemented our approach for genetic linkage maps in a tool called HGGA.
Conclusions
Our experiments on simulated and real Pacific Biosciences long reads and genetic linkage maps show that HGGA produces a more contiguous assembly with less contigs and from 1.2 to 9.8 times higher NGA50 or N50 than a plain assembly of the reads and 1.03 to 6.5 times higher NGA50 or N50 than a previous approach integrating genetic linkage maps with contig assembly. Furthermore, also the correctness of the assembly remains similar or improves as compared to an assembly using only the read data.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Cited by
1 articles.
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1. HRGF-GapCloser: A gap filling method base on HiFi read and read clustering;Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing;2024-01-26