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Open-Access Computational Biology Datasets
Efficiently access a curated library of open-access computational biology datasets. Tables support predicate pushdown and projection to the cloud storage backend, enabling quick, iterative access to otherwise massive, unwieldy tables.
bedrockbio consists of five user-facing functions:
list_namespaces(): returns a character vector of
available namespace (data source) identifiersdescribe_namespace("<name>"): returns metadata,
citation, license, instructions, and the tables for a namespacelist_tables(): returns a character vector of available
table identifiersdescribe_table("<name>"): returns metadata,
citation, partition and sort keys, and column definitions for a
tableload_table("<name>"): returns a lazily-evaluated
data frame for a tabledplyr verbs (filter, select)
can be used on the data frame returned by load_table to
push down row filters and column selections to the storage backend.
Filtering on the partition columns returned by
describe_table gives the fastest reads.
Install from CRAN:
install.packages("bedrockbio")Or install the current development version from GitHub:
# install.packages("pak")
pak::pak("bedrock-bio/bedrock-bio/r")Load the package (and dplyr for downstream data frame
manipulation):
library(bedrockbio)
library(dplyr)List available tables:
list_tables()Describe a table to see its metadata, citation, and columns:
describe_table("ukb_ppp.pqtls")Lazily load a table, filter on partition columns (for fastest reads), select columns, and collect the relevant subset into an in-memory data frame:
df <- load_table("ukb_ppp.pqtls") |>
filter(
ancestry == "EUR",
protein_id == "A0FGR8",
panel == "Inflammation"
) |>
select(
chromosome,
position,
effect_allele,
other_allele,
beta,
neg_log_10_p_value
) |>
collect()To request the addition of a new table to the library, open an issue.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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