Quality Control Checkpoints
Quality Control Checkpoints - Related Videos
01:05
Quality Control
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2024
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results. Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
10:55
Study of the DNA Damage Checkpoint using Xenopus Egg Extracts
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Cited by 35 •
2012
Xenopus egg extract is a useful model system to investigate the DNA damage checkpoint. This protocol is for the preparation of Xenopus egg extracts and DNA damage checkpoint inducing reagents. These techniques are adaptable to a variety of DNA damaging approaches in the study of the DNA damage checkpoint signaling.
06:09
Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging
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2016
The social amoebae Dictyostelium discoideum has recently been established as a system to study protein misfolding and proteostasis. Here, we describe a new imaging-based methodology to study temperature-induced protein aggregation and the cellular stress response in D. discoideum.
02:19
The Spindle Assembly Checkpoint
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2023
The spindle assembly checkpoint is a molecular surveillance mechanism ensuring the fidelity of chromosome segregation during anaphase. The checkpoint monitors the completion of all the prerequisite steps before chromosome segregation to determine whether the segregation process should proceed or be delayed. Many proteins function together to control the spindle assembly checkpoint. Mutations affecting these proteins may allow cells to proceed into anaphase prematurely, resulting in the...
09:27
DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
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Cited by 10 •
2011
We demonstrate the use of DNA microarrays for expression profiling of the nervous system. We describe RNA quality control, sample labeling, and array hybridization and scanning.
FAQs
Different checkpoint inputs address distinct risks before cell division proceeds. DNA-damage signals indicate that genetic material may require repair, replication monitoring confirms that copying has finished, and chromosome-attachment surveillance checks whether chromosomes are properly connected for segregation. Coordinating these signals helps prevent damaged, incomplete, or incorrectly distributed genetic information from entering daughter cells.
A pause creates an opportunity to correct DNA problems before the cell advances. If damage or replication defects cannot be resolved, checkpoint pathways can prevent continued progression or promote elimination of the affected cell. This selective response reduces the likelihood that harmful genetic errors will be copied and passed to descendant cells.
The apparent severity and reversibility of the defect influence the outcome. When a problem can be addressed, signaling supports a pause that permits repair or completion of the required process. Irreparable defects trigger a different protective outcome, removing the compromised cell rather than allowing inaccurate genetic information to persist.
Evaluation should address several stages of the experiment rather than relying on a single measurement. DNA sample integrity, contamination, sequencing-read quality, and coverage are central checks described for genetics workflows. Comparing these results with predefined criteria helps identify material or data that may be unsuitable for dependable downstream interpretation.
Quality checks expose problems that could distort the apparent genetic signal, including compromised input material, contamination, poor read quality, or inadequate coverage. Flagging those weaknesses before interpretation helps distinguish trustworthy evidence from technical artifacts. The resulting process lowers the chance that unreliable data will be treated as genuine sequence variation.
They are valuable whenever conclusions depend on the accuracy and reproducibility of genomic information. In research, checkpoints support consistent experiments and more reliable dataset interpretation. In diagnostic settings, they strengthen confidence that reported findings reflect the sample rather than contamination, poor sequencing performance, or insufficient coverage, thereby improving diagnostic reliability.