The Qlucore Omics Explorer base module includes all key features used by scientists on a regular basis for multi-omics data (including RNA-seq expression data). The NGS module adds a wide range of tools and methods for Genome browser centric analysis.
At Qlucore we spend significant time and effort on testing and evaluating new functionality before adding it. These carefully thought out features make the program very easy to use. Feedback from users shows that using Qlucore Omics Explorer adds value in many ways and two examples are:
- by improving the speed of analysis by up to 50%
- by stimulating creativity to explore new ideas and to discover new findings
Figure: The power of PCA plots, the left is showing sample clusters in a sample PCA plot and the plot to the right is a variable PCA plot showing the variable clusters.
Because Qlucore Omics Explorer is so easy to use, you don't need to be an expert in analysis to use it. The current user base spans a broad range of professions, from bioinformaticians, biologists, statisticians and chemists to medical doctors, students and professors.
With the Template functionality you can use Python based scripts to faster get started and to simplify repetitive tasks. Write your own templates or use the inbuilt ones.
The program is compatible with a very wide range of Omics data, for example:
- mRNA expression
- Single cell RNA-seq
- DNA methylation
- Flow cytometry
Qlucore Omics Explorer 3.7 feature overview
Qlucore Omics Explorer (QOE) and the NGS module are developed to support the user with fast, simple and visual analysis of measured data considering publicly available information such as gene ontologies, pathways and other system biology information to maximize the output of the analysis. You reach all key functionalities with one or two mouse clicks and the results of your actions are always presented to you in real time by a visual update. The visual approach makes it easy to publish results as well as working in teams.
The software ships in a base module with an option to add a NGS module with extensive functionality for NGS data analysis. Detailed information about the NGS module features is presented in the NGS Module feature overview document.
QOE is tailored for creative analysis with a focus on instant results and effective visualizations.
QOE is tailored for creative analysis with a focus on instant results and effective visualizations.
- QOE works in full real time with both 2D and 3D presentations of all data. All plots are truly interactive, this includes the Genome Browser (NGS module) and PCA plots. The user is encouraged to explore the data by changing filters and parameters dynamically.
- QOE uniquely combines powerful statistical analysis with instant visualization. Most actions are controlled with only one mouse click.
- The NGS module is centered around a dynamic Genome Browser which content is updated based on the filter settings. Filter on entities such as read coverage, Short indels presence or quality and genomic parameters of choice. Select and deselect samples to work with.
- QOE provides simple workflows for mRNA, miRNA data, protein, metabolomics and DNA Methylated data with direct import and normalization of Agilent, Thermo Fisher (Affymetrix) data and aligned BAM files for RNA-seq data. The NGS module supports a wide range of options for NGS data including Fusion gene analysis, ChIP-seq and ATAC-seq data. The powerful statistical framework supports a wide range of tests including, but not limited to, t-tests, ANOVA, paired tests, Two-way ANOVA, linear/quadratic/rank regression. The tests can be further customized using eliminated factors and restrictions.
- The integrated Biomarker Workbench is optimized for experiments and studies in the areas of drug development and biomarker discovery.
- Survival analysis with Kaplan-Meier plots and Cox proportional hazards model.
- Scripting work-flows is easy with Python based Templates. Extending functionality and integrating the program in tool chains can be achieved with the Templates.
- Analysis of single cell RNA-seq data is supported by features such as subsampling and t-SNE plots.
- With the NGS module the program offers a unique dual capability for RNA-seq data analysis. Work and filter with synchronized views, expression and genomic. Including Gene fusion Workbench and Circle plots.
- The integrated Gene Set Enrichment Analysis (GSEA) workbench allows a straightforward analysis of the biological context (pathways, ontology categories or any other relevant set of genes).
- Classifiers can be constructed using any of the following methods: Boosted trees, Support Vector Machines, Random Forest and kNN.
- QOE includes an open interface to R. Qlucore also provides free scripts for easy conversion to and from R data files.
- QOE support hierarchical clustering, K-means clustering, heatmaps with dendograms and Dynamic Principal Component Analysis (PCA).
- A direct link to Gene Expression Omnibus (GEO) enables one button data downloads and easy comparison of findings with published material and with the GO Browser you can quickly search in ontologies.
- A Template tailored for TCGA data analysis is included.
- QOE only requires a normal computer to handle huge data sets (more than 100 million entries).
- Analyze and explore data set by a combination of visualizations and intuitive filters.
