WarpSTR

WarpSTR determines tandem repeat lengths directly from raw nanopore sequencing signals to improve accuracy of short tandem repeat (STR) genotyping.


Key Features:

  • Raw-signal analysis: Determines STR lengths directly from raw Oxford Nanopore signals without relying on basecalled sequences.
  • Simple and complex repeat handling: Characterizes both simple and complex tandem repeats, including repeats with minor motif variations.
  • Algorithmic approach: Employs a finite-state automaton combined with a search algorithm analogous to dynamic time warping to align signal patterns to repeat models.
  • Improved accuracy: Demonstrated reduced mean absolute error in STR length estimates compared to basecalling-based methods and existing tools such as STRique in an evaluation of 241 STRs.
  • Nanopore-read suitability: Designed for analysis of long reads produced by Oxford Nanopore sequencing, addressing basecalling unreliability in repetitive regions.

Scientific Applications:

  • STR genotyping: Precise estimation of short tandem repeat lengths for research and clinical genotyping.
  • Genomics research: Analysis of tandem-repeat variation in genomic studies that leverage long-read nanopore sequencing.
  • Clinical diagnostics: Improved STR length determination for applications where repeat expansions or contractions have clinical relevance.

Methodology:

Analyzes raw nanopore signals using a finite-state automaton combined with a search algorithm analogous to dynamic time warping to locate and quantify tandem repeat motifs without using basecalled sequences.

Topics

Details

Cost:
Free of charge
Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/29/2024
Last Updated:
11/24/2024

Operations

Publications

Sitarčík J, Vinař T, Brejová B, Krampl W, Budiš J, Radvánszky J, Lucká M. WarpSTR: determining tandem repeat lengths using raw nanopore signals. Bioinformatics. 2023;39(6). doi:10.1093/bioinformatics/btad388. PMID:37326967. PMCID:PMC10307940.

PMID: 37326967
Funding: - European Union’s Horizon 2020 research and innovation programme: 872539

Documentation