Contents

1 Basics

1.1 Install regutools

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. regutools is a R package available via Bioconductor. R can be installed on any operating system from CRAN after which you can install regutools by using the following commands in your R session:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
      install.packages("BiocManager")
  }

BiocManager::install("regutools")

## Check that you have a valid Bioconductor installation
BiocManager::valid()

1.2 Required knowledge

regutools is based on many other packages and in particular in those that have implemented the infrastructure needed for dealing with genomic and sequence data. That is, packages like Biostrings that allow you to work with sequences and GenomicRanges for data on genomic coordinates. A regutools user is not expected to deal with those packages directly but will need to be familiar with them to understand the results regutools generates. Furthermore, it’ll be useful for the user to know the syntax of AnnotationHub (Morgan and Shepherd, 2020) in order to query and load the data provided by this package.

If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.

1.3 Asking for help

As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But R and Bioconductor have a steep learning curve so it is critical to learn where to ask for help. The blog post quoted above mentions some but we would like to highlight the Bioconductor support site as the main resource for getting help regarding Bioconductor. Other alternatives are available such as creating GitHub issues and tweeting. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.

1.4 Citing regutools

We hope that regutools will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

## Citation info
citation("regutools")
#> 
#> Chávez J, Barberena-Jonas C, Sotelo-Fonseca JE, Alquicira-Hernandez J,
#> Salgado H, Collado-Torres L, Reyes A (2020). _regutools: an R package
#> for data extraction from RegulonDB_. doi: 10.18129/B9.bioc.regutools
#> (URL: https://doi.org/10.18129/B9.bioc.regutools),
#> https://github.com/comunidadbioinfo/regutools - R package version
#> 1.3.3, <URL: http://www.bioconductor.org/packages/regutools>.
#> 
#> Chávez J, Barberena-Jonas C, Sotelo-Fonseca JE, Alquicira-Hernandez J,
#> Salgado H, Collado-Torres L, Reyes A (2020). "Programmatic access to
#> bacterial regulatory networks with regutools." _Bioinformatics_. doi:
#> 10.1093/bioinformatics/btaa575 (URL:
#> https://doi.org/10.1093/bioinformatics/btaa575), <URL:
#> https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btaa575/5861528>.
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

2 Overview

Escherichia coli K-12 (E. coli) is the best bacterial organism studied to date. Thousands of papers have been published using E. coli as a model system asking how genes are regulated. The throughput of these experiments range from single-gene studies to whole-genome approaches. Twenty years ago, the database RegulonDB started collecting, curating and organizing this information into a centralized resource. Thanks to this huge efforts, researchers have had an easy way to access all these data in a database, facilitating the advancements of complete fields, such as systems biology.

The analysis of high-throughput experiments -such as RNA-seq or ChIP-seq- often requires the integration of databases such as RegulonDB in order to give biological interpretations to these data. The regutools package is designed to facilitate such integration by providing programmatic access to RegulonDB within the R environment (Chávez, Barberena-Jonas, Sotelo-Fonseca, Alquicira-Hernandez, Salgado, Collado-Torres, and Reyes, 2020). The package retrieves information from the RegulonDB database into Bioconductor objects, ready for downstream analyses.

The package defines the object regulondb, which is a data structure that contains the path to a SQLite database retrieved from RegulonDB along with metadata such as database version and reference genome. The function connect_database() will retrieve the latest version of the database and connect to it. It will download the database using AnnotationHub or a backup mechanism if necessary. The regutools package contains functions with the most popular queries to regutools, such as retrieving information of which gene targets are regulated by a transcription factor. But users can also design queries that are specific to their analyses. This vignette describes how to use the provided functions and how to design programmatic queries to regutools. The general syntax of the function calls of this package is result <- functionCalled( regulondb, arguments ).

