Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (S1_S2)

ABSTRACT: Preservation of a balanced chromosomal content is regarded to be a key point for the success of multicellular organisms. Chromosomal segregation takes place under the strict control of well-orchestrated cell-cycle checkpoints, consequently leading to accurate transmission of intact chromosomes. Estimates of the whole chromosomal error rates per cell division based on cytogenetic analyses of newborns and products of conception, range between 4.57x10-5 and 3.42x10-4. Recent sporadic studies of single cell genome wide CNV analysis suggested that the error rate might be higher than currently estimated. To obtain accurate measures of chromosomal error rates, we plated single fibroblast and analyzed the two daughter sister cells following a single cell division. In total 14 single fibroblasts derived from 7 mitoses carried segmental aneuploidies in a total of 178 cells from 5 different cell lines that were analyzed after a single cell division, indicating a mean frequency of 7.9% in vitro. In conclusion, the chromosomal stability is hundreds times lower than the current dogma, showing that chromosomal instability is a common place and putting the efficacy of the DNA-repair mechanisms and control checkpoints in question.

ORGANISM(S): Homo sapiens

PROVIDER: GSE75069 | GEO | 2016/05/01

SECONDARY ACCESSION(S): PRJNA302482

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (S1)

Project description:Preservation of chromosome integrity is key for the survival of any organism. To assure segregation of intact chromosomes, cell division is presumed to be under strict control of cell-cycle checkpoints. Estimates of chromosomal mutation rates per generation based on cytogenetic analyses of newborns and products of conception, range between 4.57x10-5 and 3.42x10-4. In contrast, the high incidence of segmental imbalances detected by single-cell genome-wide copy number profiling indicates that the error rate per cell division might be more than an order of magnitude higher. To directly measure the de novo incidence of segmental chromosomal imbalances, we plated a single fibroblast and analyzed the genomes of the two sister cells following a single cell division. Analysis of 89 pairs of sister cells (178 single cells in total) from 5 different cell lines revealed megabase-sized chromosomal imbalances in 21 fibroblasts, 14 of which derived from 7 mitoses with complementary segmental aneuploidies in the two daughter cells. The mutation rate of segmental imbalances is thus at least 7.9%, indicating that compared to the per generation chromosome stability is at least 100 times lower in vitro and likely underestimated in vivo.

2016-05-01 | GSE75068 | GEO

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (SB_sc_2)

2016-05-01 | GSE75098 | GEO

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (SB)

2016-05-01 | GSE75089 | GEO

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (S2)

2016-05-01 | GSE75088 | GEO

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (BAC array)

2016-05-01 | GSE75065 | GEO

Segmental chromosomal imbalances arise at high frequency in human fibroblasts (SB_sc_1)

2016-05-01 | GSE75093 | GEO

β-TrCP and Casein Kinase II mediated degradation of cyclin F controls timely mitotic progression

Project description:Orderly progressions of events in the cell division cycle are necessary to ensure the replication of DNA and cell division. Checkpoint systems allow the accurate execution of each cell cycle phase. The precise regulation of the levels of cyclin proteins is fundamental to coordinate cell division with checkpoints, avoiding genome instability. Cyclin F has important functions in regulating the cell cycle during the G2 checkpoint; however, the mechanisms underlying the regulation of cyclin F are poorly understood. Here, we observe that cyclin F is regulated by proteolysis through β-TrCP. β-TrCP recognizes cyclin F through a non-canonical degron site (TSGXXS) after its phosphorylation by Casein Kinase II. The degradation of cyclin F mediated by β-TrCP occurs at the G2/M transition and this event is required to promote mitotic progression and favours the activation of a transcriptional programme required for mitosis.

2018-10-02 | PXD010737 | Pride

Polymerase Error Rates by PacBio Sequencing

Project description:Polymerase Specific Error Rates and Profiles Identified by PacBio Single Molecule Sequencing

| PRJEB12175 | ENA

A robust machine learning approach for missing persons cases with high genotyping errors

Project description:Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a robust machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.

2022-07-27 | GSE209804 | GEO

Definitive haplotypes from a collection of complete hydatidiform moles (CHMs)

Project description:The haplotype map constructed by the International HapMap Project is a valuable source for the studies of disease genes, population structure, and evolution. In the Project, haplotypes have been inferred from experimentally determined genotypes, and are fairly accurate for Caucasians and Africans since the inference was based on the genotypes of trios. However, the inference for the Asians populations was less accurate, because of the lack of familial information. Here we assessed how the error in the inference can affect downstream studies, especially the analysis of recent positive selections, by comparing the results of the analyses using the data of HapMap JPT and of definitive haplotypes (DHaplo-DB) determined by us from a collection of Japanese complete hydatidiform moles (CHM), each of which carries a genome derived from a single sperm. We found that the error in JPT was not uniform throughout the genome, and the statistics for recent positive selection was significantly affected. Keywords: Definitive haplotype determination using CHMs, which carry haploid genomes. 100 CHM samples collected throughout Japan were analyzed by Affymetrix Genechip Mapping 500K Set array.

2010-04-05 | E-GEOD-12713 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data