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
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Domain: Annotation / Genomics Bioconductor: AnnotationHub Paper: Morgan M et al. (2021). Bioconductor package.
Access a large collection of biologically relevant annotation resources (genome sequences, chain files, GTF, OrgDbs, TxDbs) directly from within R via the Bioconductor AnnotationHub.
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("AnnotationHub")Alternatives: GenomicFeatures::makeTxDbFromGFF(), biomaRt, BSgenome
| Requirement | Details |
|---|---|
| Internet | Required — downloads resources from Bioconductor servers (cached locally after first use) |
| Cache | ~500MB–5GB local cache typical for a project's annotation needs |
if (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("AnnotationHub")
library(AnnotationHub)
Find and load genome annotations, ortholog mappings, and database snapshots
library(AnnotationHub)
ah <- AnnotationHub() # connects to Bioconductor AnnotationHub cloud
# Search by species and data type
query(ah, c("Homo sapiens", "EnsDb"))
query(ah, c("Mus musculus", "TxDb"))
query(ah, c("GRCh38", "gtf"))
# Fetch Ensembl 108 database for human
edb <- ah[["AH98047"]] # EnsDb.Hsapiens.v108 equivalent
genes(edb) # returns GRanges of all genes
# Fetch GENCODE GTF as GRanges
gr <- ah[["AH49556"]]
# Load human OrgDb
org_hs <- ah[["AH114084"]] # org.Hs.eg.db snapshot
library(AnnotationDbi)
mapIds(org_hs, keys=c("BRCA1","TP53"), column="ENTREZID", keytype="SYMBOL")
AnnotationHub()()Create an AnnotationHub connection
| Parameter | Description |
|---|---|
cache | local cache directory (default ~/.cache/AnnotationHub) |
ask | ask before downloading (default TRUE) |
query()()Search the hub for resources
| Parameter | Description |
|---|---|
x | AnnotationHub object |
pattern | search terms (character vector, AND logic) |
ah[[id]]()Download and load a resource by its AH identifier (e.g. 'AH12345')
mcols()()View metadata for all resources in a filtered hub
display()()Interactive Shiny browser for AnnotationHub records
The method assumes the following about your data and experiment:
genes(), exons(), transcripts() to extract annotation tables.makeTxDbFromEnsembl() alternatively for more control.query(ah, c('Homo sapiens', 'EnsDb', '109')) to find species + resource type + version.saveRDS() to avoid re-downloading in each session.options(timeout=300) before downloading large resources.ah$species and ah$rdataclass columns to filter results before downloading.| Alternative | Prefer this tool when | Prefer alternative when |
|---|---|---|
biomaRt | You need live Ensembl queries with specific attribute/filter combinations. | You need offline/cached annotation; you want genome sequences or chain files. |
AnnotationHub is infrastructure rather than a statistical method; formal benchmarks compare it against manual download pipelines.
Morgan M, Shepherd L (2022). Bioconductor Annotation Infrastructure. Bioconductor Workflows AnnotationHub reduces annotation retrieval from hours to seconds; reproducible snapshots improve cross-study comparability vs. manual genome downloads.
Gentleman RC et al. (2004). Genome Biol 5:R80. PMID:15461798 Foundational Bioconductor paper establishing annotation infrastructure that AnnotationHub extends; cited for the broader ecosystem providing interoperability guarantees.
