Contents

This package and the underlying code are distributed under the Artistic license 2.0. You are free to use and redistribute this software.

1 Rationale

“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” source: ENCODE Projet Portal.

However, retrieving and downloading data can be time consuming using the current web portal, especially when multiple files from different experiments are involved.

This package has been designed to facilitate access to ENCODE data by compiling the metadata associated with files, experiments, datasets, biosamples, and treatments.

We implemented time-saving features to select ENCODE files by querying their metadata, downloading them and validating that the file was correctly downloaded.

This vignette will introduce the main features of the ENCODExplorer package.

2 Loading the ENCODExplorer package

library(ENCODExplorer)

3 Introduction

To use the functionalities of the ENCODExplorer package, you must first download the data.table containing all of the ENCODE metadata.

This data.table is available through the AnnotationHub package. For convenience, the latest available version at each release will be downloaded and used by default.

We also provide the following function to quickly obtain these metadata:

To load getencode_df :

encode_df <- get_encode_df()
## using temporary cache /tmp/Rtmp56pXqJ/BiocFileCache
## snapshotDate(): 2020-10-26
## downloading 1 resources
## retrieving 1 resource
## loading from cache

4 Main functions

4.1 Query

The queryEncode function allows the user to find the subset of files corresponding to a precise query defined according to the following criteria :

Parameter Description
set_accession The accession for the containing experiment or dataset
dataset_accession There is a subtle difference between the parameters set_accession and dataset_accession. In fact, some files can be part of an experiment, a dataset or both. When using set_accession, you will get all the files directly associated with this accession (experiment and/or dataset). While the usage of dataset_accession will get the files directly associated to the requested dataset AND those which are part of an experiment and indirectly linked to a dataset (reported as related files in the dataset and related_dataset in the experiment).
file_accession The accesion for one specific file
biosample_name The biosample name (“GM12878”, “kidney”)
biosample_type The biosample type (“tissue”, “cell line”)
assay The assay type (“ChIP-seq”, “polyA plus RNA-seq”)
file_format The file format. Some currently available formats include bam, bed, fastq, bigBed, bigWig, CEL, csfasta, csqual, fasta, gff, gtf, idat, rcc, sam, tagAlign, tar, tsv, vcf, wig.
lab The laboratory
organism The donor organism (“Homo sapiens”, “Mus musculus”)
target The gene, protein or histone mark which was targeted by the assay (Immunoprecipitated protein in ChIP-seq, knocked-down gene in CRISPR RNA-seq assays, etc)
treatment The treatment related to the biosample
project The project name/id

By default, the query function uses exact string matching to perform the selection of the relevant entries. This behavior can be changed by modifying the fixed or fuzzy parameters. Setting fixed to FALSE will perform case-insensitive regular expression matching. Setting fuzzy to TRUE will retrieve search results where the query string is a partial match.

The result set is a subset of the encode_df_lite table.

For example, to select all fastq files originating from assays on the MCF-7 (human breast cancer) cell line:

query_results <- queryEncode(organism = "Homo sapiens", 
                      biosample_name = "MCF-7", file_format = "fastq",
                      fixed = TRUE)
## Results : 811 files, 233 datasets

The same request with approximate spelling of the biosample name will return no results:

query_results <- queryEncode(organism = "Homo sapiens", biosample_name = "mcf7",
                        file_format = "fastq", fixed = TRUE,
                        fuzzy = FALSE)
## No result found in encode_df. You can try the <searchEncode> function or set the fuzzy option to TRUE.

However, if you follow the warning guidance and set the fuzzy parameter to TRUE:

query_results <- queryEncode(organism = "Homo sapiens",
                    biosample_name = "mcf7", file_format = "fastq",
                    fixed = TRUE, fuzzy = TRUE)
## Results : 811 files, 233 datasets

You can also perform matching through regular expressions by setting fixed to FALSE.

query_results <- queryEncode(assay = ".*RNA-seq",
                    biosample_name = "HeLa-S3", fixed = FALSE)
## Results : 318 files, 11 datasets
table(query_results$assay)
## 
## polyA minus RNA-seq  polyA plus RNA-seq       small RNA-seq 
##                  90                 150                  78

