Ingest VCF/BCF files into TileDB arrays for scalable genomic variant queries.
npx skills add https://github.com/Yezez9/Research-Agent --skill tiledbvcf-yezez9
Please help me install this Agent Skill. Skill: tiledbvcf Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/tiledbvcf Command: npx skills add https://github.com/Yezez9/Research-Agent --skill tiledbvcf-yezez9
This Skill provides efficient storage, querying, and retrieval of genomic variant data, enabling scalable analysis of large VCF/BCF files for population genomics and cohort studies.
Use the tiledbvcf skill to ingest the VCF files 'sample1.vcf.gz' and 'sample2.vcf.gz' into a new dataset named 'my_dataset'.
High-intent search queries and answers about installing and using this skill.
To store and query large VCF files for population genomics, you can ingest VCF/BCF files into TileDB arrays. This enables compressed storage, parallel queries across genomic regions, and incremental sample addition for scalable cohort analysis.
The best way to analyze allele frequencies across thousands of samples is querying a TileDB-VCF dataset. It performs parallel queries across specified genomic regions significantly faster than traditional VCF tools, enabling efficient cohort studies.
Yes, you can use TileDB-VCF with cloud storage for genomic data. It integrates directly with S3, Azure, and GCS, allowing you to ingest, query, and export variant data directly from scalable cloud environments.
You add new samples to an existing TileDB-VCF dataset incrementally during the ingestion process. The scalable ingestion feature supports adding VCF/BCF files to an already created dataset without rebuilding the entire array.
Yes, TileDB-VCF supports exporting subsets of variant data for downstream analysis. After querying specific genomic regions and samples, you can export the resulting data in various formats for further processing.
Traditional VCF tools face performance limitations for large cohort studies, resulting in slower query speeds. TileDB-VCF overcomes this by using compressed arrays and parallel queries for significantly faster data retrieval across thousands of samples.
biomaps-infraStore, query, and retrieve genomic variant data using TileDB-VCF.
exon-researchFetch population allele frequencies for a specified variant from gnomAD.
xiechyParallelize pandas and NumPy workflows with Dask DataFrames and Arrays.
Victory-HugoRead, write, and analyze SAM/BAM/CRAM, VCF/BCF, and FASTA/FASTQ genomic data via Python.
