Dataset Information

LOGIQA: a database dedicated to long-range genome interactions quality assessment.

ABSTRACT: Proximity ligation-mediated methods are essential to study the impact of three-dimensional chromatin organization on gene programming. Albeit significant progress has been made in the development of computational tools that assess long-range chromatin interactions, next to nothing is known about the quality of the generated datasets.We have developed LOGIQA ( www.ngs-qc.org/logiqa ), a database hosting quality scores for long-range genome interaction assays, accessible through a user-friendly web-based environment.Currently, LOGIQA harbors QC scores for >900 datasets, which provides a global view of their relative quality and reveals the impact of genome size, coverage and other technical aspects. LOGIQA provides a user-friendly dataset query panel and a genome viewer to assess local genome-interaction maps at different resolution and quality-assessment conditions.LOGIQA is the first database hosting quality scores dedicated to long-range chromatin interaction assays, which in addition provides a platform for visualizing genome interactions made available by the scientific community.

SUBMITTER: Mendoza-Parra MA

PROVIDER: S-EPMC4868109 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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LOGIQA: a database dedicated to long-range genome interactions quality assessment.

Mendoza-Parra Marco-Antonio MA Blum Matthias M Malysheva Valeriya V Cholley Pierre-Etienne PE Gronemeyer Hinrich H

BMC genomics 20160516

<h4>Background</h4>Proximity ligation-mediated methods are essential to study the impact of three-dimensional chromatin organization on gene programming. Albeit significant progress has been made in the development of computational tools that assess long-range chromatin interactions, next to nothing is known about the quality of the generated datasets.<h4>Method</h4>We have developed LOGIQA ( www.ngs-qc.org/logiqa ), a database hosting quality scores for long-range genome interaction assays, acc ...[more]

PMID: 27185059

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Project description:BACKGROUND: Complex human diseases may be associated with many gene interactions. Gene interactions take several different forms and it is difficult to identify all of the interactions that are potentially associated with human diseases. One approach that may fill this knowledge gap is to infer previously unknown gene interactions via identification of non-physical linkages between different mutations (or single nucleotide polymorphisms, SNPs) to avoid hitchhiking effect or lack of recombination. Strong non-physical SNP linkages are considered to be an indication of biological (gene) interactions. These interactions can be physical protein interactions, regulatory interactions, functional compensation/antagonization or many other forms of interactions. Previous studies have shown that mutations in different genes can be linked to the same disorders. Therefore, non-physical SNP linkages, coupled with knowledge of SNP-disease associations may shed more light on the role of gene interactions in human disorders. A user-friendly web resource that integrates information about non-physical SNP linkages, gene annotations, SNP information, and SNP-disease associations may thus be a good reference for biomedical research. FINDINGS: Here we extracted the SNPs located within the promoter or exonic regions of protein-coding genes from the HapMap database to construct a database named the Linkage-Disequilibrium-based Gene Interaction database (LDGIdb). The database stores 646,203 potential human gene interactions, which are potential interactions inferred from SNP pairs that are subject to long-range strong linkage disequilibrium (LD), or non-physical linkages. To minimize the possibility of hitchhiking, SNP pairs inferred to be non-physically linked were required to be located in different chromosomes or in different LD blocks of the same chromosomes. According to the genomic locations of the involved SNPs (i.e., promoter, untranslated region (UTR) and coding region (CDS)), the SNP linkages inferred were categorized into promoter-promoter, promoter-UTR, promoter-CDS, CDS-CDS, CDS-UTR and UTR-UTR linkages. For the CDS-related linkages, the coding SNPs were further classified into nonsynonymous and synonymous variations, which represent potential gene interactions at the protein and RNA level, respectively. The LDGIdb also incorporates human disease-association databases such as Genome-Wide Association Studies (GWAS) and Online Mendelian Inheritance in Man (OMIM), so that the user can search for potential disease-associated SNP linkages. The inferred SNP linkages are also classified in the context of population stratification to provide a resource for investigating potential population-specific gene interactions. CONCLUSION: The LDGIdb is a user-friendly resource that integrates non-physical SNP linkages and SNP-disease associations for studies of gene interactions in human diseases. With the help of the LDGIdb, it is plausible to infer population-specific SNP linkages for more focused studies, an avenue that is potentially important for pharmacogenetics. Moreover, by referring to disease-association information such as the GWAS data, the LDGIdb may help identify previously uncharacterized disease-associated gene interactions and potentially lead to new discoveries in studies of human diseases.

Project description:Chemotaxis enables cells to systematically approach distant targets that emit a diffusible guiding substance. However, the visual observation of an encounter between a cell and a target does not necessarily indicate the presence of a chemotactic approach mechanism, as even a blindly migrating cell can come across a target by chance. To distinguish between the chemotactic approach and blind migration, we present an objective method that is based on the analysis of time-lapse recorded cell migration trajectories: For each movement step of a cell relative to the position of a potential target, we compute a p value that quantifies the likelihood of the movement direction under the null-hypothesis of blind migration. The resulting distribution of p values, pooled over all recorded cell trajectories, is then compared to an ensemble of reference distributions in which the positions of targets are randomized. First, we validate our method with simulated data, demonstrating that it reliably detects the presence or absence of remote cell-cell interactions. In a second step, we apply the method to data from three-dimensional collagen gels, interspersed with highly migratory natural killer (NK) cells that were derived from two different human donors. We find for one of the donors an attractive interaction between the NK cells, pointing to a cooperative behavior of these immune cells. When adding nearly stationary K562 tumor cells to the system, we find a repulsive interaction between K562 and NK cells for one of the donors. By contrast, we find attractive interactions between NK cells and an IL-15-secreting variant of K562 tumor cells. We therefore speculate that NK cells find wild-type tumor cells only by chance, but are programmed to leave a target quickly after a close encounter. We provide a freely available Python implementation of our p value method that can serve as a general tool for detecting long-range interactions in collective systems of self-driven agents.

Dataset Information

LOGIQA: a database dedicated to long-range genome interactions quality assessment.

Publications

LOGIQA: a database dedicated to long-range genome interactions quality assessment.

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