Encode Ccres Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides an interface to query the ENCODE Registry of candidate cis-Regulatory Elements (cCREs) using the SCREEN GraphQL API. It also supports custom queries to the ENCODE Portal REST API for raw experimental data, such as ChIP-seq peaks. The skill includes helper scripts to manage API interactions and provides methods to filter results, identify high-confidence biosamples, and retrieve genomic annotations or gene expression data.
What it does
- Search for cCREs by genomic coordinates or accessions.
- Retrieve metadata for biosamples and identify high-confidence Type A samples.
- Find nearby or linked genes for specific regulatory elements.
- Query gene expression data across various biosamples.
- Access raw experimental data and ChIP-seq peaks via the ENCODE Portal.
- Retrieve orthologous cCREs across different genome assemblies.
When to use it
- When you need to identify functional non-coding DNA elements like promoters or enhancers.
- When you are analyzing biochemical signatures across human cell types.
- When you need to link regulatory elements to specific genes or GWAS studies.
- When you require raw experimental data from the ENCODE Portal for custom analysis.
Advantages
- Automated rate limiting for API requests.
- Support for multiple genome assemblies including GRCh38 and mm10.
- Enriched output with biosample confidence flags.
- Integration with standard bioinformatics workflows using JSON-based outputs.
Usage examples
- “Find cCREs on chromosome 11 between positions 5205263 and 5207263.”
- The agent runs the search command and saves the results to a JSON file, which can then be parsed to identify regulatory elements in that region.
- “List all high-confidence Type A biosamples.”
- The agent queries the biosamples endpoint and filters the output to display only those biosamples marked as Type A.
- “Get gene expression data for GAPDH.”
- The agent resolves the gene symbol to an Ensembl ID and retrieves TPM values across available biosamples.
- “Search for ZNF549 experiments in the ENCODE portal.”
- The agent uses the REST API script to query and return a list of experiments matching the specified target.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/encode-ccres-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip encode-ccres-database.zip -d .agents/skills
Documentation by Google DeepMind
ENCODE Database Skill
This skill allows you to query the ENCODE Registry of cCREs (candidate cis-Regulatory Elements) via the SCREEN GraphQL API. It helps identify functional non-coding DNA elements (like Promoters, Enhancers, and insulators) by analyzing biochemical signatures (DNase, H3K4me3, H3K27ac, CTCF).
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.User Notification: If .licenses/encode_ccres_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.encodeproject.org/help/rest-api/, then (2) create the file recording the notification text and timestamp.
Core Rules
Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
Parsing Output: Do NOT use
catto read the entire JSON output file into context, as it can be extremely large. You MUST usejqto efficiently parse and extract relevant fields.Notification: If this skill is used, ensure this is mentioned in the output.
Quick Start
# Search cCREs by coordinates
uv run scripts/screen_api.py search --chromosome chr11 \
--start 5205263 --end 5207263 \
--output /tmp/search.json
# Get details for a specific cCRE
uv run scripts/screen_api.py details EH38E2941922 \
--output /tmp/details.jsonAll subcommands write JSON to disk. Always save output in a temporary location
like /tmp/.
Identifying High-Confidence ("Type A") Biosamples
Biosamples in ENCODE are often categorized by their data completeness. "Type A" (or high-confidence) biosamples are those that have experimental data for all four core epigenetic markers: DNase, H3K4me3, H3K27ac, and CTCF.
The biosamples and details commands automatically enrich their output with
an is_type_a boolean flag for each biosample.
Example: Finding high-confidence cell types
uv run scripts/screen_api.py biosamples --output /tmp/biosamples.json
# Use jq to filter for Type A biosamples
jq '.data.ccREBiosampleQuery.biosamples[] | select(.is_type_a == true) | .displayname' /tmp/biosamples.jsonParsing Output (CRITICAL)
Do NOT use cat to read the entire JSON output file into context, as it
can be extremely large. Instead, you MUST use jq to efficiently parse and
extract the relevant fields from the JSON file saved by the script. If jq is
not available on the system, write your own Python filtering code (e.g.,
python3 -c "import json...") to extract the necessary data.