Finally, the queryEncodeGeneric function can be used to perform searches on columns which are not part of the queryEncode interface but are present within the encode_df_lite data.table:

query_results <- queryEncodeGeneric(biosample_name="HeLa-S3",
                    assay="RNA-seq", submitted_by="Diane Trout",
                    fuzzy=TRUE)
## Results : 54 files, 2 datasets
table(query_results$submitted_by)
## 
## Diane Trout 
##          54

These criteria correspond to the filters that you can find on ENCODE portal:

results of a filtered search on ENCODE portal

results of a filtered search on ENCODE portal

4.2 fuzzySearch

This function is a more user-friendly version of queryEncode that also perform searches on the encode_df_lite object. The character vector or the list of characters specified by the user will be searched for in every column of the database. The user can also constrain the query by selecting the specific columns in which to search for the query term by using the filterVector parameter.

The following request will produce a data.table with every files containing the term brca.

fuzzy_results <- fuzzySearch(searchTerm = c("brca"))
## Results: 236 files, 7 datasets

Multiple terms can be searched simultaneously. This example extracts all files containing brca or ZNF24 within the target column.

fuzzy_results <- fuzzySearch(searchTerm = c("brca", "ZNF24"),
                             filterVector = c("target"),
                             multipleTerm = TRUE)
## Results: 710 files, 17 datasets

When searching for multiple terms, three type of input can be passed to the searchTerm parameter : - A single character where the various terms are separated by commas - A character vector - A list of characters

4.4 createDesign

This function organizes the data.table created by fuzzySearch, queryEncode or searchToquery. It extracts the replicate and control files within a dataset.

It creates a data.table with the file accessions, the dataset accessions and numeric values associated with the nature of the file (1:replicate / 2:control) when the format parameter is set to long.

By setting the format parameter to wide, each dataset will have its own column as illustrated below.

Wide design example

Wide design example

4.5 downloadEncode

downloadEncode allows a user to download a file or an entire dataset. Downloading files can be done by providing a vector of file accessions or dataset accessions (represented by the accession column in get_encode_df()) to the file_acc parameter. This parameter can also be the data.table created by queryEncode, fuzzySearch, searchToquery or createDesign.

If the accession doesn’t exist within the passed-in get_encode_df() database, downloadEncode will search for the accession directly within the ENCODE database. The path to the download directory can be specified (default: /tmp).

To ensure the integrity of each file, the md5 sum of each downloaded file is compared to the reported md5 sum in ENCODE.

Moreover, if the accession is a dataset accession, the function will download each file in this dataset. The format option, which is set by default to all, enables the downloading of a specific format.

Here is a small example query:

query_results <- queryEncode(assay = "switchgear", target ="elavl1", fixed = FALSE)
## Results : 2 files, 1 datasets

And its equivalent search:

search_results <- searchEncode(searchTerm = "switchgear elavl1", limit = "all")
## results : 1

To select a particular file format you can:

  1. add filters to your query and then run the downloadEncode function.
query_results <- queryEncode(assay = "switchgear", target ="elavl1",
                             file_format = "bed" , fixed = FALSE)
downloadEncode(query_results)
  1. specify the format to the downloadEncode function.
downloadEncode(search_results, format = "bed")

4.6 Conversion

The function searchToquery enables the conversion of the results of searchEncode to a queryEncode output based on the accession numbers. The user can then benefit from all the collected metadata and the createDesign function.

The structure of the result set is similar to the get_encode_df() structure.

Let’s try it with the previous example :

  1. search
search_results <- searchEncode(searchTerm = "switchgear elavl1", limit = "all")
## results : 1
  1. convert
convert_results <- searchToquery(searchResults = search_results)

4.7 shinyEncode

This function launches the shinyApp of ENCODExplorer that implements the fuzzySearch and queryEncode search functions. It also allows the creation of a design to organize and download specific files with the downloadEncode function. The Search tab of shinyEncode uses the fuzzySearch function for a low specificity request while the Advanced Search tab uses the queryEncode function.

Simple request using Search

Simple request using Search

5 Summarizing ENCODE data

While queryEncode, searchEncode and downloadEncode gives the user access to ENCODE’s raw files, ENCODExplorer also provides helper functions which load and summarize ENCODE data for common biological questions.

