Epigenomics Tools

Configuration File: epigenomics_tools.json Tool Type: Local Tools Count: 12

This page contains all tools defined in the epigenomics_tools.json configuration file.

Available Tools

ENCODE_get_chromatin_state (Type: EpigenomicsTool)

Search ENCODE chromatin state annotations (ChromHMM segmentations) for specific biosamples or tis…

ENCODE_get_chromatin_state tool specification

Tool Information:

• Name: ENCODE_get_chromatin_state

• Type: EpigenomicsTool

• Description: Search ENCODE chromatin state annotations (ChromHMM segmentations) for specific biosamples or tissues. Chromatin states are genome-wide annotations that classify each genomic position into a functional state (e.g., active TSS, flanking active TSS, strong transcription, weak transcription, genic enhancers, active enhancers, bivalent enhancer, bivalent/poised TSS, flanking bivalent TSS, repressed polycomb, weak repressed polycomb, quiescent, heterochromatin) based on combinatorial patterns of histone marks. Use this to understand the chromatin landscape of specific cell types.

Parameters:

• biosample_term_name ([‘string’, ‘null’]) (optional) Biosample name (e.g., ‘K562’, ‘HepG2’, ‘GM12878’, ‘liver’). Leave empty for all.

• organism (string) (optional) Organism scientific name.

• limit (integer) (optional) Maximum number of results.

Example Usage:

query = {
    "name": "ENCODE_get_chromatin_state",
    "arguments": {
    }
}
result = tu.run(query)

ENCODE_search_annotations (Type: EpigenomicsTool)

Search ENCODE annotations including candidate cis-Regulatory Elements (cCREs), chromatin states, …

ENCODE_search_annotations tool specification

Tool Information:

• Name: ENCODE_search_annotations

• Type: EpigenomicsTool

• Description: Search ENCODE annotations including candidate cis-Regulatory Elements (cCREs), chromatin states, and imputed signals. cCREs are classified as promoter-like (PLS), proximal enhancer-like (pELS), distal enhancer-like (dELS), DNase-H3K4me3 (DNase-H3K4me3), and CTCF-only. Chromatin state annotations use ChromHMM models to segment the genome into functional states. Use this to find regulatory element annotations for specific biosamples or genome assemblies.

Parameters:

• annotation_type (string) (optional) Annotation type filter. Options: ‘candidate Cis-Regulatory Elements’ (cCREs), ‘chromatin state’ (ChromHMM), ‘imputed signal’ (predicted epigenetic marks). Default: cCREs.

• biosample_term_name ([‘string’, ‘null’]) (optional) Biosample filter (e.g., ‘K562’, ‘HepG2’). Leave empty for all.

• organism (string) (optional) Organism scientific name.

• assembly (string) (optional) Genome assembly (e.g., ‘GRCh38’, ‘hg19’, ‘mm10’).

• limit (integer) (optional) Maximum number of results.

Example Usage:

query = {
    "name": "ENCODE_search_annotations",
    "arguments": {
    }
}
result = tu.run(query)

ENCODE_search_chromatin_accessibility (Type: EpigenomicsTool)

Search ENCODE chromatin accessibility experiments (ATAC-seq and DNase-seq). ATAC-seq and DNase-se…

ENCODE_search_chromatin_accessibility tool specification

Tool Information:

• Name: ENCODE_search_chromatin_accessibility

• Type: EpigenomicsTool

• Description: Search ENCODE chromatin accessibility experiments (ATAC-seq and DNase-seq). ATAC-seq and DNase-seq identify open chromatin regions including promoters, enhancers, and other regulatory elements. Returns experiment accessions, biosample details, and accessibility assay metadata. Open chromatin regions are sites of active gene regulation where transcription factors can bind. Use this to find chromatin accessibility data for specific tissues or cell types.

Parameters:

• assay_type (string) (optional) Chromatin accessibility assay: ‘ATAC-seq’ (Assay for Transposase-Accessible Chromatin, newer/preferred) or ‘DNase-seq’ (DNase I hypersensitive sites, larger dataset).

