scholarly journals Sex and the CTAG: what ancient DNA tells us about our ancestors' liaisons

2020 ◽  
Vol 42 (1) ◽  
pp. 24-27
Author(s):  
Christiana Scheib
Keyword(s):  
Next Generation Sequencing ◽  
Ancient Dna ◽  
Copy Number ◽  
Rapid Expansion ◽  
Brick Wall ◽  
The Public ◽  
Low Copy Number Dna ◽  
Low Copy Number ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

The development of next-generation sequencing (NGS), a technology coincidentally well-suited to highly fragmented, low copy number DNA sources, spawned a rapid expansion in the field of ancient DNA (aDNA). It has gathered a reputation as a sexy subject, quite literally. Some of the headlines targeted to the public: ‘Mystery humans spiced up ancients' sex lives’ in Nature News or ‘Viking sex tourists lived happily ever after with Britons’ from The Independent, would make any scientist blush and probably want to bang their head against a brick wall. As problematic as these headlines are, people keep writing them because sex sells and while aDNA might not tell us exactly what our ancestors were into, it has and will continue to provide other unique insights regarding our reproductive past.

scholarly journals Copy number alterations in cancer: detection and precision medicine

2021 ◽  
Vol 41 ◽  
pp. 02005
Author(s):  
Arief Gusnanto
Keyword(s):  
Next Generation Sequencing ◽  
Precision Medicine ◽  
Cancer Patients ◽  
Cancer Detection ◽  
Copy Number ◽  
Copy Number Alterations ◽  
Genomic Alterations ◽  
Genomic Markers ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Copy number alterations (CNAs) are genomic alterations where some regions exhibit more or less copy number than the normal two copies. In this talk, I will describe two ideas: (1) how CNAs are estimated from data generated by next generation sequencing (NGS) and what steps are required to make the data interpretable, (2) how the CNA can be utilised for precision medicine in terms of prediction of tumour subtypes and prediction of cancer patients’ survival. If time permits, I will also discuss how to estimate genomic markers from CNA profile across cancer patients.

scholarly journals Analysis of Copy Number Variations in Solid Tumors Using a Next Generation Sequencing Custom Panel

2021 ◽  
Vol 2 (2) ◽  
pp. 123-134
Author(s):  
Marta Vives-Usano ◽  
Beatriz García Pelaez ◽  
Ruth Román Lladó ◽  
Mónica Garzón Ibañez ◽  
Erika Aldeguer ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation ◽  
Ffpe Samples ◽  
Custom Panel ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing ◽  
Approach Time ◽  
Selection Of

Somatic copy number variations (CNV; i.e., amplifications and deletions) have been implicated in the origin and development of multiple cancers and some of these aberrations are designated targets for therapies. Although FISH is still considered the gold standard for CNV detection, the increasing number of potentially druggable amplifications to be assessed makes a gene-by-gene approach time- and tissue-consuming. Here we investigated the potential of next generation sequencing (NGS) custom panels to simultaneously determine CNVs across FFPE solid tumor samples. DNA was purified from cell lines and FFPE samples and analyzed by NGS sequencing using a 20-gene custom panel in the GeneReader Platform®. CNVs were identified using an in-house algorithm based on the UMI read coverage. Retrospective validation of in-house algorithm to identify CNVs showed 97.1% concordance rate with the NGS custom panel. The prospective analysis was performed in a cohort of 243 FFPE samples from patients arriving at our hospital, which included 74 NSCLC tumors, 148 CRC tumors, and 21 other tumors. Of them, 33% presented CNVs by NGS and in 14 cases (5.9%) the CNV was the only alteration detected. We have identified CNV alterations in about one-third of our cohort, including FGFR1, CDK6, CDK4, EGFR, MET, ERBB2, BRAF, or KRAS. Our work highlights the need to include CNV testing as a part of routine NGS analysis in order to uncover clinically relevant gene amplifications that can guide the selection of therapies.

scholarly journals Next-Generation Sequencing (NGS) Methods Show Superior or Equivalent Performance to Non-NGS Methods on BRAF, EGFR, and KRAS Proficiency Testing Samples

2019 ◽  
Vol 143 (8) ◽  
pp. 980-984 ◽  
Author(s):  
Lea F. Surrey ◽  
Fredrick D. Oakley ◽  
Jason D. Merker ◽  
Thomas A. Long ◽  
Patricia Vasalos ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Proficiency Testing ◽  
Rapid Expansion ◽  
Next Generation ◽  
Limited Data ◽  
Comparative Performance ◽  
Panel Surveys ◽  
Molecular Oncology ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Context.— There has been a rapid expansion of next-generation sequencing (NGS)–based assays for the detection of somatic variants in solid tumors. However, limited data are available regarding the comparative performance of NGS and non-NGS assays using standardized samples across a large number of laboratories. Objective.— To compare the performance of NGS and non-NGS assays using well-characterized proficiency testing samples provided by the College of American Pathologists (CAP) Molecular Oncology Committee. A secondary goal was to compare the use of preanalytic and postanalytic practices. Design.— A total of 17 343 responses were obtained from participants in the BRAF, EGFR, KRAS, and the Multigene Tumor Panel surveys across 84 different proficiency testing samples interrogating 16 variants and 3 wild-type sequences. Performance and preanalytic/postanalytic practices were analyzed by method. Results.— While both NGS and non-NGS achieved an acceptable response rate of greater than 95%, the overall performance of NGS methods was significantly better than that of non-NGS methods for the identification of variants in BRAF (overall 97.8% versus 95.6% acceptable responses, P = .001) and EGFR (overall 98.5% versus 97.3%, P = .01) and was similar for KRAS (overall 98.8% and 97.6%, P = .10). There were specific variant differences, but in all discrepant cases, NGS methods outperformed non-NGS methods. NGS laboratories also more consistently used preanalytic and postanalytic practices suggested by the CAP checklist requirements than non-NGS laboratories. Conclusions.— The overall analytic performance of both methods was excellent. For specific BRAF and EGFR variants, NGS outperformed non-NGS methods and NGS laboratories report superior adherence to suggested laboratory practices.

scholarly journals Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies

2015 ◽  
Vol 9 ◽  
pp. BBI.S12462 ◽  
Author(s):  
Anastasis Oulas ◽  
Christina Pavloudi ◽  
Paraskevi Polymenakou ◽  
Georgios A. Pavlopoulos ◽  
Nikolas Papanikolaou ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Rapid Expansion ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Multiple Datasets ◽  
Dataset Analysis ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.

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