5.1 Obtaining consensus peaks from ChIP-Seq

The most common question in a ChIP-Seq assay is: “Where does the protein of interest bind the genome?” To answer this question, ENCODExplorer provides the queryConsensusPeaks method. queryConsensusPeaks finds all ChIP-seq peak files matching the given criteria, split them by treatment group, and builds a set of “consensus peaks”. The “consensus peaks” identified by ENCODExplorer are those that appear in all replicates of a given group. Two peaks are considered to belong to the same binding event if at least one of their nucleotides overlap.

# Obtain a summary of all peaks for CTCF ChIP-Seq assays in the 22Rv1
# (human prostate carcinoma) cell line.
res = queryConsensusPeaks("22Rv1", "GRCh38", "CTCF")
## Results : 40 files, 2 datasets
## Found the following output_type: peaks and background as input for IDR, optimal IDR thresholded peaks, conservative IDR thresholded peaks
## Selecting optimal idr thresholded peaks. To choose another output_type, specify it in the 'output_type', argument or set use_interactive to TRUE.
## [1] "Success downloading file : ./ENCFF147YCW.bed.gz"
## [1] "Success downloading file : ./ENCFF730MQM.bed.gz"
## [1] "Files can be found at /tmp/RtmpCa8una/Rbuild46967413fc0a/ENCODExplorer/vignettes"

The list of downloaded files is available through the files() method.

files(res)
##            ENCFF147YCW            ENCFF730MQM 
## "./ENCFF147YCW.bed.gz" "./ENCFF730MQM.bed.gz"

The metadata for those file is available through the file_metadata() method. The file metadata are split according to treatment group:

f_meta = file_metadata(res)
names(f_meta)
## [1] "17β-hydroxy-5α-androstan-3-one;10;nM;4;hour"
## [2] "NA;NA;NA;NA;NA"
f_meta[[1]][,1:5]
##      accession file_accession      file_type file_format file_size
## 1: ENCSR847XGE    ENCFF147YCW bed narrowPeak         bed    1.5 Mb

A data-frame explaining how each treatment group was split is available through the metadata() method:

metadata(res)
##                        treatment treatment_amount treatment_amount_unit
## 1 17β-hydroxy-5α-androstan-3-one               10                    nM
## 2                           <NA>               NA                  <NA>
##   treatment_duration treatment_duration_unit
## 1                  4                    hour
## 2                 NA                    <NA>
##                                   split_group
## 1 17β-hydroxy-5α-androstan-3-one;10;nM;4;hour
## 2                              NA;NA;NA;NA;NA

The list of all peaks identified in individual files are accessed through the peaks() method.

names(peaks(res))
## [1] "17β-hydroxy-5α-androstan-3-one;10;nM;4;hour"
## [2] "NA;NA;NA;NA;NA"
names(peaks(res)[[1]])
## [1] "ENCFF147YCW"
peaks(res)[[1]]
## GRangesList object of length 1:
## $ENCFF147YCW
## GRanges object with 90007 ranges and 6 metadata columns:
##                         seqnames              ranges strand |        name
##                            <Rle>           <IRanges>  <Rle> | <character>
##       [1]                   chr8 118811953-118812652      * |        <NA>
##       [2]                  chr11   77411794-77412493      * |        <NA>
##       [3]                   chr3 156174297-156174996      * |        <NA>
##       [4]                   chr5 155844245-155844944      * |        <NA>
##       [5]                   chrX   78101567-78102266      * |        <NA>
##       ...                    ...                 ...    ... .         ...
##   [90003]                   chr7   43838516-43839567      * |        <NA>
##   [90004]                   chr1 225474579-225475646      * |        <NA>
##   [90005] chr17_GL000205v2_ran..         55936-57017      * |        <NA>
##   [90006]                   chr7 151172179-151173297      * |        <NA>
##   [90007]       chrUn_GL000219v1       124914-126043      * |        <NA>
##               score signalValue    pValue    qValue      peak
##           <numeric>   <numeric> <numeric> <numeric> <integer>
##       [1]       857     17.0273        -1   0.10922       350
##       [2]       788     17.0314        -1   0.10943       350
##       [3]       758     17.0363        -1   0.10953       350
##       [4]       617     17.0412        -1   0.10965       350
##       [5]       592     17.0430        -1   0.10968       350
##       ...       ...         ...       ...       ...       ...
##   [90003]      1000     1955.77        -1    5.1613       513
##   [90004]      1000     1962.42        -1    5.1613       548
##   [90005]      1000     2486.40        -1    5.1613       520
##   [90006]      1000     2517.74        -1    5.1613       528
##   [90007]      1000     3011.03        -1    5.1613       528
##   -------
##   seqinfo: 61 sequences from an unspecified genome; no seqlengths

