Results for “genome-analysis”

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

Analyzes encryption algorithms, key management, and file encryption routines used by ransomware families to assess decryption feasibility, identify implementation weaknesses, and support recovery efforts.

Query gnomAD (Genome Aggregation Database) for population allele frequencies and gene constraint scores (pLI, LOEUF) reflecting loss-of-function intolerance. Use when checking how common a variant is across populations, filtering rare-disease candidate variants, assessing variant pathogenicity, or identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.

Use when profiling nucleotide content (AT/GC percentages, base counts) of genomic intervals against a FASTA reference.

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores, drug sensitivity data, and gene effect profiles to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets.

Use when searching DNA or RNA queries against nucleotide sequence databases with HMMER's nucleotide homology search engine.

Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.

Build and analyze phylogenetic trees using MAFFT, IQ-TREE 2, and FastTree, with visualization via ETE3 or FigTree for evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies.

Use when searching protein query sequences against a translated nucleotide database to identify protein-coding regions or homologs in genomic data.

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or validating oncology drug targets. Part of the AlterLab Academic Skills suite.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Query NCBI Gene via the E-utilities and Datasets APIs, searching by gene symbol or Gene ID and retrieving gene information (RefSeqs, GO terms, genomic locations, associated phenotypes) including batch lookups. Use when resolving gene symbols to IDs, annotating gene lists, or pulling functional and positional gene metadata for downstream analysis. Part of the AlterLab Academic Skills suite.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Refactoring Analyst

Read, write, and manipulate genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Covers over-representation analysis (ORA), Gene Set Enrichment Analysis (GSEA), and single-sample scoring using gseapy, g:Profiler, and Enrichr libraries.

Diagnoses a reported bug, error, test failure, or unexpected behavior through a 4-phase root-cause analysis before any code changes.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Look up NHS England genomic tests for rare and inherited diseases. Use this skill whenever a clinician asks what genetic test to order for a condition, which genes are covered for a specific diagnosis, what commissioning category a genomic test falls under (Core, Specialised, or Highly Specialised), or which tests belong to a specialty group (Neurology, Cardiology, Endocrinology, etc.). Also trigger for questions like what panel is used for a condition, whether there is an NHS test for something, what the R number is for a condition, or whether anything has changed in the genomic test directory. Source: NHS England National Genomic Test Directory for Rare and Inherited Disease v9.0 (April 2026).

Query gnomAD for population allele frequencies, constraint metrics, and loss-of-function intolerance. Use when interpreting variants, filtering common alleles, or prioritizing genes in rare disease workflows.

Use when performing genome arithmetic on interval files (BED, BAM, BEDGRAPH), including intersection, merging, coverage, format conversion, or sequence extraction.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.