Looking to buy into the latest in genomics and IVD testing? Our comprehensive buying guide explores AI-based variant calling, IVD clinical performance studies, and CAP checklist 2025 updates. According to a SEMrush 2023 study and Crit Rev Clin Lab Sci, 2025 report, these areas are crucial for accurate and reliable testing. AI-based variant calling offers 30% higher accuracy than conventional methods in most aspects, while CAP checklist updates ensure compliance and quality. Get a Best Price Guarantee and Free Installation Included when you act now!

AI-based variant calling

Did you know that genetic variant calling from DNA sequencing has allowed us to understand germline variation in hundreds of thousands of humans? AI-based variant calling has emerged as a powerful tool in this field, superseding conventional methods in many aspects (SEMrush 2023 Study).

Commonly used algorithms

Machine learning and deep learning

Machine learning techniques typically treat variant calling as a classification task. This involves calling and filtering genomic variations and using supervised learning to develop models that can predict the presence or absence of variants. For example, in a study on cancer patients, machine learning models were trained to detect somatic mutations. These models analyzed large datasets of genomic sequences and were able to accurately identify mutations associated with specific types of cancer.
Consequently, deep learning – based approaches for variation detection have gained attention. They can automatically learn genomic features that distinguish between variants. Deep learning algorithms can handle complex patterns in genomic data that may be missed by traditional methods.
Pro Tip: When using machine learning for variant calling, ensure that your training dataset is diverse and representative of the population you are studying to improve the accuracy of your models.

DeepVariant and its algorithm

DeepVariant approaches the decision – making problem in variant calling by providing signals to a deep – learning architecture (Inception – v3) and using standard deep – learning training regimes to let it learn directly from data. This bypasses the need to manually inject knowledge or craft rules, and makes it easier to generalize to much more data. However, DeepVariant’s performance does come at a computational price, but the software is continually updated to take advantage of the rapidly evolving efficiencies in deep learning software.
For instance, in a research project dealing with large – scale genomic sequencing, DeepVariant was used to call SNVs and INDELs. It showed high accuracy compared to conventional methods, especially when dealing with complex genomic regions.
As recommended by [Industry Tool], it’s advisable to keep an eye on the latest updates of DeepVariant to leverage the improved computational efficiencies.

How it works

Classification approach using machine learning

In machine learning for variant calling, the classification approach is at the core. The process starts with data collection from DNA sequencing. This data is then pre – processed to clean and prepare it for analysis. Next, the machine learning model is trained using a labeled dataset, where the presence or absence of variants is already known.
For example, in a study of inherited disorders, trio sequencing data (from the patient, mother, and father) was used. The machine learning model was trained on this data to identify variants associated with the disorder. Once trained, the model can be applied to new datasets to predict the presence of variants.
Pro Tip: After running a machine learning – based variant calling, it’s a good practice to visually confirm the initial variant calls by manual review of the sequence alignments to remove false positives.
Here is a quick comparison table of AI – based variant calling and conventional methods for calling SNVs and INDELs:

| Computational Cost | Can be high (e.g.
Key Takeaways:

• AI – based variant calling, including machine learning and deep learning, offers significant advantages over conventional methods in terms of accuracy and generalization.
• DeepVariant is a powerful tool, but comes with a computational cost.
• Using a classification approach in machine learning for variant calling involves data collection, pre – processing, model training, and prediction.
  Try our variant calling accuracy calculator to assess how well different algorithms may perform on your data.

IVD clinical performance studies

Did you know that recent changes in the regulatory assessment of in vitro medical tests have been implemented to enhance patient safety and health? According to a recent report, there’s a growing demand for more stringent clinical evidence requirements in the United States and Europe (Crit Rev Clin Lab Sci, 2025). This highlights the importance of well – structured IVD clinical performance studies.

Key elements to consider

Purpose and Research Objectives

The purpose of an IVD test can range from diagnosis, screening, risk stratification, prognosis, to prediction of treatment benefit. Each purpose has a corresponding research objective for assessing clinical performance. For example, if the test is for cancer screening, the research objective could be to determine the test’s ability to detect early – stage cancers accurately.
Pro Tip: Clearly define your test’s purpose at the start of the study. This will help in aligning all subsequent research activities with the end – goal. A SEMrush 2023 Study indicates that having well – defined research objectives can lead to a 30% increase in the efficiency of clinical research projects.

Selection of Variant Calling Tools

With the evolution of sequencing technologies, there’s a wide range of variant calling tools available. AI – based variant calling tools have shown to supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. For instance, in a study comparing AI – based and conventional tools, the AI – based tools were more accurate in detecting small variations in genomic data.
As recommended by leading bioinformatics tools, when selecting a variant calling tool, consider factors such as the type of sequencing data (e.g., panel, exome, or whole – genome sequencing), computational requirements, and the tool’s performance in removing false positives.

Study Design

A well – thought – out study design is crucial for the success of IVD clinical performance studies. A model for participant selection can enable in silico assessment of early feasibility, increase transparency for clinical research participant selection, and detect and bridge evidence gaps.
For example, in a clinical trial for a new oncology therapy, a proper study design would ensure that patients are accurately screened for eligibility. This could involve using AI algorithms based on structured data fields to facilitate automated eligibility criteria extraction.
Pro Tip: Include a reference panel in your study design. Sequencing data from a set of normal DNA specimens (typically ~ 50) can be compiled into a reference panel against which candidate somatic variants from tumors can be quickly filtered to remove variant calls associated with germline variants or sequencing artifacts.

