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.