Services

What We Offer

We provide comprehensive support across diverse -omics platforms and analytical needs:

Genomics (Transcriptomics & Genetic Variation)
  • Single-cell RNA-seq (scRNA-seq) – clustering, differential expression, cell-type annotation, trajectory inference / pseudotime
  • Single-cell Multiome (RNA + ATAC) – joint transcriptome + chromatin profiles
  • CITE-seq (RNA + surface protein)
  • Spatial transcriptomics – 10x Visium, Slide-seq, GeoMx DSP
  • Spatial proteomics – IMC, CODEX, MIBI, DSP platforms
  • Bulk & long-read RNA-seq – Illumina, PacBio, Oxford Nanopore
  • Whole-genome & exome sequencing (WGS/WES) – variant calling & annotation
  • GWAS, eQTL/sQTL analysis – linking variants to expression and splicing traits

Epigenomics & Chromatin Profiling
  • ATAC-seq – open-chromatin landscapes
  • ChIP-seq / CUT&RUN / CUT&TAG – histone marks & TF binding
  • DNA methylation – 850 K array, WGBS, RRBS

Microbiome Analysis
  • 16S rRNA sequencing – taxonomic profiling, diversity metrics
  • Shotgun metagenomics – functional annotation, resistome, virome, strain-level analysis

Metabolomics & Lipidomics
  • Targeted and untargeted workflows – peak detection, normalization, pathway mapping
  • Integration with transcriptomics & proteomics

Multi-Omics & Advanced Analytics
  • Cross-dataset integration (e.g., transcriptome + proteome + methylome)
  • Pathway & functional enrichment (GO, KEGG, Reactome, MSigDB, IPA)
  • Gene-regulatory networks & cell-cell communication (CellPhoneDB, NicheNet)
  • Custom visualizations – heatmaps, volcano plots, UMAPs, dot plots

Cancer Genomics & Clinical Correlation
  • TCGA, GTEx, TARGET analyses
  • Survival analysis and Cox regression with clinical metadata
  • Biomarker discovery & validation

Structural Bioinformatics
  • Protein-structure prediction (AlphaFold, RoseTTAFold)
  • Protein–protein docking (ClusPro, HADDOCK, ZDOCK)
  • Interface analysis, mutation modeling, dynamics

Statistical & Machine-Learning Analytics
  • Classical statistics – linear/mixed-effects models, generalized linear models, survival & competing-risk analyses, power & sample-size calculations
  • High-dimensional methods – DESeq2/edgeR for bulk data; MAST, limma-voom, and pseudobulk for single-cell
  • Feature selection & predictive modeling – LASSO/elastic net, random forest, gradient-boosting, SVM
  • Deep learning – autoencoders, CNN/RNN architectures for sequencing, imaging, and multimodal data (TensorFlow/PyTorch)
  • Unsupervised learning – PCA, ICA, NMF, t-SNE, UMAP, graph-based clustering, topic modeling
  • Explainability tools – SHAP, LIME, permutation importance
  • Reproducible workflows – tidyverse/targets (R); scikit-learn/Scanpy (Python); Docker/Singularity