For a complete reference of the JSON structure returned by eachmcommand (so you
know which fields to query with jq), read
references/json_output_structure.md.
Available Commands
search: Search cCREs by coordinates, accessions, or epigenetic signals.uv run scripts/screen_api.py search \ --chromosome chr11 --start 5205263 --end 5207263 \ --output /tmp/search.jsonnearby-genes: Find nearby genes for given cCRE accessions.uv run scripts/screen_api.py nearby-genes \ EH38E1516972 --output /tmp/nearby.jsondetails: Get detailed information and biosample-specific max Z-scores for a specific cCRE.uv run scripts/screen_api.py details EH38E2941922 \ --output /tmp/details.jsonbiosamples: Get biosample metadata for an assembly.uv run scripts/screen_api.py biosamples \ --output /tmp/biosamples.jsonorthologs: Get orthologous cCREs in another assembly.uv run scripts/screen_api.py orthologs EH38E2941922 \ --output /tmp/orthologs.jsonlinked-genes: Find linked genes via methods like HiC or eQTLs.uv run scripts/screen_api.py linked-genes \ EH38E1516972 --output /tmp/linked.jsongene-expression: Get gene expression (TPM) across all biosamples for a named gene. Internally resolves the gene symbol to an Ensembl gene ID, then queries per-biosample RNA-seq quantifications.uv run scripts/screen_api.py gene-expression GAPDH \ --output /tmp/gene_expr.jsonentex: Get ENTEx data for a cCRE or genomic region.uv run scripts/screen_api.py entex \ --accession EH38E1310345 \ --output /tmp/entex.jsonuv run scripts/screen_api.py entex \ --region chr1:1000068:1000409 \ --output /tmp/entex.jsongwas: Query genome-wide association studies, SNPs, or enrichment data.uv run scripts/screen_api.py gwas studies \ --output /tmp/gwas.jsonuv run scripts/screen_api.py gwas snps --study \ Ahola-Olli_AV-27989323-Eotaxin_levels \ --output /tmp/gwas_snps.json
You can supply the --assembly mm10 or --assembly grch38 flag to explicitly
request a specific assembly for most commands. By default, the script targets
grch38 but will automatically fall back to mm10 if no results are found or
if the query fails.
ENCODE Portal REST API (Direct Access)
For accessing raw experiments, ChIP-seq peaks, or other datasets that are not
represented as cCREs in SCREEN, use the scripts/encode_portal_api.py script.
It allows custom queries to the ENCODE Portal REST API.
Usage
uv run scripts/encode_portal_api.py search "type=Experiment&target.label=ZNF549" --output /tmp/znf549_experiments.jsonData Analysis Tips
When analyzing .bed or .bigBed files downloaded from ENCODE, standard
bioinformatics tools are highly recommended for finding overlaps (e.g., between
gene promoters and peaks):
bedtools: For fast mathematical operations on genomic intervals.bigBedToBed: For converting binary BigBed files to readable BED format.pybedtools: A Python wrapper forbedtools.
Write custom logic if these tools are not pre-installed.
Custom Queries (SCREEN GraphQL)
If you need to make a complex GraphQL query that the script does not support,
read references/graphql_schema.md for a reference of available queries,
arguments, and return fields in the SCREEN GraphQL API.