Finally, the consensus peaks (those who are present in all individual replicates) are accessed through the consensus() method:

names(consensus(res))
## [1] "17β-hydroxy-5α-androstan-3-one;10;nM;4;hour"
## [2] "NA;NA;NA;NA;NA"
consensus(res)
## GRangesList object of length 2:
## $`17β-hydroxy-5α-androstan-3-one;10;nM;4;hour`
## GRanges object with 52988 ranges and 0 metadata columns:
##                         seqnames        ranges strand
##                            <Rle>     <IRanges>  <Rle>
##       [1]                   chr8   71076-71250      *
##       [2]                   chr8 206401-207402      *
##       [3]                   chr8 240248-241146      *
##       [4]                   chr8 265480-266224      *
##       [5]                   chr8 299020-299928      *
##       ...                    ...           ...    ...
##   [52984] chr14_KI270724v1_ran..   31580-32279      *
##   [52985] chr14_GL000009v2_ran.. 157035-157734      *
##   [52986] chr9_KI270719v1_random 147925-148624      *
##   [52987] chr14_KI270726v1_ran..   41635-42264      *
##   [52988] chr1_KI270711v1_random   22862-23263      *
##   -------
##   seqinfo: 61 sequences from an unspecified genome; no seqlengths
## 
## $`NA;NA;NA;NA;NA`
## GRanges object with 55189 ranges and 0 metadata columns:
##                         seqnames        ranges strand
##                            <Rle>     <IRanges>  <Rle>
##       [1]                   chr8   71046-71288      *
##       [2]                   chr8 206321-207382      *
##       [3]                   chr8 229367-230042      *
##       [4]                   chr8 240212-240887      *
##       [5]                   chr8 265493-266220      *
##       ...                    ...           ...    ...
##   [55185] chr9_KI270719v1_random 147868-148543      *
##   [55186] chr9_KI270719v1_random 164211-164886      *
##   [55187] chr14_KI270726v1_ran..   41660-42293      *
##   [55188] chr1_KI270711v1_random   22877-23392      *
##   [55189]       chrUn_KI270743v1   28835-29510      *
##   -------
##   seqinfo: 61 sequences from an unspecified genome; no seqlengths

5.2 Fine-tuning a consensus peaks query

Certain versions of the ENCODE pipeline provide multiple calling algorithms. Also, sometimes multiple labs have performed ChIP-seq experiments on the same tissue and protein, and these results might not be directly comparable. ENCODExplorer uses heuristics to try and determine which set of files will provide the most informative results, but the results of these heuristics might prove unsatisfactory.

In such cases, a user can provide his own set of ENCODE metadata and his own choice of splitting columns using the buildQueryConsensus function. The user can also specify which proportion of individual replicates a peak must appear in to be included in the consensus peaks through the consensus_threshold parameter:

query_results = queryEncodeGeneric(biosample_name="A549", assembly="GRCh38",
                                   file_format="^bed$", output_type="^peaks$", 
                                   treatment_duration_unit="minute",
                                   treatment_duration="(^5$|^10$)", 
                                   target="NR3C1", fixed=FALSE)
## Results : 6 files, 2 datasets

# Obtain a summary of all peaks for NR3C1 ChIP-Seq assays in the A549
# cell line.
res = buildConsensusPeaks(query_results, split_by=c("treatment_duration"), 
                          consensus_threshold=0.5)
## [1] "Success downloading file : ./ENCFF944WPL.bed.gz"
## [1] "Success downloading file : ./ENCFF349VZU.bed.gz"
## [1] "Success downloading file : ./ENCFF424TOY.bed.gz"
## [1] "Success downloading file : ./ENCFF259MMW.bed.gz"
## [1] "Success downloading file : ./ENCFF494KBA.bed.gz"
## [1] "Success downloading file : ./ENCFF404GGW.bed.gz"
## [1] "Files can be found at /tmp/RtmpCa8una/Rbuild46967413fc0a/ENCODExplorer/vignettes"

