Audrey Lemacon, Charles Joly Beauparlant and Arnaud Droit.
This package and the underlying ENCODExplorer code are distributed under the Artistic license 2.0. You are free to use and redistribute this software.
"The ENCODE (Encyclopedia of DNA Elements) Consortium is an international collaboration of research groups funded by the National Human Genome Research Institute (NHGRI). The goal of ENCODE is to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active" ^[1] .
However, data retrieval and downloading can be really time consuming using current web portal, especially with multiple files from different experiments.
This package has been designed to facilitate the data access by compiling the metadata associated with file, experiment, dataset, biosample, and treatment.
We first extract ENCODE schema from its public github repository to rebuild the ENCODE database into an SQLite database. Thanks to this package, the user will be enable to generate, store and query ENCODE database locally. We also developped a function which can extract the essential metadata in a R object to aid data exploration.
We implemented time-saving features to select ENCODE files by querying their metadata, downloading them and validating that the file was correctly downloaded.
The SQLite database can be regenerated at will to keep it up-to-date.
This vignette will introduce all the main features of the ENCODExplorer package.
suppressMessages(library(ENCODExplorer))
This package comes with a up-to-date list of data.frame
containing the essential
of ENCODE files metadata: encode_df
. This list contains two elements.
The first one encode_df$experiment
is a data.frame
containing information for
each file part of an experiment ; the second one encode_df$dataset
is a
data.frame
containing information for each file part of a dataset.
This encode_df
is mandatory for the functions provided in this package.
The queryEncode
function allow the user to find the subset of files corresponding to
a precise query defined according to the following criteria :
|parameter|available for| description| |---------|------------|-------------| |set_accession|experiment / dataset|The experiment or dataset accession| |assay|experiment|The assay type| |biosample|experiment|The biosample name| |dataset_access^[2] |experiment / dataset|The dataset accession| |file_accession|experiment / dataset|The file accesion| |file_format|experiment / dataset|The file format| |lab|experiment / dataset|The laboratory| |organism|experiment|The donor organism| |target|experiment|The experimental target| |treatment|experiment|The treatment|
By default, the query use the exact string matching to perform the selection of
the relevant entries. This behavior can be changed by setting the fixed
option
to FALSE.
The structure of the result set is similar to the encode_df
structure : a list
of two elements experiment and dataset.
For example, to select all the fastq files produced by RNA-seq assay on human cell MCF-7:
query_results <- queryEncode(assay = "RNA-seq", organism = "Homo sapiens", biosample = "MCF-7", file_format = "fastq", fixed = TRUE)
The same request with approximate spelling of the assay type and fixed
option
to TRUE
, will give no results :
query_results <- queryEncode(assay = "rnaseq", organism = "Homo sapiens", biosample = "MCF-7", file_format = "fastq", fixed = TRUE)
If you follow the warning guidance and set the fixed
option to `FALSE``:
query_results <- queryEncode(assay = "rnaseq", organism = "Homo sapiens", biosample = "MCF-7", file_format = "fastq", fixed = FALSE)
These criteria correspond to the filters that you can find on ENCODE portal :
Note: the usage of some criteria, like organism or target, will automatically dismiss the dataset results because this information isn't available for that type of data.
This function simulates a key word search that you could perform through the ENCODE web portal.
The searchEncode
function returns a data frame which corresponds to the result page
provided by ENCODE portal.
Here is the example of the following search : "a549 chip-seq homo sapiens".
On ENCODE portal :
With our function :
search_results <- searchEncode(searchTerm = "a549 chip-seq homo sapiens", limit = "all")
Following a search or a query, you may want to download the corresponding files. When using, the ENCODE portal you have to select the experiments one after an other and for each experiment, select manually the files you're interested in.
Our downloadEncode
function is a real time saving feature. To use it, you have to
provide the results set that you've just get from the searchEncode
or queryEncode
function, indicate the origin of the dataset ("searchEncode" or "queryEncode") and then the
path to the directory where you want to copy the downloaded files
(default: /tmp
).
To ensure that the downloading have succeeded, we conduct a check md5 sum comparison for each file.
Moreover, if your results set stem from the searchEncode
function, you may want to
restrict the downloading to a certain type of file. To do so, you can set the
format
option (which is set by defaul to all
)
Here is a small query:
query_results <- queryEncode(assay = "switchgear", target ="elavl1", fixed = FALSE)
And its equivalent search:
search_results <- searchEncode(searchTerm = "switchgear elavl1", limit = "all")
To select a particular file format you can:
1) add this filter to your query and then run the downloadEncode
function
query_results <- queryEncode(assay = "switchgear", target ="elavl1", file_format = "bed_broadPeak" , fixed = FALSE) downloadEncode(resultSet = query_results, resultOrigin = "queryEncode")
2) specify the format to the downloadEncode
function
downloadEncode(resultSet = search_results, resultOrigin = "searchEncode", format = "bed_broadPeak")
[1]: source: ENCODE Projet Portal
[2]: There is a subtle difference between the parameters set_accession and dataset_accession. In fact, some files can be part of experiment, dataset or both. When using set_accession, you will get all the files directly linked with this accession (experiment and/or dataset). While the usage of dataset_accesstion will get the files directly link to the requested dataset AND those which are part of an experiment and indirectly link to a dataset (reported as related_files in the dataset and related_dataset in experiment).
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