Qlucore Insights

Disease subclassification analysis for frontline research

Advances in precision medicine will require powerful yet accessible research tools and methods that unite common goals with collaborative effort. Qlucore Insights is a powerful and flexible architecture that enables disease-specific model development for subtype classification from one platform. Researchers can use it to build and evaluate new models, or as the starting point for companion diagnostics. And frontline labs now have a powerful yet easy-to-use tool for transforming clinical data into new insights, and new RNA-seq, transcriptomic-based or molecular-based research.

Rigshospitalet Case Study

The team at Rigshospitalet, Copenhagen, Denmark has implemented the use of Qlucore Insights for in-house use, in accordance with Article 5(5) in EU IVDR, and included it in the RNA lab workflow. As data input whole RNA sequencing data (WTS) from diagnostic ALL samples are used.

PCA plot

RNA-seq analysis software for better insights

Qlucore Insights enables disease specific subtype classification  and first class gene fusion analysis support of  individual samples.

Qlucore Insights is an easy to use software that takes RNA-seq data as input and generate plots and a report based on gene expression based classification and gene fusion based filters. Qlucore Insights is platform and the user can select to use different so called models. The models plug into the Qlucore Insight platform and create one unified solution for the lab to manage a range of tumor types.  The wet lab workflow is similar in all cases and based on Illumina RNA-seq kits, and the bioinformatic pipeline uses state of the art open-source tools. 

Models

There are currently four models using both gene expression subtyping and gene fusions available. The are targetted at Leukemia (BCP-ALL and AML), Lung cancer and Bladder cancer. Additionally there are two dedicated gene fusion models.

Qlucore Insights

Visual reporting

The subtype classification is complemented with flexible visualizations which put the sample in context with reference data by using known cases of the disease. Complementing the reports with easy-to-interpret visualizations improves the interpretation and communication.

One sample prediction

How it works

The program includes AI powered subtype classification of individual samples, with flexible visualization and comprehensive reporting. Additionally the user-friendly interface for exploration of detected gene fusions and database integration provides a future proof solution adapting to new medical findings.

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