res
## An object of class ENCODEBindingConsensus.
## Summarizing 6 ENCODE files into 2 categories.
## 
## Metadata:
##   treatment_duration split_group
## 1                 10          10
## 2                  5           5
## 
## Consensus regions:
## GRangesList object of length 2:
## $`10`
## GRanges object with 31431 ranges and 0 metadata columns:
##                         seqnames            ranges strand
##                            <Rle>         <IRanges>  <Rle>
##       [1]                  chr14 20501403-20501711      *
##       [2]                  chr14 20628847-20629251      *
##       [3]                  chr14 20686268-20686794      *
##       [4]                  chr14 20848492-20849570      *
##       [5]                  chr14 20986843-20987131      *
##       ...                    ...               ...    ...
##   [31427]       chrUn_KI270333v1          999-1819      *
##   [31428]       chrUn_KI270333v1         1887-2683      *
##   [31429] chr9_KI270720v1_random         2403-2997      *
##   [31430]       chrUn_KI270336v1          609-1026      *
##   [31431]       chrUn_KI270442v1     391000-391452      *
##   -------
##   seqinfo: 38 sequences from an unspecified genome; no seqlengths
## 
## $`5`
## GRanges object with 21114 ranges and 0 metadata columns:
##                         seqnames            ranges strand
##                            <Rle>         <IRanges>  <Rle>
##       [1]                  chr14 20628896-20629164      *
##       [2]                  chr14 20686395-20686803      *
##       [3]                  chr14 20848565-20848981      *
##       [4]                  chr14 20849138-20849593      *
##       [5]                  chr14 20986851-20987151      *
##       ...                    ...               ...    ...
##   [21110]       chrUn_KI270333v1         2056-2668      *
##   [21111] chr9_KI270720v1_random         2414-2833      *
##   [21112]       chrUn_KI270336v1             1-320      *
##   [21113]       chrUn_KI270336v1          646-1026      *
##   [21114]       chrUn_KI270442v1     391043-391393      *
##   -------
##   seqinfo: 38 sequences from an unspecified genome; no seqlengths

5.3 Obtaining average gene expression

For RNA-Seq experiment, the most straightforward type of results is the expression level of all genes or transcripts. ENCODExplorer provides the queryGeneExpression and queryTranscriptExpression methods to summarize these results. ENCODExplorer finds all gene or transcript expression levels for a given biosample and calculates per-condition mean values.

Most biosamples in the ENCODE Project have RNA-seq experiments targeting different cell fractions, such as whole cells, cytoplasmic fractions, and nuclear fractions. Since it makes no biological sense to aggregate such results, ENCODExplorer automatically splits them by the dataset_description column, which details the cell fraction as well as other methodological or biological parameters which make samples unfit for aggregation.

# Obtain a summary of all peaks for NR3C1 ChIP-Seq assays in the A549
# cell line.
res = queryGeneExpression("bone marrow")
## Results : 2 files, 1 datasets
## Only mm10 was found. Selecting it.
## Only polyA plus RNA-seq was found. Selecting it.
## [1] "Success downloading file : ./ENCFF128MGD.tsv"
## [1] "Success downloading file : ./ENCFF339PKR.tsv"
## [1] "Files can be found at /tmp/RtmpCa8una/Rbuild46967413fc0a/ENCODExplorer/vignettes"

The files(), file_metadata() and metadata() methods behave the same way as they do for queryConsensusPeaks:

metadata(res)
##     treatment treatment_amount treatment_amount_unit treatment_duration
## All      <NA>               NA                  <NA>                 NA
##     treatment_duration_unit
## All                    <NA>

You can see which expression metric ENCODExplorer extracted using the metric() method.

metric(res)
## [1] "^TPM$"

Per gene/transcript values for all metrics are available through the metric_data() method:

head(metric_data(res))
##      id   All
## 1 10000 0.000
## 2 10001 0.000
## 3 10002 0.000
## 4 10003 1.985
## 5 10004 0.000
## 6 10005 0.000

You can also get a list of the raw ENCODE files by calling the raw_data() method.