Files
- ATTRIBUTION.mdAdded by AutoGineer924 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.7 KiB
- references/citation.bib7.7 KiB
- references/graphql_schema.md4.6 KiB
- references/json_output_structure.md5.5 KiB
- scripts/encode_portal_api.pyScript2.0 KiB
- scripts/screen_api.pyScript20.0 KiB
references/citation.bib
@ARTICLE{Dunham2012,
ABSTRACT = {The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80{\%} of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.},
AUTHOR = {Dunham, Ian and Kundaje, Anshul and Aldred, Shelley F. and Collins, Patrick J. and Davis, Carrie A. and Doyle, Francis and Epstein, Charles B. and Frietze, Seth and Harrow, Jennifer and Kaul, Rajinder and Khatun, Jainab and Lajoie, Bryan R. and Landt, Stephen G. and Lee, Bum-Kyu and Pauli, Florencia and Rosenbloom, Kate R. and Sabo, Peter and Safi, Alexias and Sanyal, Amartya and Shoresh, Noam and Simon, Jeremy M. and Song, Lingyun and Trinklein, Nathan D. and Altshuler, Robert C. and Birney, Ewan and Brown, James B. and Cheng, Chao and Djebali, Sarah and Dong, Xianjun and Ernst, Jason and Furey, Terrence S. and Gerstein, Mark and Giardine, Belinda and Greven, Melissa and Hardison, Ross C. and Harris, Robert S. and Herrero, Javier and Hoffman, Michael M. and Iyer, Sowmya and Kellis, Manolis and Kheradpour, Pouya and Lassmann, Timo and Li, Qunhua and Lin, Xinying and Marinov, Georgi K. and Merkel, Angelika and Mortazavi, Ali and Parker, Stephen C. J. and Reddy, Timothy E. and Rozowsky, Joel and Schlesinger, Felix and Thurman, Robert E. and Wang, Jie and Ward, Lucas D. and Whitfield, Troy W. and Wilder, Steven P. and Wu, Weisheng and Xi, Hualin S. and Yip, Kevin Y. and Zhuang, Jiali and Bernstein, Bradley E. and Green, Eric D. and Gunter, Chris and Snyder, Michael and Pazin, Michael J. and Lowdon, Rebecca F. and Dillon, Laura A. L. and Adams, Leslie B. and Kelly, Caroline J. and Zhang, Julia and Wexler, Judith R. and Good, Peter J. and Feingold, Elise A. and Crawford, Gregory E. and Dekker, Job and Elnitski, Laura and Farnham, Peggy J. and Giddings, Morgan C. and Gingeras, Thomas R. and Guigó, Roderic and Hubbard, Timothy J. and Kent, W. James and Lieb, Jason D. and Margulies, Elliott H. and Myers, Richard M. and Stamatoyannopoulos, John A. and Tenenbaum, Scott A. and Weng, Zhiping and White, Kevin P. and Wold, Barbara and Yu, Yanbao and Wrobel, John and Risk, Brian A. and Gunawardena, Harsha P. and Kuiper, Heather C. and Maier, Christopher W. and Xie, Ling and Chen, Xian and Mikkelsen, Tarjei S. and Gillespie, Shawn and Goren, Alon and Ram, Oren and Zhang, Xiaolan and Wang, Li and Issner, Robbyn and Coyne, Michael J. and Durham, Timothy and Ku, Manching and Truong, Thanh and Eaton, Matthew L. and Dobin, Alex and Tanzer, Andrea and Lagarde, Julien and Lin, Wei and Xue, Chenghai and Williams, Brian A. and Zaleski, Chris and Röder, Maik and Kokocinski, Felix and Abdelhamid, Rehab F. and Alioto, Tyler and Antoshechkin, Igor and Baer, Michael T. and Batut, Philippe and Bell, Ian and Bell, Kimberly and Chakrabortty, Sudipto and Chrast, Jacqueline and Curado, Joao and Derrien, Thomas and Drenkow, Jorg and Dumais, Erica and Dumais, Jackie and Duttagupta, Radha and Fastuca, Megan and Fejes-Toth, Kata and Ferreira, Pedro and Foissac, Sylvain and Fullwood, Melissa J. and Gao, Hui and Gonzalez, David and Gordon, Assaf and Howald, Cédric and Jha, Sonali and Johnson, Rory and Kapranov, Philipp and King, Brandon and Kingswood, Colin and Li, Guoliang and Luo, Oscar J. and Park, Eddie and Preall, Jonathan B. and Presaud, Kimberly and Ribeca, Paolo and Robyr, Daniel and Ruan, Xiaoan and Sammeth, Michael and Sandhu, Kuljeet Singh and Schaeffer, Lorain and See, Lei-Hoon and Shahab, Atif and Skancke, Jorgen and Suzuki, Ana Maria and