3. Data, Science & AI

End-to-end Hi-C analysis workflow processing contact pairs to identify genomic structures including compartments, TADs, and loops using cooler matrices and cooltools.

Executes multi-omics data integration using mixOmics, featuring sPLS for pairwise analysis and DIABLO for discriminant multi-block analysis.

Enables inspection, querying, and structural analysis of genetic variant data in VCF/BCF formats using bcftools and cyvcf2.

Estimates species-level abundance from Kraken2 metagenomic classification by redistributing reads using Bracken for improved accuracy.

End-to-end proteomics pipeline processing MaxQuant output to differential protein abundance via statistical testing with MSstats or limma.

Performs production-grade germline variant calling from WGS/WES data using GATK best practices, including VQSR and joint genotyping.

Performs comprehensive genomic variant annotation using VEP, SnpEff, ANNOVAR, and bcftools to predict functional consequences and clinical significance.

Automates end-to-end flow cytometry data analysis from FCS files through compensation, transformation, gating, and statistical testing with CATALYST/diffcyt.

Enables viewing, converting, and analyzing SAM/BAM/CRAM genomic alignment files with samtools and pysam for bioinformatics data workflows.

Visualizes Hi-C genomic interaction data including contact matrices, TADs, and loops using specialized bioinformatics tools for scientific analysis.

Integrates scRNA-seq samples using Harmony, scVI, and Seurat to remove batch effects while preserving biological variation.

Generates publication-ready visualizations for copy number variation (CNV) data from genomic analysis tools, including genome-wide plots and chromosome-level views.

Conducts quality control and exploratory analysis on RNA-seq count matrices to identify outliers, batch effects, and sample relationships before differential expression analysis.

Analyzes codon usage bias and calculates Codon Adaptation Index (CAI) for gene expression optimization and evolutionary studies using Biopython.

Filters DNA sequencing reads by quality, length, and N content using Trimmomatic and fastp, applying sliding window trimming and discarding low-quality reads.

Performs fast taxonomic classification of metagenomic reads using Kraken2 against RefSeq database for initial metagenomic analysis before abundance estimation.

Visualizes spatial transcriptomics data with gene expression, clusters, and annotations overlaid on histology images using Squidpy and Scanpy.

Automates functional enrichment analysis from differential expression data using GO, KEGG, and Reactome pathways with clusterProfiler visualization.

Infers cell type proportions in spatial transcriptomics spots from scRNA-seq references using reference-based deconvolution tools.

End-to-end multi-omics data integration and analysis workflow for transcriptomics, proteomics, and metabolomics using MOFA and mixOmics.

Conducts comprehensive quality control for proteomics data, including sample metrics, missing values, batch effects, and intensity distributions.

Validates NGS alignment quality using insert size, GC bias, and strand balance metrics to ensure data integrity before variant calling.

Detects antimicrobial resistance genes in clinical isolates and metagenomic data using AMRFinderPlus, ResFinder, and CARD databases.

Statistically compares cell populations in flow cytometry data to identify significant changes in cell frequencies and marker expression between experimental conditions.