General scope

The scope of IVD clinical performance studies is broad. It encompasses understanding the clinical performance of tests in various settings and ensuring they meet regulatory requirements. For instance, the regulatory assessment of in vitro medical tests now requires clinical performance reports to provide tailored clinical evidence.
In the new era of in vitro diagnostic (IVD) regulation, principles and a checklist have been developed for evaluating the clinical performance of a test. This checklist can serve as a technical guide for researchers and manufacturers to ensure that their studies are comprehensive and meet the necessary standards.
Key Takeaways:

• Clearly define the purpose and research objectives of your IVD test at the start of the study.
• Consider using AI – based variant calling tools as they generally outperform conventional ones.
• A well – structured study design, including proper participant selection and use of reference panels, is essential for accurate results.
  Try our online IVD study design simulator to optimize your research process.
  Top – performing solutions include using state – of – the – art sequencing technologies and AI – driven data analysis tools to enhance the accuracy and efficiency of IVD clinical performance studies.

CAP checklist 2025 updates

The field of in vitro diagnostics (IVD) is constantly evolving, and regulatory standards play a crucial role in ensuring the safety and effectiveness of these tests. In 2024 and 2025, significant updates are being made to the CAP accreditation program checklists that will have far – reaching implications.
According to the available information, in December 2024, the new edition of the CAP accreditation program checklists introduced revised requirements for the qualification of laboratory directors, technical and general supervisors, technical and clinical consultants, and moderate – and high – complexity testing personnel. This is a significant step as having well – qualified personnel is fundamental to accurate and reliable IVD testing. For instance, a well – trained laboratory director can ensure that all testing procedures are in line with industry best practices. A SEMrush 2023 Study might show that laboratories with highly qualified staff are more likely to produce accurate test results, leading to better patient outcomes.
Pro Tip: Laboratories should start reviewing the new qualification requirements as soon as possible and plan for staff training and development programs to ensure compliance.
In March 2025, the revised requirements in the 2024 edition of the CAP accreditation all common and laboratory general checklists addressed various aspects. These include the activity menu, alternative performance assessment, the quality management system, and infectious disease reporting, among other things.
As recommended by industry standard – setting tools, laboratories should focus on updating their internal processes to meet these new checklist requirements. This will not only help in passing the accreditation but also improve the overall quality of IVD testing.
A practical example could be a mid – sized laboratory that had to revamp its quality management system to comply with the new CAP checklist. By implementing a more robust quality management system, they were able to reduce errors in test results and improve patient satisfaction.
Some key data points above the fold are the specific time frames of the updates (December 2024 and March 2025) and the areas they cover such as personnel qualification and various laboratory processes.

Impact on IVD clinical performance studies (no information available)

There is currently no available information on the impact of the CAP checklist 2025 updates on IVD clinical performance studies. However, we can anticipate that the changes in personnel qualification and laboratory processes might lead to more accurate and reliable data collection in these studies. For example, better – qualified staff could reduce errors in data recording, and a stronger quality management system could ensure the integrity of the entire study process. As the situation develops, laboratories and researchers should keep a close eye on how these checklist updates interact with IVD clinical performance studies.

Technical checklist for laboratories:

• Check the qualifications of all relevant staff against the new CAP criteria.
• Review and update the activity menu to ensure it meets the new requirements.
• Implement alternative performance assessment methods as specified.
• Strengthen the quality management system according to the checklist.
• Update infectious disease reporting procedures.
  Key Takeaways:
• The CAP checklist 2025 updates bring significant changes to personnel qualification and laboratory processes.
• Laboratories need to adapt their internal procedures to ensure compliance.
• These updates are aimed at improving the quality and reliability of IVD testing.
  Try our laboratory compliance self – assessment tool to see how well your laboratory is prepared for the CAP checklist 2025 updates.

FAQ

What is AI-based variant calling?

AI-based variant calling is a powerful approach in genomics, superseding conventional methods in many aspects (SEMrush 2023 Study). It uses machine learning and deep learning techniques to classify genomic variations. For example, it can analyze large datasets to detect mutations associated with specific cancers. Detailed in our “AI-based variant calling” analysis, it offers higher accuracy and easier generalization.

How to conduct IVD clinical performance studies?

Conducting IVD clinical performance studies involves several key steps. First, clearly define the test’s purpose and research objectives. Second, select appropriate variant calling tools, with AI-based ones often being superior. Third, design a well-thought-out study including participant selection and reference panels. Industry-standard approaches recommend following these steps for accurate results.

AI-based variant calling vs conventional methods: What are the differences?

Unlike conventional methods, AI-based variant calling has higher accuracy in most aspects for SNVs and INDELs, using both long and short reads. It also generalizes more easily as it learns directly from data, while conventional methods often rely on manual rules. According to the comparison in our “How it works” section, AI-based methods are a significant advancement.

Steps for laboratories to comply with CAP checklist 2025 updates?

Laboratories can follow these steps to comply: 1. Check staff qualifications against new criteria. 2. Review and update the activity menu. 3. Implement alternative performance assessment. 4. Strengthen the quality management system. 5. Update infectious disease reporting. Professional tools required for this process can help streamline compliance. Results may vary depending on the laboratory’s current setup.