Takahashi, Hazuki and Tilgner, Hagen and Trout, Diane and Walters, Nathalie and Wang, Huaien and Hayashizaki, Yoshihide and Reymond, Alexandre and Antonarakis, Stylianos E. and Hannon, Gregory J. and Ruan, Yijun and Carninci, Piero and Sloan, Cricket A. and Learned, Katrina and Malladi, Venkat S. and Wong, Matthew C. and Barber, Galt P. and Cline, Melissa S. and Dreszer, Timothy R. and Heitner, Steven G. and Karolchik, Donna and Kirkup, Vanessa M. and Meyer, Laurence R. and Long, Jeffrey C. and Maddren, Morgan and Raney, Brian J. and Grasfeder, Linda L. and Giresi, Paul G. and Battenhouse, Anna and Sheffield, Nathan C. and Showers, Kimberly A. and London, Darin and Bhinge, Akshay A. and Shestak, Christopher and Schaner, Matthew R. and Ki Kim, Seul and Zhang, Zhuzhu Z. and Mieczkowski, Piotr A. and Mieczkowska, Joanna O. and Liu, Zheng and McDaniell, Ryan M. and Ni, Yunyun and Rashid, Naim U. and Kim, Min Jae and Adar, Sheera and Zhang, Zhancheng and Wang, Tianyuan and Winter, Deborah and Keefe, Damian and Iyer, Vishwanath R. and Zheng, Meizhen and Wang, Ping and Gertz, Jason and Vielmetter, Jost and Partridge, E. and Varley, Katherine E. and Gasper, Clarke and Consortium, The ENCODE Project},
URL = {https://doi.org/10.1038/nature11247},
DATE = {2012-09-01},
DOI = {10.1038/nature11247},
ISSN = {1476-4687},
JOURNALTITLE = {Nature},
NUMBER = {7414},
PAGES = {57--74},
TITLE = {An integrated encyclopedia of DNA elements in the human genome},
VOLUME = {489},
}
@ARTICLE{Consortium2020,
AUTHOR = {{ENCODE Project Consortium}},
DATE = {2020},
DOI = {10.1038/s41586-020-2493-4},
JOURNALTITLE = {Nature},
NUMBER = {7818},
PAGES = {699--710},
TITLE = {Expanded encyclopaedias of DNA elements in the human and mouse genomes},
VOLUME = {583},
}
@ARTICLE{Luo2020,
ABSTRACT = {The Encyclopedia of DNA Elements (ENCODE) is an ongoing collaborative research project aimed at identifying all the functional elements in the human and mouse genomes. Data generated by the ENCODE consortium are freely accessible at the ENCODE portal (https://www.encodeproject.org/), which is developed and maintained by the ENCODE Data Coordinating Center (DCC). Since the initial portal release in 2013, the ENCODE DCC has updated the portal to make ENCODE data more findable, accessible, interoperable and reusable. Here, we report on recent updates, including new ENCODE data and assays, ENCODE uniform data processing pipelines, new visualization tools, a dataset cart feature, unrestricted public access to ENCODE data on the cloud (Amazon Web Services open data registry, https://registry.opendata.aws/encode-project/) and more comprehensive tutorials and documentation.},
AUTHOR = {Luo, Yunhai and Hitz, Benjamin C and Gabdank, Idan and Hilton, Jason A and Kagda, Meenakshi S and Lam, Bonita and Myers, Zachary and Sud, Paul and Jou, Jennifer and Lin, Khine and Baymuradov, Ulugbek K and Graham, Keenan and Litton, Casey and Miyasato, Stuart R and Strattan, J Seth and Jolanki, Otto and Lee, Jin-Wook and Tanaka, Forrest Y and Adenekan, Philip and O’Neill, Emma and Cherry, J Michael},
URL = {https://doi.org/10.1093/nar/gkz1062},
DATE = {2020-01},
DOI = {10.1093/nar/gkz1062},
EPRINT = {https://academic.oup.com/nar/article-pdf/48/D1/D882/32874907/gkz1062.pdf},
ISSN = {0305-1048},
JOURNALTITLE = {Nucleic Acids Research},
NUMBER = {D1},
PAGES = {D882--D889},
TITLE = {New developments on the Encyclopedia of DNA Elements (ENCODE) data portal},
VOLUME = {48},
}
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Low risk
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/encode_ccres_database · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Google DeepMind or Google DeepMind.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download encode-ccres-database.zip