UMIErrorCorrect

UMIErrorCorrect performs clustering of unique molecular identifiers (UMIs), error correction, and variant calling on UMI-containing sequencing reads to improve detection of low-frequency mutant alleles such as those in cell-free DNA from liquid biopsies.


Key Features:

  • Input Flexibility: Requires only FASTQ files as input.
  • Integrated Workflow: Integrates alignment, UMI clustering, error correction, and variant calling within a single pipeline.
  • Error Correction and Sensitivity: Leverages UMIs to enhance accuracy and sensitivity of variant detection, improving detection of low-frequency alleles.
  • Validation and Performance: Validated with cell-free DNA reference material containing known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public datasets, demonstrating superior variant detection across targeted sequencing protocols.
  • Customization and Flexibility: Adaptable to data from various library preparation protocols and enrichment chemistries that utilize UMIs.

Scientific Applications:

  • Liquid biopsy analysis: Detection of low-frequency mutant alleles in cell-free DNA from liquid biopsies.
  • Targeted sequencing assays: Sensitive variant calling in targeted sequencing protocols that employ UMIs.
  • Research and clinical genomics: Use in both basic research and clinical applications requiring high-sensitivity UMI-based sequencing analysis.

Methodology:

Accepts FASTQ input and performs alignment, UMI clustering, error correction, and variant calling.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python, R
Added:
10/5/2022
Last Updated:
11/24/2024

Operations

Publications

Österlund T, Filges S, Johansson G, Ståhlberg A. UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers. Clinical Chemistry. 2022;68(11):1425-1435. doi:10.1093/clinchem/hvac136. PMID:36031761.

PMID: 36031761
Funding: - Swedish Research Council: 2020-01008 - ALF-agreement: 965065 - Sweden’s Innovation Agency: 2018-00421, 2020-04141 - Swedish Cancer Society: 19-0306 - Swedish Childhood Cancer Foundation: 2020-007, MTI2019-0008

Documentation

Links