Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

137
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
137
Quality Control01:05

Quality Control

273
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...
273
Data Validation01:15

Data Validation

231
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
231
Sample Preparation for Analysis: Overview01:21

Sample Preparation for Analysis: Overview

312
Sample preparation is an essential step in the analytical process. It involves preparing a sample so that it can be analyzed accurately. The goal is to extract the analyte, the substance you want to measure, from the sample while removing any components that may interfere with the analysis. Sample preparation techniques vary depending on the physical state of the sample.
Bulk or large solid samples are typically reduced in size using grinding, crushing, or milling techniques to increase the...
312

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ex vivo drug sensitivity testing predicts treatment outcomes in advanced ovarian cancer.

NPJ precision oncology·2026
Same author

Mixed-methods process evaluation of ctDNA use to guide decision-making in patients with advanced solid cancers: study protocol for a substudy of the LIQPLAT trial.

BMJ open·2025
Same author

Case report: Histiocytic sarcoma as transdifferentiation of a marginal zone lymphoma-a case presentation based on <i>post mortem</i> examination and review of the literature.

Frontiers in oncology·2025
Same author

Tumor immune dynamics and long-term clinical outcome of stage IIIA NSCLC patients treated with neoadjuvant chemoimmunotherapy.

Nature communications·2025
Same author

Recurrent clonal radiotherapy-associated fibroepithelial polyp of the pharynx: do low grade radiogenic stromal tumours exist? Case report.

Virchows Archiv : an international journal of pathology·2025
Same author

Novel KIT mutation, D816_N819delinsll, in a patient with systemic mastocytosis: a case report.

Virchows Archiv : an international journal of pathology·2025

Related Experiment Video

Updated: Sep 6, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.9K

Preanalytical Variables and Sample Quality Control for Clinical Variant Analysis.

Ilaria Alborelli1, Philip M Jermann2

  • 1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.

Methods in Molecular Biology (Clifton, N.J.)
|June 25, 2022
PubMed
Summary
This summary is machine-generated.

High-quality next-generation sequencing (NGS) data is crucial for precision oncology. This review details preanalytical steps and quality control metrics essential for accurate tumor genetic profiling and effective clinical decision-making.

Keywords:
FFPELiquid biopsyMolecular pathologyNext-generation sequencingPreanalytical variableVariant callingctDNA

More Related Videos

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.1K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K

Related Experiment Videos

Last Updated: Sep 6, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.9K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.1K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K

Area of Science:

  • Oncology
  • Genomics
  • Molecular Diagnostics

Background:

  • Precision oncology relies heavily on molecular profiling of solid tumors.
  • Biomarker-driven clinical trials are prevalent, necessitating accurate genetic assessments.
  • Targeted therapies and clinical trial enrollment depend on reliable tumor genetic data.

Purpose of the Study:

  • To outline critical preanalytical steps for high-quality next-generation sequencing (NGS) data.
  • To identify challenges and pitfalls in NGS workflows for solid tumors.
  • To provide guidelines for robust genetic variant detection in a clinical setting.

Main Methods:

  • Review of preanalytical quality control measures in NGS.
  • Discussion of sample types, DNA extraction, library preparation, and sequencing platforms.
  • Emphasis on sequencing quality control metrics for variant detection.

Main Results:

  • Preanalytical steps significantly influence the quality of NGS data.
  • Each stage of the NGS workflow presents unique challenges.
  • Adherence to quality control guidelines ensures optimal results for variant assessment.

Conclusions:

  • Standardized preanalytical quality control is vital for accurate tumor molecular profiling.
  • Addressing workflow challenges enhances the reliability of genetic variant detection.
  • Implementing QC metrics supports precise and robust clinical decision-making in oncology.