Bioinformatics

Whole-genome (WGS) and whole-exome (WES) sequencing analysis from raw FASTQ reads through QC, alignment, and variant calling (SNPs, indels, CNVs, SVs). Variants are annotated for functional consequence, population frequency, and clinical significance using Ensembl VEP and ANNOVAR. Outputs include annotated VCF files, summary statistics, and an interpretation report.

Bulk RNA-seq analysis covering QC (FastQC, MultiQC), adapter trimming, alignment (STAR or HISAT2), gene-level quantification (featureCounts, Salmon), and differential expression analysis with DESeq2 or edgeR. Visualisations include volcano plots, MA plots, PCA, and sample-to-sample distance heatmaps. scRNA-seq analysis (Seurat / Scanpy) is available for single-cell experiments including clustering, cell-type annotation and trajectory inference.

Over-representation analysis (ORA) and gene set enrichment analysis (GSEA) against GO terms (BP, MF, CC), KEGG pathways, Reactome, MSigDB, and WikiPathways. Results are ranked by normalised enrichment score (NES) and FDR-adjusted p-value, with dot plots, enrichment plots, and pathway-network visualisations. Network-based functional analysis using STRING-imported PPI networks in Cytoscape is available for hub-gene and module identification.

Protein sequence analysis including domain annotation (Pfam, InterPro), homology search (BLAST, HMMER), and structure prediction (AlphaFold2 or ESMFold). For mass spectrometry-based proteomics, MaxQuant or Proteome Discoverer quantification with downstream statistical testing and pathway enrichment. Interaction network construction from STRING or BioGRID data with Cytoscape visualisation and MCODE module detection.

Construction and analysis of biological networks: gene co-expression networks (WGCNA), protein interaction networks (STRING, BioGRID), gene regulatory networks (ARACNE, GENIE3), and genome-scale metabolic models. Network topology metrics (degree, betweenness, eigenvector centrality, clustering coefficient) are used for hub gene identification and network-based biomarker discovery. Multi-omics integration available via MOFA+.