3. Data, Science & AI

Detects differentially methylated regions (DMRs) in genomic data using methylKit, bsseq, and DMRcate for comparing methylation patterns between conditions or cell types.

Automates end-to-end single-cell RNA-seq analysis from 10X Genomics data, including QC, clustering, and cell type annotation.

Computes spatial statistics for spatial transcriptomics data using Squidpy, including Moran's I, Geary's C, and neighborhood enrichment analysis.

Visualizes metagenomic data from taxonomic profiling tools using R and Python, generating publication-ready plots including heatmaps and PCA.

Conducts KEGG pathway enrichment analysis on gene lists to identify over-represented biological pathways across 4000+ organisms using clusterProfiler.

Removes batch effects from RNA-seq data using ComBat, ComBat-Seq, limma, and SVA for unknown batch variables.

Creates and manages BAI/CSI indices for BAM/CRAM genomic alignment files to enable efficient random access and region fetching using samtools and pysam.

Detects transcription factor binding sites in ATAC-seq data using TOBIAS footprinting analysis, identifying DNA regions protected from Tn5 cutting.

Visualizes biological pathway enrichment results using enrichplot R package functions for clusterProfiler output, supporting multiple plot types.

Calculates genomic read depth and coverage across intervals using bedtools, generating bedGraph files and statistics for sequencing assessment and target capture evaluation.

Performs ChIP-seq peak calling with MACS3/MACS2, supporting narrow peaks for transcription factors and broad peaks for histone modifications, outputting in BED formats.

Interactive tool for annotating cell types in IMC data, generating training data for AI classifiers and validating phenotyping results.

Designs qPCR primers and TaqMan/molecular beacon probes with customizable parameters using primer3-py for real-time PCR assays.

Creates portable bioinformatics pipelines using WDL for cloud execution, supporting GATK best practices and Terra/AnVIL platforms.

Analyzes multiple sequence alignments (MSA) using Biopython, extracting sequences, identifying conserved regions, and preparing data for biological research.

Sorts genomic alignment files (BAM) by coordinate or read name using samtools and pysam for variant calling and indexing workflows.

Imports transcript-level RNA-seq quantifications from Salmon/kallisto into R for gene-level differential expression analysis using DESeq2 or edgeR.

Analyzes tissue images from spatial transcriptomics using Squidpy, extracting features, segmenting cells/nuclei, and computing morphological features from H&E or IF images.

Executes RNA-seq differential expression analysis via DESeq2 in R/Bioconductor, including dataset creation, workflow execution, and log fold change shrinkage results extraction.

Processes 16S rRNA and ITS amplicon sequencing data using DADA2 for ASV inference, including quality filtering, denoising, and chimera removal to generate an ASV table.

Identifies cell type-specific marker genes and annotates clusters in single-cell RNA-seq data using Seurat and Scanpy for differential expression analysis.

Calculates sequence alignment statistics including identity, conservation, and similarity metrics for bioinformatics analysis of evolutionary patterns and sequence divergence.

Automates DNA sequencing analysis from FASTQ to variant calls, including QC, alignment with BWA, and variant calling using GATK or bcftools.

Annotates ChIP-seq peaks to genomic features, genes, and calculates TSS distances using ChIPseeker, generating plots and statistics.