Qlucore Newsletter: Simplifying Tumor Classification with Qlucore: Unlock New Insights from Standard Kits and Workflows

Qlucore Insights automates the analysis of whole transcriptome sequencing data. This involves subclassifying tumors based on expression profile using machine learning techniques and detection of gene fusions. The software works with input data which can be generated using widely available standard kits and instruments. 

For tumor classification in pediatric leukemia, the process to generate data for Qlucore Insights is as follows (an example):

1. RNA extraction, High-molecular-weight RNA extracted from bone marrow or peripheral blood.
2. Sequence library preparation, Illumina TruSeq Stranded mRNA or Illumina stranded mRNA prep kit.
3. Sequencing, Paired 2x150 bp sequencing reads generated using an Illumina machine, such as NextSeq or NovaSeq, at a sequencing depth of roughly 10-15 million (M) read-pairs.
4. Bioinformatic pipeline, STAR, STAR-fusion, Arriba and Fusion catcher are all freely available open-source bioinformatics software. Detailed information about their setup is provided by Qlucore.

This data generation process provides aligned BAM files and fusion files that are used by Qlucore Insights for expression profile-based classification and detection of gene fusions.

Fast, accurate, and automated tumor classification is now within your reach, all with tools you already have in your lab.

Read more about the BCP-ALL model:

Acute Lymphoblastic Leukemia (BCP-ALL) model (RuO) | Qlucore