• biosample_term_name ([‘string’, ‘null’]) (optional) Biosample name filter (e.g., ‘K562’, ‘GM12878’, ‘liver’). Leave empty for all.

• organism (string) (optional) Organism scientific name.

• limit (integer) (optional) Maximum number of results to return.

Example Usage:

query = {
    "name": "ENCODE_search_chromatin_accessibility",
    "arguments": {
    }
}
result = tu.run(query)

ENCODE_search_histone_experiments (Type: EpigenomicsTool)

Search ENCODE histone ChIP-seq experiments by histone modification mark, biosample, or organism. …

ENCODE_search_histone_experiments tool specification

Tool Information:

• Name: ENCODE_search_histone_experiments

• Type: EpigenomicsTool

• Description: Search ENCODE histone ChIP-seq experiments by histone modification mark, biosample, or organism. Returns experiment accessions, histone marks (H3K4me3, H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K9me3), biosample summaries, and metadata. Use this to find histone modification profiling data for specific marks or tissues. Common marks: H3K4me3 (active promoters), H3K27ac (active enhancers), H3K27me3 (polycomb repression), H3K4me1 (poised enhancers), H3K36me3 (gene bodies), H3K9me3 (heterochromatin).

Parameters:

• histone_mark ([‘string’, ‘null’]) (optional) Histone modification mark to filter by (e.g., ‘H3K4me3’, ‘H3K27ac’, ‘H3K27me3’, ‘H3K4me1’, ‘H3K36me3’, ‘H3K9me3’). Leave empty to search all histone marks.

• biosample_term_name ([‘string’, ‘null’]) (optional) Biosample name filter (e.g., ‘K562’, ‘HepG2’, ‘GM12878’, ‘liver’, ‘brain’). Leave empty to search all biosamples.

• organism (string) (optional) Organism scientific name (e.g., ‘Homo sapiens’, ‘Mus musculus’).

• limit (integer) (optional) Maximum number of results to return (1-100).

Example Usage:

query = {
    "name": "ENCODE_search_histone_experiments",
    "arguments": {
    }
}
result = tu.run(query)

ENCODE_search_methylation_experiments (Type: EpigenomicsTool)

Search ENCODE whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite seque…

ENCODE_search_methylation_experiments tool specification

Tool Information:

• Name: ENCODE_search_methylation_experiments

• Type: EpigenomicsTool

• Description: Search ENCODE whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS) experiments for DNA methylation profiling. Returns experiment accessions, biosample information, methylation assay type, and metadata. DNA methylation (5mC) at CpG sites is a key epigenetic mark regulating gene silencing, genomic imprinting, and X-inactivation. Use this to find methylation data for specific tissues, cell lines, or developmental stages.

Parameters:

• assay_type (string) (optional) Methylation assay type: ‘WGBS’ (whole-genome bisulfite sequencing, comprehensive CpG coverage) or ‘RRBS’ (reduced-representation, enriched for CpG islands).

• biosample_term_name ([‘string’, ‘null’]) (optional) Biosample name filter (e.g., ‘K562’, ‘liver’, ‘brain’, ‘motor neuron’). Leave empty for all.

• organism (string) (optional) Organism scientific name.

• limit (integer) (optional) Maximum number of results to return.

Example Usage:

query = {
    "name": "ENCODE_search_methylation_experiments",
    "arguments": {
    }
}
result = tu.run(query)

EnsemblReg_get_regulatory_elements (Type: EpigenomicsTool)

Get Ensembl regulatory features (enhancers, promoters, CTCF binding sites, open chromatin, TF bin…

EnsemblReg_get_regulatory_elements tool specification

Tool Information:

• Name: EnsemblReg_get_regulatory_elements

• Type: EpigenomicsTool

• Description: Get Ensembl regulatory features (enhancers, promoters, CTCF binding sites, open chromatin, TF binding sites) for a genomic region from the Ensembl Regulatory Build. The Ensembl Regulatory Build integrates epigenomic data from ENCODE, Roadmap Epigenomics, and BLUEPRINT to annotate regulatory elements across the human genome. Returns feature IDs, types, coordinates, and extended bounds. Use this to identify known regulatory elements in a region of interest for epigenomic analysis or variant interpretation.