3 The regulondb object

The regulondb object is an extension of an SQLiteConnection class that host a connection to a database with the table structure defined in the RegulonDB database. It contains additional slots that specify the organism, genome version and database version. The function regulondb() is the constructor function of regulondb objects. This function receives as input a file path to the database file as well as information about the annotation as character vectors.

library("regutools")

## Other packages used
library("Biostrings")

## Connect to the RegulonDB database
regulondb_conn <- connect_database()

## Build a regulondb object
e_coli_regulondb <-
    regulondb(
        database_conn = regulondb_conn,
        organism = "E.coli",
        database_version = "1",
        genome_version = "1"
    )

e_coli_regulondb
#> regulondb object
#>   organism: E.coli
#>   genome_build: 1
#>   database_version: 1
#>   database_conn: /home/biocbuild/.cache/BiocFileCache/3e00b91c6c88cb_regulondb_v10.8_sqlite.db%3Fdl%3D1

In order to get an overview of the tables present in a regulondb object, we can use the function list_datasets(). This function will output all the available tables (datasets) that can be used to build queries.

list_datasets(e_coli_regulondb)
#> [1] "DNA_OBJECTS"       "GENE"              "NETWORK"          
#> [4] "OPERON"            "PROMOTER"          "REGULONDB_OBJECTS"
#> [7] "TF"                "TU"

For each table in the database, users can explore the fields (or attributes) of each table using the function list_attributes.

head(list_attributes(e_coli_regulondb, "GENE"), 8)
#> [1] "id"       "name"     "bnumber"  "gi"       "synonyms" "posleft"  "posright"
#> [8] "strand"

4 Retrieving data

Since the regulondb object is an extension of the SQLiteConnection, users can retrieve data from a regulondb object using the function dbGetQuery(). Additionally, this package provides a wrapper function to build queries to the database. This function is called get_dataset() and has a very similar syntax to the getBM() function from the biomaRt package. The main arguments of the get_dataset() function are a regulondb object, a dataset (or table) of the database, the fields of the dataset to retrieve (attributes) and filters to specify what information to get. The code below shows an example where three attributes from the dataset “GENE” for the genes araC, crp and lacI. Note that if the filters= parameter is empty, the function will retrieve all the hits it find in the database.

get_dataset(
    regulondb = e_coli_regulondb,
    dataset = "GENE",
    attributes = c("posleft", "posright", "strand", "name"),
    filters = list("name" = c("araC", "crp", "lacI"))
)
#> regulondb_result with 3 rows and 4 columns
#>     posleft  posright      strand        name
#>   <integer> <integer> <character> <character>
#> 1     70387     71265     forward        araC
#> 2   3486120   3486752     forward         crp
#> 3    366428    367510     reverse        lacI

Some of the filters, such as posright or posleft, can be filtered by specifying intervals. For example, the code below indicates that all the start positions *posright" should be between position 1 and position 5000 of the genome. The parameter inverval= is used to specify that the filter for that field will be defined by an interval rather than a exact match.

get_dataset(
    e_coli_regulondb,
    attributes = c("posright", "name"),
    filters = list("posright" = c(1, 5000)),
    interval = "posright",
    dataset = "GENE"
)
#> regulondb_result with 3 rows and 2 columns
#>    posright        name
#>   <integer> <character>
#> 1      2799        thrA
#> 2      3733        thrB
#> 3       255        thrL

5 The regulondb_result object and integration into the BioC ecosystem

By default, the function get_dataset() outputs a regulondb_result object, which is an extension of a DataFrame that stores information about the query used to generate this object. This additional information includes the organism name, the database and genome versions, and the table (or dataset) of the regulondb object that was queried by get_dataset().

res <- get_dataset(
    regulondb = e_coli_regulondb,
    dataset = "GENE",
    attributes = c("posleft", "posright", "strand", "name"),
    filters = list("name" = c("araC", "crp", "lacI"))
)
slotNames(res)
#>  [1] "organism"         "genome_version"   "database_version" "dataset"         
#>  [5] "rownames"         "nrows"            "listData"         "elementType"     
#>  [9] "elementMetadata"  "metadata"

To enable integration with other Bioconductor packages, we provide the function convert_to_granges() which converts a regulondb_result object into a GRanges object whenever possible. For example, the result stored in the variable res has genomic coordinates and it is thus possible convert res into a GRanges object.

