LOGIQA
LOGIQA evaluates the quality of long-range genome interaction assays from proximity ligation-mediated methods to provide quantitative quality scores that inform analyses of three-dimensional chromatin organization and its impact on gene regulation.
Key Features:
- Quality Scoring System: Assigns quantitative quality scores to long-range genome interaction datasets to enable comparative evaluation of dataset integrity.
- Dataset Scope: Contains quality assessments for over 900 long-range genome interaction datasets.
- Technical Parameter Analysis: Evaluates the impact of technical parameters such as genome size and coverage depth on dataset quality.
- Genome Interaction Visualization: Provides visualization of local genome-interaction maps at varying resolutions and under different quality-assessment conditions.
- Proximity Ligation–Method Assessment: Implements an assessment framework specific to proximity ligation-mediated long-range chromatin interaction assays.
Scientific Applications:
- Chromatin architecture studies: Supports analysis of three-dimensional genome organization and chromatin architecture.
- Gene regulation research: Informs investigations into the impact of genome interactions on gene regulation.
- Epigenetics: Aids studies of epigenetic modifications that are associated with long-range chromatin interactions.
- Dataset selection and benchmarking: Facilitates selection of high-quality datasets and benchmarking of experimental and computational protocols.
Methodology:
Uses computational assessment methods that integrate coverage depth and genome size to systematically evaluate long-range chromatin interaction data and highlight potential biases or errors within datasets.
Topics
Details
- Tool Type:
- web application
- Operating Systems:
- Linux, Windows, Mac
- Added:
- 5/19/2018
- Last Updated:
- 12/10/2018
Operations
Publications
Mendoza-Parra M, Blum M, Malysheva V, Cholley P, Gronemeyer H. LOGIQA: a database dedicated to long-range genome interactions quality assessment. BMC Genomics. 2016;17(1). doi:10.1186/s12864-016-2642-1. PMID:27185059. PMCID:PMC4868109.