- Do statistical analysis using a wide range of built in tests, such as ANOVA, as well as through the open interface to R. Generate results with false discovery rates(q-value), fold change and p-values.
- Use the Biomarker workbench to run many statistical tests in batch mode.
- Import data and clinical annotations with easy to use Wizards.
- Pre-filter to remove unwanted variables.
Figure: Left: Heatmap with hierarchical clustering Right: PCA sample plot
- Perform hierarchical clustering and generate dynamic heatmap plots.
- Analyze RNA-seq data both in the Genome browser and a PCA plot in a synchronized view. Analyze single cell RNA-seq data using t-SNE plots.
- Fine tune and generate results using any combination of scatter plots, box plots, venn, volcano and line plots.
- Instantly create Principal Component Analysis PCA plots of large data sets and confirm the information content by using the Qlucore unique functionality Projection Score.
- Estimate dimensionality with Talus and Scree plots.
- Use unsupervised clustering based on K-means++ clustering and silhouette values.
- Use any of several methods, Hierarchical clustering, PCA, clustering, ISOMAP and or graphs for visual data exploration.
- Build classifiers and classify new samples.
- Do functional analysis in the context of public available gene sets such as pathways and so on using GSEA.
- Download data from Gene Expression Omnibus (GEO) to compare your own results with published material.
- Remove unwanted dependencies such as artifacts and outliers. Manage batch effects.
- Benefit from the streamlined workflows for ThermoFisher (Affymetrix) gene expression microarray and Agilent miRNA and mRNA data as well as direct import of aligned BAM files with RNA-seq data for digital gene expression analysis.
- Execute and use templates for faster and easier start, repetitive tasks och integration with tool chains. Keep track of your work with powerful global log and restore function.
Qlucore Omics Explorer NGS Module feature overview
The Next Generation Sequencing (NGS) Module is an add-on module that will enable additional functionality related to analysis of data generated with NGS technologies and will make it possible to interactively and dynamically analyze and explore NGS data both from DNA and RNA.
All functionalities will be provided as integrated parts of Qlucore Omics Explorer and work with the well-known functionality.
The main components of the NGS Module are:
- An interactive and fast genome browser for many samples and many tracks per sample
- A genome filter control component
- A project manager for project set-up and definition of which files to include
- A built in variant caller for short indels and variants
- A Gene fusions workbench with Circle plot visualizations
Examples on supported analysis
- Variant detection and variant analysis
- RNA-seq expression data analysis
- Synchronized analysis of the genomic as well as expression levels
- Peak analysis (ChIPseq and ATAC seq)
- Singe cell RNA-seq
- Fusion gene analysis
The NGS module is unique in providing true interactive and dynamic analysis of NGS data. The Genome Browser content is
dynamically updated when filters and filter cut-off are changed, for instance using sliders and check-boxes.
Workflow and data storage
One unique feature of the NGS Module is the two-stage work flow. The first stage is a pre-processing step that prepares data for the fast and interactive analysis in stage two.
The project files and associated data for indexing samples is stored locally on the user’s computer to secure fast access and interactivity while files such as large BAM files can be stored anywhere on a network.
The options available for RNA-seq analysis are impressive. Utilizing the existing functionality in QOE for expression data and combining it with the new NGS functionality enables significantly increased analysis options. When a RNA-seq project is initiated, both quantitative normalized expression levels will be calculated at the same time as data is prepared for the genome browser. The program is built to seamlessly handle information both from a quantitative and genomic view. As an example the user can define discriminating genes with a t-test, visualize them in a heat map and study the genes in the genome browser. If a sample group is selected or de-selected all plots will be updated and the analysis can directly continue.
The peak analysis support in Qlucore Omics Explorer allows for comprehensive analysis of peak data such as ChIP-seq and ATAC-seq. The main components of the peak analysis processing are peak detection, consensus peaks and count matrix generation. The genome browser includes powerful filtering options for peak analysis, making it easy to find interesting features. The browser allows the user to annotate peaks and export the results as a bed file. If the experiment also includes RNA-seq data, it is possible to generate a count matrix for RNA-seq and swap between the RNA-seq and ChIP-seq count matrices during the analysis.
Read more about Next Generation Sequencing.
Watch our video tutorials to learn more
You decide the workflow
Publications ready plots
Dynamic PCA 3D-plots
Direct RNA-seq import
Multiple data sets
One button GEO download
Powerful statistics including open API
Scripting with Templates
Machine learning (Classifiers)
Export pictures & videos
Export lists & data
Global restore & log