IMOS
IMOS aligns noisy long reads to reference genomes to provide accurate and scalable alignments of long-read sequencing data for downstream genomic analyses.
Key Features:
- High Performance on Distributed Systems: Operates on single nodes and distributed clusters to reduce processing time for large genomic datasets.
- Improved Accuracy and Speed: Implements an enhanced version of the Meta-aligner (IM) that achieves up to six times faster performance than the predecessor while maintaining alignment accuracy.
- Scalability with Apache Spark Integration: Leverages Apache Spark to enable deployment and scaling across compute clusters.
- Competitive Multi-node Performance: In multi-node configurations outperforms SparkBWA and reports execution 1.5 times faster than IM and 25 times faster than Minimap2.
- Multi-platform Compatibility: Runs on Linux, Windows, and macOS.
Scientific Applications:
- Genome assembly: Provides rapid alignments of noisy long reads to support assembly of complex genomes.
- Variant calling: Supplies accurate read alignments for detection of single-nucleotide variants and small indels.
- Structural variation analysis: Enables mapping of long reads to identify large insertions, deletions, inversions, and translocations.
- Population genomics: Facilitates large-scale alignment of long-read datasets for comparative and population-level studies.
- Personalized medicine: Supports high-throughput alignment of long-read data for clinical genomics applications.
Methodology:
Enhances the Meta-aligner (IM) for single-node acceleration and integrates Minimap2 with Apache Spark to perform distributed alignment of noisy long-read sequencing data.
Topics
Details
- License:
- CC-BY-4.0
- Maturity:
- Mature
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Java, C
- Added:
- 5/22/2019
- Last Updated:
- 6/16/2020
Operations
Publications
Hadadian Nejad Yousefi M, Goudarzi M, Motahari SA. IMOS: improved Meta-aligner and Minimap2 On Spark. BMC Bioinformatics. 2019;20(1). doi:10.1186/s12859-018-2592-5. PMID:30678641. PMCID:PMC6345043.