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