Comprehensive analysis and reporting services for your omics data.
We design and manage comprehensive analysis workflows across genomic, transcriptomic, and proteomic datasets. By unifying data processing, statistical modeling, and visualization in one architecture, we build reproducible, scalable analysis systems.
RNA-seq data quality control, alignment, count matrix generation, differential expression analysis, and functional enrichment analyses.
Variant annotation, SNP/Indel filtering, GWAS result visualization, and data retrieval and processing from public genomic databases.
Protein-protein interaction networks, pathway enrichment analysis, GO term annotation, and functional classification studies.
Multiple testing corrections, regression models, clustering analyses, and machine learning-based classification methods.
Project-specific Python and R scripts, Snakemake or Nextflow-based reproducible workflows, and automated reporting systems.
Raw data quality assessment, filtering of low-quality samples, batch effect correction, and normalization procedures.
Differential expression, correlation analyses, PCA, t-SNE, UMAP dimensionality reduction, and hierarchical clustering methods.
Heatmaps, volcano plots, MA plots, pathway diagrams, and interactive visualizations with comprehensive analysis reports.
Detailed documentation including all code, parameters, and methods used. Executable reports shared in R Markdown or Jupyter Notebook format.
Publication-standard vector format (PDF, SVG) figures, heatmaps, network diagrams, and statistical summary tables.
Comprehensive result documents summarizing statistical findings, listing significant genes or proteins, and providing recommendations for next steps.
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