Parameters:

• species (string) (optional) Species name (e.g., ‘homo_sapiens’, ‘mus_musculus’).

• chrom (string) (required) Chromosome number without ‘chr’ prefix (e.g., ‘17’, ‘7’, ‘X’).

• start (integer) (required) Start position (1-based).

• end (integer) (required) End position (1-based).

Example Usage:

query = {
    "name": "EnsemblReg_get_regulatory_elements",
    "arguments": {
        "chrom": "example_value",
        "start": 10,
        "end": 10
    }
}
result = tu.run(query)

GEO_get_dataset_details (Type: EpigenomicsTool)

Get detailed metadata for a specific GEO dataset by its accession (GSE ID). Returns comprehensive…

GEO_get_dataset_details tool specification

Tool Information:

• Name: GEO_get_dataset_details

• Type: EpigenomicsTool

• Description: Get detailed metadata for a specific GEO dataset by its accession (GSE ID). Returns comprehensive information including title, summary, experiment type, platform, organism, sample count, publication date, and supplementary data links. Use this after searching with GEO_search_methylation_datasets or GEO_search_chipseq_datasets to get full details about a dataset of interest.

Parameters:

• geo_id (string) (required) GEO dataset accession (numeric part only, e.g., ‘200291249’ for GSE291249). Get IDs from GEO search results.

Example Usage:

query = {
    "name": "GEO_get_dataset_details",
    "arguments": {
        "geo_id": "example_value"
    }
}
result = tu.run(query)

GEO_search_chipseq_datasets (Type: EpigenomicsTool)

Search NCBI GEO for ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) datasets. ChI…

GEO_search_chipseq_datasets tool specification

Tool Information:

• Name: GEO_search_chipseq_datasets

• Type: EpigenomicsTool

• Description: Search NCBI GEO for ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) datasets. ChIP-seq profiles genome-wide binding of proteins (transcription factors, histones) to DNA. Returns dataset accessions, titles, summaries, and sample counts. Use this to find ChIP-seq studies for specific transcription factors, histone marks, or in specific cell types/diseases.

Parameters:

• query (string) (required) Search terms for ChIP-seq datasets (e.g., ‘H3K27ac liver’, ‘CTCF cancer’, ‘p53 ChIP-seq’).

• organism (string) (optional) Organism filter.

• limit (integer) (optional) Maximum number of dataset IDs to return.

Example Usage:

query = {
    "name": "GEO_search_chipseq_datasets",
    "arguments": {
        "query": "example_value"
    }
}
result = tu.run(query)

GEO_search_methylation_datasets (Type: EpigenomicsTool)

Search NCBI GEO for DNA methylation array datasets, including Illumina 450K, EPIC (850K), and oth…

GEO_search_methylation_datasets tool specification

Tool Information:

• Name: GEO_search_methylation_datasets

• Type: EpigenomicsTool

• Description: Search NCBI GEO for DNA methylation array datasets, including Illumina 450K, EPIC (850K), and other methylation profiling platforms. Returns dataset accessions (GSE IDs), titles, summaries, platform information, sample counts, and organisms. DNA methylation arrays measure methylation levels at hundreds of thousands of CpG sites genome-wide. Use this to find published methylation studies for specific conditions, tissues, or diseases. Follow up with GEO_get_dataset_details for full metadata.

Parameters:

• query (string) (required) Search terms for methylation datasets (e.g., ‘breast cancer methylation’, ‘brain methylation aging’, ‘CpG island methylation’). Combined with methylation platform filter.

• organism (string) (optional) Organism filter (e.g., ‘Homo sapiens’, ‘Mus musculus’).

• limit (integer) (optional) Maximum number of dataset IDs to return.

