3. Data, Science & AI

Performs quality control on Hi-C contact matrices by computing coverage, cis/trans ratios, and decay curves to assess data suitability for genomic analyses.

Manages genomic data reproducibility by merging BAM files, creating pseudo-replicates, and performing IDR analysis to generate consensus peak sets from multiple replicates.

Analyzes differential transcription factor binding from ChIP-seq data using DiffBind in R, identifying significant binding changes between biological conditions.

Identifies UMRs, LMRs, and PMDs from WGBS methylation data for epigenetic analysis in cancer, aging, and reprogramming.

Analyzes protein-mediated regulatory communities from ChIA-PET datasets using BED and BEDPE files, with community visualization capabilities.

Performs transcription factor footprint analysis on ATAC-seq data using TOBIAS, correcting Tn5 bias and quantifying TF occupancy at motif sites.

Identifies differentially accessible regions and transcription factor binding regions using DESeq2 from genomic count data, supporting ATAC-seq and ChIP-seq assays.

Analyzes differential DNA methylation (DMRs/DMCs) from WGBS data, standardizing inputs, running Metilene, and generating visualizations.

Performs ATAC-seq specific quality control by calculating chromatin accessibility metrics including TSS enrichment and fragment size distributions from peak-called BAM files.

Generates normalized BigWig tracks from BAM files for ATAC-seq and ChIP-seq visualization with automatic normalization and Tn5 correction.

Detects chromatin loops from Hi-C .mcool files using cooltools for genomic structure analysis.

Identifies topologically associating domains (TADs) from Hi-C data in .mcools/.cool files and visualizes TADs in target genomic regions.

Analyzes genomic peak data (ChIP-seq, ATAC-seq) to identify transcription factor binding motif enrichment using HOMER.

Discovers novel transcription factor binding motifs in genomic regions using HOMER for motif enrichment analysis in ChIP-seq and ATAC-seq data.

Generates interactive HTML dashboards and visualizations from LimaCharlie data using D3.js and Jinja2 templates with strict data accuracy.

Generates and validates Grok parsing rules from sample logs for USP, Cloud Sensor, and External adapters, enabling efficient log data extraction and configuration deployment.

Graph-aware triage engine for analyzing and prioritizing issues within Beads projects using graph data structures.

Queries local Astro Mac app database to deliver App Store Optimization (ASO) insights including keyword rankings, competitor analysis, and health metrics.

Framework for developing AI agents using Google's platform and tools.

Provides an introduction and implementation guide for Google Cloud Vector Search 2.0, enabling vector-based search in AI applications.

Analyzes database query performance by detecting N+1 issues, using EXPLAIN for slow queries, identifying missing indexes, and ensuring safe index migrations.

Extracts task details (title, due date, priority, category) from natural language using Google Gemini AI.

Calculates statistical power and required sample sizes for research studies, supporting experimental design and compliance with standards like NIH rigor.

Guides selection, assumption checking, interpretation, and reporting of statistical hypothesis tests for data analysis.