HGGA

HGGA hierarchically assembles de novo genomes by clustering sequencing reads using auxiliary genomic data such as genetic linkage maps to improve assembly contiguity and accuracy.


Key Features:

  • Integration of auxiliary genomic data: Uses additional genomic data sources, specifically genetic linkage maps, to guide read clustering and assembly.
  • Read clustering by genomic proximity: Clusters sequencing reads based on their proximity within the genome inferred from the auxiliary data.
  • Independent cluster assembly: Assembles each read cluster independently into contigs.
  • Hierarchical contig assembly: Performs a hierarchical assembly of contigs to refine overall genome structure.
  • Implementation for PacBio long reads: Implemented and tested on simulated and real datasets derived from Pacific Biosciences long-read sequencing technologies.
  • Improved contiguity metrics: Produces fewer contigs and substantially higher NGA50 or N50 values, ranging from 1.2 to 9.8 times greater than plain read assembly and 1.03 to 6.5 times higher than previous methods integrating genetic linkage maps with contig assembly.
  • Preserved or improved correctness: Maintains or improves assembly correctness relative to assemblies produced from raw sequencing data alone.

Scientific Applications:

  • De novo genome assembly with linkage maps: Producing more contiguous and accurate de novo assemblies by incorporating genetic linkage maps.
  • Assembly of PacBio long-read datasets: Improving contiguity and accuracy for assemblies derived from Pacific Biosciences long-read sequencing technologies.
  • High-resolution genome mapping and analysis: Enabling analyses that require contiguous genome reconstructions for downstream mapping and comparative studies.
  • Benchmarking assembly strategies: Evaluating assembly performance on simulated and real datasets to compare contiguity and correctness metrics.

Methodology:

Clusters reads by genomic proximity using auxiliary data (e.g., genetic linkage maps), assembles each cluster into contigs independently, and performs hierarchical assembly of those contigs.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
workflow
Programming Languages:
Python
Added:
8/28/2022
Last Updated:
11/24/2024

Operations

Publications

Walve R, Salmela L. HGGA: hierarchical guided genome assembler. BMC Bioinformatics. 2022;23(1). doi:10.1186/s12859-022-04701-2. PMID:35525918. PMCID:PMC9077837.