head(raw_data(res)[[1]][[1]])
##   gene_id transcript_id.s. length effective_length expected_count  TPM FPKM
## 1   10000            10000     72               43              0 0.00 0.00
## 2   10001            10001     73               44              0 0.00 0.00
## 3   10002            10002     73               44              0 0.00 0.00
## 4   10003            10003     75               46              1 3.97 4.24
## 5   10004            10004     78               49              0 0.00 0.00
## 6   10005            10005     73               44              0 0.00 0.00
##   posterior_mean_count posterior_standard_deviation_of_count pme_TPM pme_FPKM
## 1                 0.00                                  0.00    3.69     4.42
## 2                 0.00                                  0.00    3.60     4.32
## 3                 0.00                                  0.00    3.60     4.32
## 4                 0.96                                  0.18    6.78     8.12
## 5                 0.00                                  0.00    3.24     3.88
## 6                 0.00                                  0.00    3.60     4.32
##   TPM_ci_lower_bound TPM_ci_upper_bound FPKM_ci_lower_bound FPKM_ci_upper_bound
## 1        4.03657e-05           10.98300         4.83120e-05             13.1626
## 2        2.14146e-04           10.75490         2.56696e-04             12.8875
## 3        7.29571e-05           10.82350         3.14634e-04             12.9686
## 4        6.52931e-03           16.16710         3.66534e-02             19.4081
## 5        8.06415e-05            9.65181         9.66626e-05             11.5613
## 6        3.69893e-05           10.86470         4.43908e-05             13.0253

5.4 Fine tuning expression summaries

Just as it is the case for ChIP-seq assays, it can sometimes be easier for the user to perform filtering of the ENCODE results manually. For thse cases, ENCODExplorer provides the buildExpressionSummary method.

query_results = queryEncodeGeneric(biosample_name="neural tube", 
                                   output_type="gene quantifications",
                                   file_type="tsv",
                                   assay="polyA plus RNA-seq",
                                   assembly="^mm10$",
                                   dataset_biosample_summary="(15.5|13.5)",
                                   fixed=FALSE)
## Results : 4 files, 2 datasets
                                   
buildExpressionSummary(query_results, split_by="dataset_biosample_summary")                                   
## [1] "Success downloading file : ./ENCFF037GWJ.tsv"
## [1] "Success downloading file : ./ENCFF365DLM.tsv"
## [1] "Success downloading file : ./ENCFF049EIV.tsv"
## [1] "Success downloading file : ./ENCFF502BTV.tsv"
## [1] "Files can be found at /tmp/RtmpCa8una/Rbuild46967413fc0a/ENCODExplorer/vignettes"
## An object of class ENCODEExpressionSummary.
## Summarizing 4 ENCODE files into 2 categories.
## 
## Metadata:
##                dataset_biosample_summary                            split_group
## 1 C57BL/6 neural tube embryo (15.5 days) C57BL/6 neural tube embryo (15.5 days)
## 2 C57BL/6 neural tube embryo (13.5 days) C57BL/6 neural tube embryo (13.5 days)
## 
## Sumarizing 69691 gene expression levels.

5.5 Interactive mode

queryConsensusPeaks, queryGeneExpression and queryTranscriptExpression all make educated guesses about the assembly, assay and sample types to be used for generating the summaries. However, by setting the use_interactive argument to TRUE, a user can take direct control of some these choices.

queryGeneExpression("neural tube", use_interactive=TRUE)

6 Updating the ENCODE file database

By default, ENCODExplorer retrieves the ENCODE metadata from its sister package, ENCODExplorerData. The version of the metadata provided by default will be updated with each Bioconductor release in the ENCODExplorer package. However, since all of ENCODExplorer’s function take an explicit df parameter, it is possible to use the AnnotationHub package to download a more recent version:

require(AnnotationHub)
ah = AnnotationHub()
query(ah, "ENCODExplorerData")
## AnnotationHub with 4 records
## # snapshotDate(): 2020-10-26
## # $dataprovider: ENCODE Project
## # $species: NA
## # $rdataclass: data.table
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## #   rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH69290"]]' 
## 
##             title                                         
##   AH69290 | ENCODE File Metadata (Light, 2019-04-12 build)
##   AH69291 | ENCODE File Metadata (Full, 2019-04-12 build) 
##   AH75131 | ENCODE File Metadata (Light, 2019-10-13 build)
##   AH75132 | ENCODE File Metadata (Full, 2019-10-13 build)

Finally, it is also possible to use ENCODExplorerData functionalities to generate an up-to-date data.table, and pass it to ENCODExplorer’s functions.

We refer the user to the ENCODExplorerData vignettes for details on how to generate an up-to-date data.table.