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.