convert_to_granges(res)
#> GRanges object with 3 ranges and 1 metadata column:
#>       seqnames          ranges strand |        name
#>          <Rle>       <IRanges>  <Rle> | <character>
#>   [1]   E.coli     70387-71265      + |        araC
#>   [2]   E.coli 3486120-3486752      + |         crp
#>   [3]   E.coli   366428-367510      - |        lacI
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

An alternative way to get to the same result is to use the parameter output_format= directly in the function get_dataset().

get_dataset(
    regulondb = e_coli_regulondb,
    dataset = "GENE",
    attributes = c("posleft", "posright", "strand", "name"),
    filters = list("name" = c("araC", "crp", "lacI")),
    output_format = "GRanges"
)
#> GRanges object with 3 ranges and 1 metadata column:
#>       seqnames          ranges strand |        name
#>          <Rle>       <IRanges>  <Rle> | <character>
#>   [1]   E.coli     70387-71265      + |        araC
#>   [2]   E.coli 3486120-3486752      + |         crp
#>   [3]   E.coli   366428-367510      - |        lacI
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

In a similar manner, the function convert_to_biostrings() converts regulondb objects into objects from the Biostrings package. Possible outputs of convert_to_biostrings() are DNAStringSet objects if seq_type="DNA" or a BStringSet if seq_type="product".

res_dnastring <- get_dataset(
    regulondb = e_coli_regulondb,
    dataset = "GENE",
    attributes = c("posleft", "posright", "strand", "name", "dna_sequence"),
    filters = list("name" = c("araC", "crp", "lacI"))
)
res_dnastring <-
    convert_to_biostrings(res_dnastring, seq_type = "DNA")
res_dnastring
#> DNAStringSet object of length 3:
#>     width seq
#> [1]   879 ATGGCTGAAGCGCAAAATGATCCCCTGCTGCCGG...AAAGTGAATGATGTAGCCGTCAAGTTGTCATAA
#> [2]   633 ATGGTGCTTGGCAAACCGCAAACAGACCCGACTC...GGTAAAACCATCGTCGTTTACGGCACTCGTTAA
#> [3]  1083 GTGAAACCAGTAACGTTATACGATGTCGCAGAGT...CGACAGGTTTCCCGACTGGAAAGCGGGCAGTGA
GenomicRanges::mcols(res_dnastring)
#> regulondb_result with 3 rows and 4 columns
#>     posleft  posright      strand        name
#>   <integer> <integer> <character> <character>
#> 1     70387     71265     forward        araC
#> 2   3486120   3486752     forward         crp
#> 3    366428    367510     reverse        lacI
res_prodstring <- get_dataset(
    regulondb = e_coli_regulondb,
    dataset = "GENE",
    attributes = c("posleft", "posright", "strand", "name", "product_sequence"),
    filters = list("name" = c("araC", "crp", "lacI"))
)
res_prodstring <-
    convert_to_biostrings(res_prodstring, seq_type = "product")
mcols(res_prodstring)
#> regulondb_result with 3 rows and 4 columns
#>     posleft  posright      strand        name
#>   <integer> <integer> <character> <character>
#> 1     70387     71265     forward        araC
#> 2   3486120   3486752     forward         crp
#> 3    366428    367510     reverse        lacI

As with the GRanges output mentioned above, it is possible for the output of get_dataset() to be a DNAStringSet object by specifying the parameter output_format="DNAStringSet" or a BStringSet object by specifying output_format="BStringSet". Note that the functions to convert regulondb_result objects will throw errors if there is insufficient information for the coercion to occur. For example, we will get an error if we try to convert into a GRanges object when genomic coordinates are missing from the regulondb_result object.

6 Building your own queries

In the regutools package, we have implemented features that are commonly used when querying data from databases: filtering results by partial matching, filtering by numeric intervals, and building complex queries.

