We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Improved Data Analysis with New “Omics” Software

Improved Data Analysis with New “Omics” Software content piece image
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

Qlucore, a company specializing in the development of bioinformatics software, has announced version 3.6 of its Qlucore Omics Explorer data analysis software.

Qlucore Omics Explorer (QOE) supports the user with fast, simple and visual analysis of measured data from a wide range of sources and instruments to maximize the output of the analysis. Results are presented in real-time with a visual update, aiming to make it easier for publishing and working in teams.

QOE ships in a base module with an option to add an NGS module with extensive functionality for NGS data analysis. The program integrates with workflows and through the Python-based template functionality, it is possible to control the program through a script and configure well-defined analysis steps.

QOE V3.6 sees the addition of several new features in different areas. The main improvements are the direct import of single-cell data and improved survival calculations using Hazard ratios, as well as the addition of one more classifier method (gradient boosted decision trees) in the Machine Learning module. This is especially important for precision medicine applications.

The Fusion Gene workbench, which incorporates a new Circle plot enables a completely new analysis category. The suite of standard Templates is expanded. It now includes Templates for direct import of single-cell data from 10x Genomics and one for direct data download from The Cancer Genome Atlas (TCGA). The data import Wizard supports import and normalization of count data. Support for direct import of data from the Salmon tool is also added. The Volcano plot has more options for cut-off lines, making it easier to generate publication-ready plots.

The Machine Learning support (build classifiers and use them to predict the outcome) has been improved both in terms of features with the addition of gradient boosted decision trees and in terms of performance and usability. The new Fusion Gene workbench (part of NGS module) supports filtering on detected gene fusions both based on quality parameters as well as based on existence in public databases. The program comes with the Mitelman as well as the Tumor fusion data base.