scholarly journals Sequencing of human genomes extracted from single cancer cells isolated in a valveless microfluidic device

Lab on a Chip ◽  
2018 ◽  
Vol 18 (13) ◽  
pp. 1891-1902 ◽  
Author(s):  
Rodolphe Marie ◽  
Marie Pødenphant ◽  
Kamila Koprowska ◽  
Loic Bærlocher ◽  
Roland C. M. Vulders ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Cells ◽  
Genome Sequencing ◽  
Microfluidic Device ◽  
Whole Genome ◽  
Human Genomes ◽  
Single Cancer

Whole genome sequencing of single cancer cells isolated and lysed in an injection-moulded valveless microfluidic device.

scholarly journals Quantifying the influence of mutation detection on tumour subclonal reconstruction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Lydia Y. Liu ◽  
Vinayak Bhandari ◽  
Adriana Salcedo ◽  
Shadrielle M. G. Espiritu ◽  
Quaid D. Morris ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Cells ◽  
Clinical Outcomes ◽  
Cancer Cell ◽  
Genome Sequencing ◽  
Somatic Mutations ◽  
Mutation Detection ◽  
Cell Populations ◽  
Whole Genome ◽  
Prostate Cancers

AbstractWhole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

scholarly journals Quantifying the Influence of Mutation Detection on Tumour Subclonal Reconstruction

2018 ◽  
Author(s):  
Lydia Y. Liu ◽  
Vinayak Bhandari ◽  
Adriana Salcedo ◽  
Shadrielle M. G. Espiritu ◽  
Quaid D. Morris ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Cancer Cells ◽  
Clinical Outcomes ◽  
Cancer Cell ◽  
Genome Sequencing ◽  
Somatic Mutations ◽  
Mutation Detection ◽  
Cell Populations ◽  
Whole Genome ◽  
Prostate Cancers

AbstractWhole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluated sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

scholarly journals Whole-genome sequencing

2021 ◽  
pp. practneurol-2020-002561
Author(s):  
Huw R Morris ◽  
Henry Houlden ◽  
James Polke
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Genetic Disease ◽  
Large Scale ◽  
Clinical Care ◽  
Whole Genome ◽  
Routine Clinical Care ◽  
Human Genomes ◽  
Neurology Clinic

The costs of whole-genome sequencing have rapidly decreased, and it is being increasingly deployed in large-scale clinical research projects and introduced into routine clinical care. This will lead to rapid diagnoses for patients with genetic disease but also introduces uncertainty because of the diversity of human genomes and the potential difficulties in annotating new genetic variants for individual patients and families. Here we outline the steps in organising whole-genome sequencing for patients in the neurology clinic and emphasise that close liaison between the clinician and the laboratory is essential.

Utility of Whole Genome Sequencing in diagnosing complex disorders: lesson from renal tubular disorders

2018 ◽  
Author(s):  
Mark Stevenson ◽  
Alistair T Pagnamenta ◽  
Heather G Mack ◽  
Judith A Savige ◽  
Kate E Lines ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Complex Disorders ◽  
Tubular Disorders ◽  
Renal Tubular Disorders ◽  
Renal Tubular

0306 Exploring the feasibility of using copy number variants as genetic markers through large-scale whole genome sequencing experiments

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 146-146
Author(s):  
D. M. Bickhart ◽  
L. Xu ◽  
J. L. Hutchison ◽  
J. B. Cole ◽  
D. J. Null ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genetic Markers ◽  
Genome Sequencing ◽  
Copy Number ◽  
Large Scale ◽  
Copy Number Variants ◽  
Whole Genome

Evaluation of Whole Genome Sequencing for outbreak detection of Salmonella enterica serovar Enteritidis

2020 ◽  
Author(s):  
Ainhoa Arrieta-Gisasola ◽  
Aitor Atxaerandio Landa ◽  
Javier Garaizar ◽  
Joseba Bikandi ◽  
José Karkamo ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Salmonella Enterica ◽  
Outbreak Detection ◽  
Whole Genome ◽  
Salmonella Enterica Serovar Enteritidis
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