HGGA: hierarchical guided genome assembler.

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

SUBMITTER: Walve R 

PROVIDER: S-EPMC9077837 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature