Single GAM datasets collected with one nuclear profile each (samples failing QC)
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ABSTRACT:
PROVIDER: PRJNA700650 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. We applied the original GAM protocol to Mouse ES cells to generate a deeper dataset for comparison to Hi-C. Overall design: This series contains GAM samples with a single nuclear profile from mouse embryonic stem (mES) cells that failed our QC checks.
2022-06-13 | GSE166377 | GEO
Project description:Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. We developed multiplex-GAM, a faster and more affordable version of Genome Architecture Mapping (GAM), a ligation-free technique to map chromatin contacts genome-wide. We applied multiplex-GAM to Mouse ES cells. Overall design: This series contains multiplex-GAM samples containing an average of three nuclear profiles from mouse embryonic stem (mES) cells that failed our QC checks.
2022-06-13 | GSE166379 | GEO
Project description:Multiplex GAM datasets collected with an average of three nuclear profiles each (samples failing QC)
| PRJNA700652 | ENA
Project description:Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. We developed multiplex-GAM, a faster and more affordable version of Genome Architecture Mapping (GAM), a ligation-free technique to map chromatin contacts genome-wide. We applied multiplex-GAM to Mouse ES cells. Overall design: This series contains multiplex-GAM samples each containing an average of three nuclear profiles from mouse embryonic stem (mES) cells.
2022-06-13 | GSE166380 | GEO
Project description:Multiplex GAM datasets collected with an average of three nuclear profiles each
| PRJNA700651 | ENA
Project description:Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. We applied the original GAM protocol to Mouse ES cells to generate a deeper dataset for comparison to Hi-C. Overall design: This series contains GAM samples each containing a single nuclear profiles from mouse embryonic stem (mES) cells.
2022-06-13 | GSE166378 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. Overall design: Refer to individual Series Processed data files included at the foot of this record: gam_contact_matrices_40kb.tar.gz - "GAM normalised linkage (Dprime) matrices at 40kb resolution." slice_interaction_matrices_40kb.tar.gz - "SLICE prominent interaction matrices (thresholded at p<=0.05) at 40kb resolution."
2022-06-13 | GSE166381 | GEO
Project description:Hi-C, split-pool recognition of interactions by tag extension (SPRITE) and genome architecture mapping (GAM) are powerful technologies utilized to probe chromatin interactions genome wide, but how faithfully they capture three-dimensional (3D) contacts and how they perform relative to each other is unclear, as no benchmark exists. Here, we compare these methods in silico in a simplified, yet controlled, framework against known 3D structures of polymer models of murine and human loci, which can recapitulate Hi-C, GAM and SPRITE experiments and multiplexed fluorescence in situ hybridization (FISH) single-molecule conformations. We find that in silico Hi-C, GAM and SPRITE bulk data are faithful to the reference 3D structures whereas single-cell data reflect strong variability among single molecules. The minimal number of cells required in replicate experiments to return statistically similar contacts is different across the technologies, being lowest in SPRITE and highest in GAM under the same conditions. Noise-to-signal levels follow an inverse power law with detection efficiency and grow with genomic distance differently among the three methods, being lowest in GAM for genomic separations >1 Mb.
| S-EPMC8416658 | BioStudies
Project description:24 hours following transfection with either a control mix, a construct overexpressing GAM or siRNAs directed against GAM (siGAM), THP-1 cells were challenged with LPS 100 ng/ml. RNAs were analyzed after 6 hours of LPS challenge
| E-TABM-868 | BioStudies
Project description:<h4>Background</h4>In recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.<h4>Results</h4>GAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools.<h4>Conclusions</h4>The difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.
| S-EPMC3633056 | BioStudies