PEMer

PEMer detects structural variants in genomic sequences from paired-end sequencing reads to map and characterize genomic structural variation.


Key Features:

  • PEMer workflow: Implements a sensitive workflow for detecting structural variants from paired-end sequence reads using a coverage-adjusted multi-cutoff scoring strategy.
  • Simulation-based error models: Uses simulation-based error models to assign confidence values to each detected structural variant.
  • Platform compatibility: Supports multiple next-generation sequencing platforms.
  • Relative insensitivity to base-calling errors: Shows relative insensitivity to base-calling errors as assessed by simulation studies.
  • Back-end database: Provides a back-end database for storage and retrieval of genomic data and structural-variant results.

Scientific Applications:

  • Structural variant discovery: Detection and reconstruction of structural variants from paired-end sequencing data.
  • Population genomics: Mapping genomic structural variation in population-scale projects such as the 1000 Genomes Project.
  • Disease and phenotype studies: Identifying genetic variations that may contribute to disease susceptibility or phenotypic differences.
  • Evolutionary biology: Characterizing structural variation to study genetic diversity and evolutionary processes.

Methodology:

Processes paired-end sequence reads, applies a coverage-adjusted multi-cutoff scoring strategy for structural-variant reconstruction, employs simulation-based error models to assign confidence values and evaluate base-calling error effects, and stores results in a back-end database.

Topics

Details

Tool Type:
workflow
Operating Systems:
Linux
Programming Languages:
Perl, Python
Added:
1/13/2017
Last Updated:
11/25/2024

Operations

Publications

Korbel JO, Abyzov A, Mu XJ, Carriero N, Cayting P, Zhang Z, Snyder M, Gerstein MB. PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biology. 2009;10(2). doi:10.1186/gb-2009-10-2-r23. PMID:19236709. PMCID:PMC2688268.