3. Data, Science & AI

Calculates biological sequence properties including GC content, molecular weight, and isoelectric point using Biopython for sequence analysis.

Generates clustered heatmaps with row/column annotations for gene expression and omics data using ComplexHeatmap, pheatmap, and seaborn to identify co-expressed gene clusters.

Performs end-to-end single-cell ATAC-seq analysis including QC, clustering, peak calling, and motif scoring using Signac and ArchR.

Writes biological sequences to standard file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython's SeqIO module for sequence data management.

Queries NCBI GEO for gene expression datasets using Biopython Bio.Entrez, enabling retrieval of microarray/RNA-seq data and linking to SRA.

Detects and removes doublets from single-cell RNA-seq data using Scrublet, DoubletFinder, and scDblFinder to ensure accurate clustering and analysis.

Detects chromatin loops, CTCF-mediated interactions, and enhancer-promoter contacts from Hi-C data using bioinformatics tools.

Performs ATAC-seq quality control by analyzing fragment size distribution, TSS enrichment, FRiP, and library complexity to identify problematic samples.

Executes genomic interval arithmetic (intersect, merge, subtract) via bedtools and pybedtools for region analysis and annotation transfer.

Performs quality control, batch effect correction, and data normalization on metabolomics datasets to prepare for statistical analysis.

Performs structural variant calling from short-read sequencing data using Manta, Delly, and LUMPY to detect large genomic alterations beyond standard SNV callers.

Maps metabolites to biochemical pathways via KEGG, Reactome, and MetaboAnalyst for enrichment and topology analysis in metabolomics interpretation.

Performs unsupervised integration of multi-omics data (e.g., RNA-seq, proteomics) using MOFA2 to identify shared and modality-specific biological variation factors.

End-to-end ATAC-seq analysis pipeline from FASTQ to differential accessibility and transcription factor footprinting using MACS3 and TOBIAS.

Performs bead-based normalization for CyTOF and flow cytometry data, correcting instrument drift and harmonizing batches to ensure data consistency.

Generates variant statistics, sample concordance, and quality metrics from VCF files using bcftools and gtcheck for genomic data analysis.

End-to-end bisulfite sequencing analysis pipeline from FASTQ to differentially methylated regions using Bismark and methylKit.

Performs protein inference from peptide identifications in proteomics, handling shared peptides and protein-level FDR control via parsimony and probabilistic methods.

Comprehensive Illumina read preprocessing workflow using fastp for adapter trimming, quality filtering, deduplication, and HTML report generation in a single tool.

End-to-end somatic variant calling pipeline for tumor-normal samples using Mutect2/Strelka2, including preprocessing, calling, filtering, and annotation in cancer genomics.

Infers developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo.

Provides comprehensive quality control for flow cytometry and CyTOF data, assessing flow rate stability, signal drift, dead cell exclusion, and batch QC to identify problematic samples before analysis.

Parse and write protein structure files (PDB, mmCIF, MMTF) using Biopython with RCSB PDB integration.

Performs statistical differential binding analysis on ChIP-seq data to identify regions with significant binding changes between experimental conditions.