6.1 Partial matching

The code below illustrates the concept of partial matching, in which by setting the parameter partialmatch= to "name", the query returns all the gene name in which the word ara is contained.

get_dataset(
    e_coli_regulondb,
    attributes = c("posright", "name"),
    filters = list("name" = "ara"),
    partialmatch = "name",
    dataset = "GENE"
)
#> regulondb_result with 13 rows and 2 columns
#>      posright        name
#>     <integer> <character>
#> 1       68337        araA
#> 2       70048        araB
#> 3       71265        araC
#> 4       66550        araD
#> 5     2982182        araE
#> ...       ...         ...
#> 9      412481        araJ
#> 10      30799        carA
#> 11    2917813        barA
#> 12    1619957        marA
#> 13     939337        rarA

Note that setting the parameter partialmatch= to NULL will only return genes where the name string is identical to ara.

6.2 Filtering by numeric intervals

In addition to partial matching, queries can be filtered by numeric intervals. For example, in the code below, the parameter interv= is set to "posright". By doing this assignment, we are specifying that the values for "posright" must lie between the values of posright specified in the filter= parameter. Thus, the result of this query will be genes whose right positions lie between the coordinates 2000 and 4000000. Note that the use of the interv= parameter in the code below is equivalent to setting the parameter output_format= to "GRanges" and further subsetting the GRanges object using the function subsetByOverlaps().

get_dataset(
    e_coli_regulondb,
    attributes = c("name", "strand", "posright", "product_name"),
    dataset = "GENE",
    filters = list(posright = c("2000", "4000000")),
    interval = "posright"
)
#> regulondb_result with 3963 rows and 4 columns
#>             name      strand  posright           product_name
#>      <character> <character> <integer>            <character>
#> 1           modB     forward    796551 molybdate ABC transp..
#> 2           cysZ     forward   2532224 sulfate:H<sup>+</sup..
#> 3            dfp     forward   3813951 fused 4'-phosphopant..
#> 4           hisM     reverse   2425233 lysine/arginine/orni..
#> 5           rhsE     forward   1529938 RhsE protein in rhs ..
#> ...          ...         ...       ...                    ...
#> 3959        yqfH     reverse   3033010           protein YqfH
#> 3960        yliM     forward    850397           protein YliM
#> 3961        ynfS     forward   1642211 Qin prophage; protei..
#> 3962        ylcJ     forward    568844           protein YlcJ
#> 3963        sdhX     forward    765150 small regulatory RNA..

6.3 Retrieving genomic elements

Based on genomic coordinates, the code below retrieves all genomic elements whose positions lie between the coordinates provided as a GRanges object. If no aditional parameters are provided, the result will retrieve genes that relies within the first 5000pb from the E. coli genome.

get_dna_objects(e_coli_regulondb)
#> GRanges object with 3 ranges and 4 metadata columns:
#>       seqnames    ranges strand |           id        type        name
#>          <Rle> <IRanges>  <Rle> |  <character> <character> <character>
#>   [1]   E.coli  337-2799      + | ECK120000987        gene        thrA
#>   [2]   E.coli 2801-3733      + | ECK120000988        gene        thrB
#>   [3]   E.coli   190-255      + | ECK120001251        gene        thrL
#>                  description
#>                  <character>
#>   [1] fused aspartate kina..
#>   [2]      homoserine kinase
#>   [3] <i>thr</i> operon le..
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Especific genomic positions can be provided within the parameter grange. It is important to provide a genomic range that covers as minimal the length of one genomic element.

grange <- GenomicRanges::GRanges(
    "chr",
    IRanges::IRanges(5000, 10000)
)
get_dna_objects(e_coli_regulondb, grange)
#> GRanges object with 3 ranges and 4 metadata columns:
#>       seqnames    ranges strand |           id        type        name
#>          <Rle> <IRanges>  <Rle> |  <character> <character> <character>
#>   [1]   E.coli  337-2799      + | ECK120000987        gene        thrA
#>   [2]   E.coli 2801-3733      + | ECK120000988        gene        thrB
#>   [3]   E.coli   190-255      + | ECK120001251        gene        thrL
#>                  description
#>                  <character>
#>   [1] fused aspartate kina..
#>   [2]      homoserine kinase
#>   [3] <i>thr</i> operon le..
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Aditional genomic elements such as “-35 promoter box”, “gene”, “promoter”, “Regulatory Interaction”, “sRNA interaction”, or “terminator” can be selected.

