본문으로 건너뛰기
cccskills
무료GitHub에 공개

bioconductor-drugtargetinteractions

Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md4.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

drugTargetInteractions

Workflows

Standard Workflow

Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.

library(drugTargetInteractions)
chembldb <- system.file("extdata", "chembl_sample.db", package="drugTargetInteractions")
resultsPath <- system.file("extdata", "results", package="drugTargetInteractions")
config <- genConfig(chemblDbPath=chembldb, resultsPath=resultsPath)
downloadUniChem(config=config)
cmpIdMapping(config=config)
queryBy <- list(molType="cmp", idType="chembl_id", ids=c("CHEMBL17", "CHEMBL19"))
qresult1 <- drugTargetAnnot(queryBy, config=config)

Input: A list containing query molecule/protein IDs and a configuration object. Output: A data.frame of drug-target annotations.

When to Use

  • Identify drug-target interactions for sets of small molecules or gene/protein identifiers using a local SQLite instance of the ChEMBL database.
  • Retrieve UniProt IDs for Ensembl gene IDs using sequence similarity nearest neighbors (SSNNs) via getUniprotIDs or paralogs via getParalogs.
  • Query bioactivity data for compounds or proteins using drugTargetBioactivity.

When NOT to Use

  • For high-throughput de novo molecular docking or 3D structure-based drug design, use packages like Rcpi or external tools.
  • For querying online databases directly without a local SQLite copy of ChEMBL, use direct web API clients.

Data Requirements

  • A local SQLite instance of the ChEMBL database (e.g., chembl_sample.db for testing).
  • Query identifiers formatted as a list with molType (e.g., "cmp", "protein", "gene"), idType (e.g., "chembl_id", "UniProt_ID", "ensembl_gene_id"), and a character vector of ids.

Key Parameters

  • chemblDbPath (NULL): Path to the local ChEMBL SQLite database file passed to genConfig.
  • resultsPath (NULL): Path to the directory where results are stored, passed to genConfig.
  • taxId (9606): Taxonomy ID for organism selection in getUniprotIDs.
  • kt ("ENSEMBL"): Keytype of the input keys in getUniprotIDs.
  • seq_cluster ("UNIREF90"): Sequence similarity level ("UNIREF100", "UNIREF90", "UNIREF50") in getUniprotIDs.
  • id_mapping (c(chembl="chembl_id", pubchem="PubChem_ID", uniprot="UniProt_ID", drugbank="DrugBank_ID")): Mapping vector used in getDrugTarget.

Best Practices

  • Use genConfig to set up paths to the local ChEMBL SQLite database and output directories before running queries.
  • Run downloadUniChem and cmpIdMapping to download and prepare compound identifier mapping tables.
  • Use getParalogs instead of getUniprotIDs when faster execution and wider evolutionary distances are preferred.
  • Combine ID mapping and drug-target annotation steps using the meta function runDrugTarget_Annot_Bioassay.

Common Pitfalls

  • Using the toy database chembl_sample.db for real analyses: replace the path in genConfig with the path to a full downloaded ChEMBL SQLite database.
  • Slow execution of getUniprotIDs: set chunksize=1 only when tracking query Ensembl gene ID information is strictly required, or use getParalogs as a faster alternative.
  • Missing UniProt to Ensembl mappings in final tables: use tapply to collapse Ensembl IDs when mapping back to UniProt IDs.

Alternatives

  • biomaRt for general gene/protein annotation and paralog retrieval.
  • UniProt.ws for direct web-based UniProt ID mapping.
  • ensembldb for local genomic and protein feature mapping.

Citations

  • Wu et al. 2006, Nucleic Acids Res (The Universal Protein Resource)
  • Gaulton et al. 2012, Nucleic Acids Res (ChEMBL database)
  • Bento et al. 2014, Nucleic Acids Res (ChEMBL bioactivity database update)

References

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

bioMate-AI의 스킬 모두 보기

이 스킬의 문제 신고