Qlucore Omics Explorer
Fast, Visual and Versatile software for instant analysis of Omics and NGS data.
Qlucore Omics Explorer is a D.I.Y next-generation bioinformatics software for research in life science, plant- and biotech industries, as well as academia. The powerful and flexible visualization-based data analysis tool with inbuilt powerful statistics delivers immediate results and provides instant exploration and visualization of big data.
The software is developed to allow the workflow which best suits you and your experiments and maximizes the outcome of your research.
Templates are Python based scripts that can be used to configure repetitive tasks as well as extending the functionality and integrating the program in tool chains.
For RNA-seq data is it possible to analyze expression levels in a synchronized model with filtering along the genome.
You decide the workflow
Visualization is central in Qlucore Omics Explorer.
By combining instant visualization with powerful statistics and flexible selection methods, you will be able to see your results immediately.
Instant exploration is one of the key features.
As a user, you decide your own workflow and starting point. You are in control and can tailor the exploration to meet your specific needs.
The combination of powerful statistics and instant visualization drives exciting results.
Flexibility and expansion are cornerstones. Analyze data using an easy to use statistical model. Integrate and extend the analysis with Python based Templates for scripting. Add statistical methods through the Open API to R. Filter on genomic entities such as read coverage and variants with the NGS module. Use the GSEA workbench, the kmeans++ clustering or generate machine learning based classifiers.
You can share your results in a number of different ways.
The unique global log and restore function not only lets you keep track of what you have done, but also allows you to store information of each analysis step you have performed.
Easy data import
A wide selection of file formats and data types are supported. Import of data and clinical information can be done in several ways, with or without normalization.
The value of using Qlucore Omics Explorer
RNA-Seq analysis using Qlucore
Performing gene expression analysis based on RNA sequencing data, in Dilated Cardiomyopathy studies.
Stanford University, USRead more
Analysis of proteomics data using Qlucore
Using proteomics to understand cardiovascular disease
The Center for Interdisciplinary Cardiovascular Sciences, Boston, USRead more
Analyzing proteomics and transcriptomics data
Study of hundreds of entries about pollutants and nutrients in different fish species, showing how levels are changing over time.
National Institute of Nutrition and Seafood Research (NIFES), NorwayRead more
Analysis of public data using Qlucore
This case study is an example of how the use of public information from multiple sources was used to propose a new classification for glioma cancer.
Beijing Normal University, ChinaRead more