본문으로 건너뛰기
cccskills
무료GitHub에 공개

bioconductor-organismdbi

The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

OrganismDbi

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 1.54.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Depends: BiocGenerics, AnnotationDbi, Seqinfo, GenomicFeatures
  • Imports: DBI, BiocManager, Biobase, graph, RBGL, S4Vectors, IRanges, GenomicRanges
  • System requirements: URL
  • Install: BiocManager::install("OrganismDbi")

When to Use

  • Unified Annotation Queries: Querying multiple annotation resources (e.g., TxDb and OrgDb) simultaneously through a single unified interface using select.
  • Custom Meta-Package Building: Building custom OrganismDbi meta-packages (like Homo.sapiens) that link genome-centric and gene-centric databases using makeOrganismPackage.
  • Feature Extraction: Extracting genomic features (transcripts, exons, cds) mapped directly to gene symbols or other identifiers using transcripts, exons, or cds.
  • Grouped Feature Extraction: Grouping genomic features by a specific identifier (e.g., by gene) using transcriptsBy, exonsBy, or cdsBy.

When NOT to Use

  • Non-Model Organisms: When working with non-model organisms that do not have pre-built OrgDb or TxDb packages available to link together.
  • Cyclic Graph Relationships: When the relationships between your annotation packages present more than one pathway between any two nodes/objects (the package cannot handle cycles in the graph).

Data Requirements

  • Annotation Packages: Installed Bioconductor annotation packages (e.g., org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, GO.db).
  • Graph Data: A list of short two-element character vectors representing foreign key relationships (e.g., graphData) when building custom packages.

Key Parameters

  • keys: The character vector of keys to search for in select or keys.
  • keytype: The type of input keys (e.g., "ENTREZID") provided to select or keys.
  • columns: The columns of information to retrieve (e.g., c("TXNAME", "SYMBOL")) in select, transcripts, or transcriptsBy.
  • by: The grouping factor (e.g., "gene") used in transcriptsBy.
  • pkgname: The name of the custom package to create (e.g., "Homo.sapiens") in makeOrganismPackage.
  • graphData: The list expressing how different packages relate to each other via foreign keys in makeOrganismPackage.

Best Practices

  • Inspect Fields: Use keytypes() and columns() to inspect valid query fields before executing a search.
  • Use Select: Prefer using the unified select() method to retrieve multi-database annotations in a single tabular format.
  • GRanges Integration: When extracting genomic ranges, use functions like transcripts(Homo.sapiens, columns=...) to get GRanges objects pre-annotated with gene metadata.
  • Consistent Builds: Take extra care to ensure that the different packages used in makeOrganismPackage are from the same build (e.g., hg19).

Common Pitfalls

  • Conflicting Package Schemas: Mixing incompatible TxDb and OrgDb versions. Fix: Ensure the underlying TxDb and OrgDb packages correspond to the same genome assembly and annotation release.
  • Non-Unique Columns: Having more than one example of each field across supporting packages. Fix: Ensure all values returned by columns are unique across ALL of the supporting packages before using makeOrganismPackage.
  • Graph Cycles: Providing a graphData list with multiple pathways between nodes. Fix: Choose exactly one foreign key relationship to connect any two packages in your graph.

Alternatives

  • AnnotationDbi: The base package for querying individual OrgDb databases.
  • GenomicFeatures: Specifically for managing and querying TxDb transcript databases.

Citations

  • Carlson M, Atiku Mustapha A (2026). OrganismDbi: A meta framework for Annotation Packages.

References

<!-- biomate-cta -->

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use organismdbi. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open organismdbi on BioMate →

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