3. Data, Science & AI

End-to-end pipeline for imaging mass cytometry data, handling preprocessing, segmentation, phenotyping, and spatial statistics for cell analysis.

Creates circular genome visualizations with Circos and pyCircos, displaying multi-track genomic data including genes, variants, and interactions.

Normalizes indel representations and splits multiallelic variants in VCF files using bcftools for consistent variant comparison and downstream analysis.

Analyzes spatial transcriptomics data to identify tissue regions by clustering spots based on gene expression and spatial context using Squidpy and Scanpy.

Designs PCR primers for DNA sequences using primer3-py, allowing constraint specification and returning ranked primer pairs with quality metrics.

Quantifies gene-level RNA-seq read counts from BAM files using Subread featureCounts for differential expression analysis with DESeq2 or edgeR.

Generates unified patient similarity networks by fusing multi-omics data using Similarity Network Fusion (SNF) for biomedical patient stratification.

Performs alpha and beta diversity analysis on microbiome datasets using phyloseq and vegan to assess within-sample richness and between-sample dissimilarity.

Performs over-representation analysis and GSEA on biological pathways using WikiPathways and clusterProfiler for multi-species data.

End-to-end pipeline for joint scRNA-seq and scATAC-seq data analysis, including modality processing and WNN integration via Seurat/Signac.

Analyzes cell-cell communication in spatial transcriptomics data via ligand-receptor analysis using Squidpy, identifying signaling pathways and visualizing interaction networks.

End-to-end RNA-seq analysis pipeline from FASTQ to differential expression results, including QC, quantification, and visualization.

Creates scalable, containerized bioinformatics pipelines using Nextflow DSL2 with Docker, Singularity, and cloud execution for portable data processing.

Creates and manipulates biological sequence objects (Seq, MutableSeq, SeqRecord) using Biopython for sequence data processing and annotation.

Creates interactive HTML visualizations with Plotly and Bokeh for exploratory data analysis and web sharing of omics datasets.

Processes and deduplicates sequencing reads using Unique Molecular Identifiers (UMIs) for accurate molecule counting in genomic analyses.

End-to-end workflow for spatial transcriptomics data analysis using Visium/Xenium platforms and Squidpy, covering preprocessing, spatial analysis, and visualization.

Constructs, manages, and searches spectral libraries for proteomics, supporting DDA-based generation and AI-predicted libraries via Prosit and DeepLC.

Builds reference-quality diploid genome assemblies from PacBio HiFi reads using hifiasm with phasing support for haplotype resolution.

Automates cell type annotation in single-cell RNA-seq data using reference-based AI models for consistent and reproducible labeling.

Conducts Gene Ontology over-representation analysis on gene lists using clusterProfiler, testing biological processes, molecular functions, and cellular components.

Aligns RNA-seq reads with HISAT2, a memory-efficient splice-aware aligner for genomic data processing.

Performs pairwise sequence alignment for DNA, RNA, or protein sequences using Biopython, scoring similarity and identifying local or global matches.

Corrects batch effects in CRISPR screen data through normalization and technical replicate handling for accurate multi-batch analysis.