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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Related Experiment Video

Updated: Apr 3, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Integrating AI into clinical practice: Human-centered design requirements for next-generation sequencing workflows.

Markus Plass1, Andreas Holzinger2, Robert Reihs1

  • 1Machine Learning and Data Science Group, Diagnostic and Research Institute of Pathology, Medical University Graz, Austria.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|April 1, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible AI framework to integrate artificial intelligence (AI) with next-generation sequencing (NGS) clinical workflows. It aims to improve usability and decision support in genetic diagnostics and beyond.

Keywords:
AI usability engineeringClinical genomicsDecision supportDigital transformationFHIRNGSStakeholder workflows

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Area of Science:

  • Genomics
  • Bioinformatics
  • Clinical Informatics

Background:

  • Next-generation sequencing (NGS) is crucial for clinical genomics, but its integration is challenged by fragmented workflows and usability issues.
  • Artificial intelligence (AI) is increasingly driving NGS analysis, from data processing to clinical decision support.

Purpose of the Study:

  • To present a design-oriented framework (DUXU) for embedding AI-powered NGS workflows into clinical decision support systems (CDSS).
  • To address the socio-technical demands of clinical genomics and propose actionable design requirements for AI-based systems.

Main Methods:

  • Development of a conceptual and methodological framework (DUXU) focused on Design, User eXperience, and Usability.
  • Grounded in real-world clinical environments and aligned with standards like FHIR and GA4GH.
  • Focus on AI's role in multimodal data interpretation, patient-specific visualization, and explainable decision-making.

Main Results:

  • The DUXU framework offers a flexible approach adaptable to specific clinical use cases, including genetic screening, tumor testing, and potential pathogen detection.
  • Highlights the central role of AI in creating trustworthy, interpretable, and operationally embedded AI-based NGS systems.
  • Proposes actionable design requirements for interoperable, role-specific interfaces.

Conclusions:

  • The DUXU framework facilitates the integration of AI-driven NGS into clinical practice by addressing usability and workflow challenges.
  • Advances the development of explainable AI for clinical genomics, enhancing diagnostic and treatment strategies.
  • Future work will extend the framework to diverse applications like pathogen detection and antimicrobial stewardship.