DNA sequencing continues to evolve quickly even after > 30 years. Many new platforms suddenly appeared and former established systems have vanished in almost the same manner. Since establishment of next-generation sequencing devices, this progress gains momentum due to the continually growing demand for higher throughput, lower costs and better quality of data. In consequence of this rapid development, standardized procedures and data formats as well as comprehensive quality management considerations are still scarce. Here, we listed and summarized current standardization efforts and quality management initiatives from companies, organizations and societies in form of published studies and ongoing projects. These comprise on the one hand quality documentation issues like technical notes, accreditation checklists and guidelines for validation of sequencing workflows. On the other hand, general standard proposals and quality metrics are developed and applied to the sequencing workflow steps with the main focus on upstream processes. Finally, certain standard developments for downstream pipeline data handling, processing and storage are discussed in brief. These standardization approaches represent a first basis for continuing work in order to prospectively implement next-generation sequencing in important areas such as clinical diagnostics, where reliable results and fast processing is crucial. Additionally, these efforts will exert a decisive influence on traceability and reproducibility of sequence data.
Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, highquality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.
Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.
Introduction 2nd generation sequencing or better known as next-generation sequencing (NGS) represents a cutting-edge technology in life sciences and current foundation for unravelling nucleotide sequences. Since advent of first platforms in 2005 the number of different types of NGS platforms increased in the last 10 years in the same manner as the variety of possible applications. Higher throughput, lower cost and better quality of data were the incentive for a range of enterprises developing new NGS devices, whereas economic issues and competitive pressure, based on expensive workflows of obsolete systems and decreasing cost of market leader platforms, resulted simultaneously in accelerated vanishing of several companies. Due to the fast development, NGS is currently characterized by a lack of standard operating procedures, quality management/quality assurance specifications, proficiency testing systems and even less approved standards along with high cost and uncertainty of data quality. On the one hand, appropriate standardization approaches were already performed by different initiatives and projects in the format of accreditation checklists, technical notes and guidelines for validation of NGS workflows. On the other hand, these approaches are exclusively located in the US due to origins of NGS overseas, therefore there exists an obvious lack of European-based standardization initiatives. An additional problem represents the validity of promising standards across different NGS applications. Due to highest demands and regulations in specific areas like clinical diagnostics, the same standards, which will be established there, will not be applicable or reasonable in other applications. These points emphasize the importance of standardization in NGS mainly addressing the laboratory workflows, which are the prerequisite and foundation for sufficient quality of downstream results. Methods This work was based on a platform-dependent and -independent systematic literature review as well as personal communications with i.a. Illumina, Inc., ISO/TC 276 as well as DIN NA 057-06-02 AA 'Biotechnology'. Results Prior formulation of specific standard proposals and collection of current de facto standards, the problems of standardization in NGS itself were identified and interpreted. Therefore, a variety of different standardization approaches and projects from organizations, societies and companies were reviewed. Conclusions There is already a distinct number of NGS standardization efforts present; however, the majority of approaches target the standardization of the bioinformatics processing pipeline in the context of “Big Data”. Therefore, an essential prerequisite is the simplification and standardization of wet laboratory workflows, because respective steps are directly affecting the final data quality and thus there exists the demand to formulate experimental procedures to ensure a sufficient final data output quality.
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