Developing Genome and Exome Sequencing for Candidate Gene Identification in Inherited Disorders: An Integrated Technical and Bioinformatics Approach

Context.—Advances in sequencing technology with the commercialization of next-generation sequencing (NGS) has substantially increased the feasibility of sequencing human genomes and exomes. Next-generation sequencing has been successfully applied to the discovery of disease-causing genes in rare, inherited disorders. By necessity, the advent of NGS has fostered the concurrent development of bioinformatics approaches to expeditiously analyze the large data sets generated. Next-generation sequencing has been used for important discoveries in the research setting and is now being implemented into the clinical diagnostic arena. Objective.—To review the current literature on technical and bioinformatics approaches for exome and genome sequencing and highlight examples of successful disease gene discovery in inherited disorders. To discuss the challenges for implementing NGS in the clinical research and diagnostic arenas. Data Sources.—Literature review and authors' experience. Conclusions.—Next-generation sequencing approaches are powerful and require an investment in infrastructure and personnel expertise for effective use; however, the potential for improvement of patient care through faster and more accurate molecular diagnoses is high.

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k-mer-Based Metagenomics Tools Provide a Fast and Sensitive Approach for the Detection of Viral Contaminants in Biopharmaceutical and Vaccine Manufacturing Applications Using Next-Generation Sequencing

mSphere ◽

10.1128/msphere.01336-20 ◽

2021 ◽

Vol 6 (2) ◽

Author(s):

Madolyn L. MacDonald ◽

Shawn W. Polson ◽

Kelvin H. Lee

Keyword(s):

Next Generation Sequencing ◽

Sensitivity And Specificity ◽

Data Sets ◽

Next Generation ◽

Viral Detection ◽

Read Alignment ◽

Ensure Patient Safety ◽

Hela Cell Lines ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

ABSTRACT Adventitious agent detection during the production of vaccines and biotechnology-based medicines is of critical importance to ensure the final product is free from any possible viral contamination. Increasing the speed and accuracy of viral detection is beneficial as a means to accelerate development timelines and to ensure patient safety. Here, several rapid viral metagenomics approaches were tested on simulated next-generation sequencing (NGS) data sets and existing data sets from virus spike-in studies done in CHO-K1 and HeLa cell lines. It was observed that these rapid methods had comparable sensitivity to full-read alignment methods used for NGS viral detection for these data sets, but their specificity could be improved. A method that first filters host reads using KrakenUniq and then selects the virus classification tool based on the number of remaining reads is suggested as the preferred approach among those tested to detect nonlatent and nonendogenous viruses. Such an approach shows reasonable sensitivity and specificity for the data sets examined and requires less time and memory as full-read alignment methods. IMPORTANCE Next-generation sequencing (NGS) has been proposed as a complementary method to detect adventitious viruses in the production of biotherapeutics and vaccines to current in vivo and in vitro methods. Before NGS can be established in industry as a main viral detection technology, further investigation into the various aspects of bioinformatics analyses required to identify and classify viral NGS reads is needed. In this study, the ability of rapid metagenomics tools to detect viruses in biopharmaceutical relevant samples is tested and compared to recommend an efficient approach. The results showed that KrakenUniq can quickly and accurately filter host sequences and classify viral reads and had comparable sensitivity and specificity to slower full read alignment approaches, such as BLASTn, for the data sets examined.

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Review of Clinical Next-Generation Sequencing

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2016-0501-ra ◽

2017 ◽

Vol 141 (11) ◽

pp. 1544-1557 ◽

Cited By ~ 87

Author(s):

Sophia Yohe ◽

Bharat Thyagarajan

Keyword(s):

Next Generation Sequencing ◽

Clinical Care ◽

Cost Effective ◽

Next Generation ◽

Inherited Disorders ◽

Academic Center ◽

Next Generation Sequencing Ngs ◽

Ngs Data ◽

Generation Sequencing

Context.— Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS. Objective.— To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care. Data Sources.— The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center. Conclusions.— The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.

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Next Generation Sequencing Technology in the Clinic and Its Challenges

Cancers ◽

10.3390/cancers13081751 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1751

Author(s):

Lau K. Vestergaard ◽

Douglas N. P. Oliveira ◽

Claus K. Høgdall ◽

Estrid V. Høgdall

Keyword(s):

Next Generation Sequencing ◽

Large Data ◽

Data Sets ◽

Next Generation ◽

Biological Drugs ◽

Next Generation Sequencing Technology ◽

Bioinformatic Tools ◽

Sequencing Technologies ◽

Genome Complexity ◽

Generation Sequencing

Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.

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