Example Usage:

query = {
    "name": "GEO_search_methylation_datasets",
    "arguments": {
        "query": "example_value"
    }
}
result = tu.run(query)

UCSC_get_cpg_islands (Type: UCSCEpigenomicsTool)

Get CpG island annotations for a genomic region from UCSC Genome Browser. CpG islands are genomic…

UCSC_get_cpg_islands tool specification

Tool Information:

• Name: UCSC_get_cpg_islands

• Type: UCSCEpigenomicsTool

• Description: Get CpG island annotations for a genomic region from UCSC Genome Browser. CpG islands are genomic regions with high CpG dinucleotide density, often found at gene promoters. Methylation of CpG islands is associated with gene silencing. Returns CpG island locations, length, CpG count, GC content, and observed/expected CpG ratio. Use this to identify CpG islands near genes of interest for methylation analysis, or to characterize the regulatory landscape of a genomic region.

Parameters:

• genome (string) (optional) Genome assembly (e.g., ‘hg38’, ‘hg19’, ‘mm10’, ‘mm39’).

• chrom (string) (required) Chromosome name (e.g., ‘chr17’, ‘chr1’, ‘chrX’).

• start (integer) (required) Start position (0-based, inclusive).

• end (integer) (required) End position (0-based, exclusive).

Example Usage:

query = {
    "name": "UCSC_get_cpg_islands",
    "arguments": {
        "chrom": "example_value",
        "start": 10,
        "end": 10
    }
}
result = tu.run(query)

UCSC_get_encode_cCREs (Type: UCSCEpigenomicsTool)

Get ENCODE4 candidate cis-Regulatory Elements (cCREs) for a genomic region from UCSC Genome Brows…

UCSC_get_encode_cCREs tool specification

Tool Information:

• Name: UCSC_get_encode_cCREs

• Type: UCSCEpigenomicsTool

• Description: Get ENCODE4 candidate cis-Regulatory Elements (cCREs) for a genomic region from UCSC Genome Browser. cCREs are classified by epigenetic signatures into: Promoter-like (PLS, red), Proximal enhancer-like (pELS, orange), Distal enhancer-like (dELS, yellow), DNase-H3K4me3 (blue), CTCF-only/CTCF-bound (green). Each cCRE includes max Z-scores for DNase, H3K4me3, H3K27ac, and CTCF signals across biosamples. Use this to identify regulatory elements in a region of interest for functional genomics or GWAS variant interpretation.

Parameters:

• genome (string) (optional) Genome assembly (e.g., ‘hg38’, ‘mm10’).

• chrom (string) (required) Chromosome name (e.g., ‘chr17’).

• start (integer) (required) Start position (0-based).

• end (integer) (required) End position (0-based).

Example Usage:

query = {
    "name": "UCSC_get_encode_cCREs",
    "arguments": {
        "chrom": "example_value",
        "start": 10,
        "end": 10
    }
}
result = tu.run(query)

UCSC_get_tf_binding_clusters (Type: UCSCEpigenomicsTool)

Get Transcription Factor ChIP-seq Clusters from ENCODE3 for a genomic region via UCSC Genome Brow…

UCSC_get_tf_binding_clusters tool specification

Tool Information:

• Name: UCSC_get_tf_binding_clusters

• Type: UCSCEpigenomicsTool

• Description: Get Transcription Factor ChIP-seq Clusters from ENCODE3 for a genomic region via UCSC Genome Browser. Returns clusters of TF binding sites from 340 factors across 129 cell types, aggregated from ENCODE3 ChIP-seq peak data. Each cluster shows the transcription factor name, peak score, and number of source experiments. Use this to identify which transcription factors bind to a specific genomic region, discover regulatory hotspots, or interpret GWAS variants by finding overlapping TF binding sites.

Parameters:

• genome (string) (optional) Genome assembly (e.g., ‘hg38’, ‘hg19’).

• chrom (string) (required) Chromosome name (e.g., ‘chr17’).

• start (integer) (required) Start position (0-based).

• end (integer) (required) End position (0-based).

Example Usage:

query = {
    "name": "UCSC_get_tf_binding_clusters",
    "arguments": {
        "chrom": "example_value",
        "start": 10,
        "end": 10
    }
}
result = tu.run(query)

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