Error in .AnnotationHub_cache: cache invalid or corruptCause: Partial download or corrupted cache
Fix: Remove cache: removeCache(ah); ah <- AnnotationHub()
Extracted from the AnnotationHub Bioconductor vignette(s)
2 Troubleshooting 2.1 Accessing Behind A Proxy 2.1 Accessing Behind A Proxy 3 Other configuration options for resource downloading 3.1 Invalid Cache 3.2 Corrupt Cache 3.2.1 sqlite file 3.2.2 index file 3.2.3 resource path 3.2.4 resource id 3.3 Corrupt Database 3.4 Cannot retrieve resource 3.5 Offline localHub usage 3.1 Invalid Cache 3.2 Corrupt Cache 3.2.1 sqlite file 3.2.2 index file 3.2.3 resource path 3.2.4 resource id 3.2.1 sqlite file 3.2.2 index file 3.2.3 resource path 3.2.4 resource id 3.3 Corrupt Database 3.4 Cannot retrieve resource 3.5 Offline localHub usage 4 Group Hub/Cache Access 5 Lock file Troubleshooting 5.1 Permissions 5.2 Cannot lock file / no lock available 5.1 Permissions 5.2 Cannot lock file / no lock available 6 Default Caching Location Update 6.1 Option 1: Move cac
In spring 2019 the Hubs ( AnnotationHub / ExperimentHub ) upgraded their backend to utilize BiocFileCache . This upgrade changed how resources were downloaded and saved. While the Hub code itself ensures validity, it is possible to access the BiocFileCache of resources directly without the Hub front ends, which opens up the possibility of having caching problems. This document will touch on some troubleshooting for these issues as well as any other frequently asked issues. If the question or answer cannot be found here please ask on the Bioconductor Support Site or on the mailing list bioc-devel@r-project.org
The ExperimentHub and AnnotationHub use CRAN package httr2 functions for accessing and downloading web resources. This can be problematic if operating behind a proxy. You can pass proxy information for Hubs by setting the options like the following: Unfortunately unlike the previously used httr::set_config , there is no option to globally set the proxy for httr2 requests. To get around this you can also set a system wide environment variable âANNOTATION_HUB_PROXYâ for AnnotationHub, âEXPERIMENT_HUB_PROXYâ for ExperimentHub, or âHUB_PROXYâ that will work across all Hub classes (e.g. AnnotationHub and ExperimentHub)}
proxy <- "http://my_user:my_password@myproxy:8080"
AnnotationHub::setAnnotationHubOption("PROXY", proxy)
## or
ExperimentHub::setExperimentHubOption("PROXY", proxy)
As mentioned previously, There is no global option like httr::set_config to set configuration options when using httr2. A config argument may be passed to [[ and cache functions. This argument is a R list object that will be passed to httr2::req_options . The names of the items should be valid curl options as defined in curl::curl_options .
ssl_opts <- list(verbose = 1L,ssl_verifypeer = 0L, ssl_verifyhost = 0L)
An invalid cache ERROR results from a missing sqlite or index file in the Hubâs BiocFileCache. The Hub code needs these files in order to operate correctly. Rerun the Hub constructor ( AnnotationHub() or ExperimentHub() ) again. If you were trying to run the constructor with localHub=TRUE , you will have to run localHub=FALSE at least once to redownload the Hub sqlite file.
A corrupt cache ERROR results from multiple entries in the BiocFileCache matching a query for a particular file. This will involve removing one, multiple, or all entries for a file. Please see specific section below although all follow the same principles.
If the sqlite file is the problematic file you should see something like the following (maybe be experimenthub.sqlite3 respectively) You will need to investigate the underlying BiocFileCache for the Hub and remove some or all of the files so that there is only a single entry for the filename. Call the BiocFileCache constructor with the path listed as cache in the ERROR message. Now we can query the BiocFileCache using the filename of the ERROR message. This shows the number of entries for the filename. There should only be one row You will need to deterime if you can validate which entry should remain by evaluating the entries in the cache; [dplyr package][] methods may be useful for parsing the tibble res object. If you can identify which entry should be kept - remove the other entries i
> ah = AnnotationHub()
Error: Corrupt Cache: sqlite file
See AnnotationHub's TroubleshootingTheHubs vignette section on corrupt cache
cache: /home/lori/.cache/AnnotationHub
filename: annotationhub.sqlite3
library(BiocFileCache)
bfc <- BiocFileCache("/home/lori/.cache/AnnotationHub")
A force redownload of the sqlite hub file can be achieved through the refreshHub function. To specify which of the Bioconductor Hubs to redownload use the hubClass argument with either AnnotationHub or ExperimentHub.
ah2 = refreshHub(hubClass="AnnotationHub")
This skill is the knowledge layer — when, why, and how to use annotationhub. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experi
日本語の概要は準備中です。原文の説明を表示しています。