Bioinformatics software and data analysis

Bioinformatics software

Qlucore Omics Explorer is an easy-to-use bioinformatics multi-omics data analysis software. The powerful visualization-based data analysis tool with inbuilt powerful statistics delivers immediate results and provides instant exploration and visualization of big data. A broad spectrum of Omics and NGS data types are supported and easily imported.

Perform data analysis without being statistical experts

The bioinformatics software supports many different types of omics data such as gene expression (microarray, RNA-seq, single cell), proteomics, metabolomics, miRNA, qPCR, Flow cytometry etc. With the NGS add on module analysis along the genome is supported with the fast Genome browser.  

Data analysis and visualization using statistical methods for continues as well as count-based data can quickly and easily be performed. Results are updated in real time in many different plot types such as PCA, heatmaps, t-SNE, UMAP, box, scatter, volcano, Venn diagram, Kaplan Meier, Violin plots etc.  

Many types of bioinformatics analysis are supported:  

• Statistical analysis - The powerful statistical framework supports a wide range of tests including, but not limited to, DESeq2 (Wald, LRT, Independent filtering), t-tests, ANOVA, paired tests, Two-way ANOVA, linear/quadratic/rank regression, Welch, Kruskal-Wallis and Mann-Whitney.
• Fold change and difference
• Tests can be further customized using eliminated factors and restrictions.   
• Biomarker discovery analysis - using our Biomarker workbench optimized for experiments and studies in the areas of drug development and biomarker discovery.  
• Genome analysis – with the NGS module. Filters and easy to use navigation tools make it possible to dynamically investigate specific regions of the genome.  
• Peak detection – with the NGS module. Detect peaks and generate counts for ChIP-seq and ATAC-seq data, find matching genes and dynamically investigate specific regions of the genome. 
• Cluster analysis - either use a semi-supervised mode using PCA, t-SNE or UMAP, with Projection score and variance filtering or an unsupervised mode using kmeans++.  
• Survival analysis using methods such as Hazard ratio calculations as well as visualizations in Kaplan-Meier plots. 
• Gene fusion analysis with the integrated Gene fusion workbench which is part of the NGS module. 
• Pathway analysis - compare and enhance your findings use the integrated GSEA Workbench which is set-up using only a few mouse-clicks.  
• Classification and machine learning – enables both the option to easily build classifiers based on models such as Boosted trees, Support Vector Machines (SVM), Random Trees (RT) and kNN and to classify new samples. 

Read more and choose your preferred application area.