SLR

SLR scaffolds genome assemblies using long reads (Pacific Biosciences and Oxford Nanopore) combined with contig classification to resolve repetitive regions and improve assembly completeness and accuracy.


Key Features:

  • Long-Read Utilization: Uses alignment information from long reads (Pacific Biosciences and Oxford Nanopore) to span repetitive regions and inform scaffolding.
  • Contig Classification: Classifies contigs into unique and ambiguous categories, using unique contigs to build draft scaffolds and reserving ambiguous contigs for later integration.
  • Iterative Scaffolding Process: Implements a two-step scaffolding workflow that constructs initial scaffolds from unique contigs and subsequently integrates ambiguous contigs to complete final scaffolds.
  • Enhanced Accuracy and Completeness: Comparative analyses with three popular scaffolding tools reported improved assembly accuracy and completeness attributed to long-read usage and handling of repetitive sequences.

Scientific Applications:

  • Assembly of complex, repeat-rich genomes: Produces more accurate and complete scaffolds for genomes with significant repetitive content.
  • Support for downstream analyses of genetic structure and function: Generates higher-fidelity assemblies that facilitate analyses of genetic structure and function.

Methodology:

Align long reads to contigs to collect alignment information; classify contigs as unique or ambiguous; construct initial scaffolds from unique contigs and then integrate ambiguous contigs to produce final scaffolds.

Topics

Details

License:
GPL-3.0
Programming Languages:
C++, C
Added:
1/14/2020
Last Updated:
1/16/2021

Operations

Publications

Luo J, Lyu M, Chen R, Zhang X, Luo H, Yan C. SLR: a scaffolding algorithm based on long reads and contig classification. BMC Bioinformatics. 2019;20(1). doi:10.1186/s12859-019-3114-9. PMID:31666010. PMCID:PMC6820941.