grange <- GenomicRanges::GRanges(
    "chr",
    IRanges::IRanges(5000, 10000)
)
get_dna_objects(e_coli_regulondb, grange, elements = c("gene", "promoter"))
#> GRanges object with 19 ranges and 4 metadata columns:
#>        seqnames    ranges strand |           id        type
#>           <Rle> <IRanges>  <Rle> |  <character> <character>
#>    [1]   E.coli  337-2799      + | ECK120000987        gene
#>    [2]   E.coli 2801-3733      + | ECK120000988        gene
#>    [3]   E.coli   190-255      + | ECK120001251        gene
#>    [4]   E.coli       148      + | ECK120010236    promoter
#>    [5]   E.coli        38      + | ECK125230824    promoter
#>    ...      ...       ...    ... .          ...         ...
#>   [15]   E.coli      3066      + | ECK125230834    promoter
#>   [16]   E.coli      3368      + | ECK125230835    promoter
#>   [17]   E.coli      3396      + | ECK125230836    promoter
#>   [18]   E.coli      3401      + | ECK125230837    promoter
#>   [19]   E.coli      4241      + | ECK125230838    promoter
#>                          name            description
#>                   <character>            <character>
#>    [1]                   thrA fused aspartate kina..
#>    [2]                   thrB      homoserine kinase
#>    [3]                   thrL <i>thr</i> operon le..
#>    [4] thrLp promoter with .. thrLp promoter with ..
#>    [5] TSS_1 promoter with .. TSS_1 promoter with ..
#>    ...                    ...                    ...
#>   [15] TSS_12 promoter with.. TSS_12 promoter with..
#>   [16] TSS_13 promoter with.. TSS_13 promoter with..
#>   [17] TSS_14 promoter with.. TSS_14 promoter with..
#>   [18] TSS_15 promoter with.. TSS_15 promoter with..
#>   [19] TSS_16 promoter with.. TSS_16 promoter with..
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Evenmore, the genomic elements retrieved can be observed in a Genome Browser-like plot. The genomic elements are annotated using a UCSC genome as reference, it is important to provide a valid chromosome name for annotation purpose.

e_coli_regulondb <-
    regulondb(
        database_conn = regulondb_conn,
        organism = "chr",
        database_version = "1",
        genome_version = "1"
    )

grange <- GenomicRanges::GRanges("chr", IRanges::IRanges(5000, 10000))
plot_dna_objects(e_coli_regulondb, grange, elements = c("gene", "promoter"))

6.4 Complex filters

The examples so far have considered queries in which the results are filtered according to single fields from tables. In order to build queries with filters from more than one field, several filters names can be passed as a list to the filters= argument. Additionally the and= argument is used to specify whether the filtering conditions of the result of the query must be satisfied (and=TRUE) or if satisfying a single condition is enough (and=FALSE).

For example, the code below extracts the genes where either name or product_name contain the word Ara or Ara, respectively, if the gene is in the forward strand or if the right position of the gene is between 2000 and 40000000.

nrow(
    get_dataset(
        e_coli_regulondb,
        attributes = c("name", "strand", "posright", "product_name"),
        dataset = "GENE",
        filters = list(
            name = c("ARA"),
            product_name = c("Ara"),
            strand = c("forward"),
            posright = c("2000", "4000000")
        ),
        and = FALSE,
        partialmatch = c("name", "product_name"),
        interval = "posright"
    )
)
#> [1] 2331

The query below, which is identical to the query above except the and= is set to TRUE, returns the genes where all of the specified conditions are satisfied.

nrow(
    get_dataset(
        e_coli_regulondb,
        attributes = c("name", "strand", "posright", "product_name"),
        dataset = "GENE",
        filters = list(
            name = c("ARA"),
            product_name = c("Ara"),
            strand = c("forward"),
            posright = c("2000", "4000000")
        ),
        and = TRUE,
        partialmatch = c("name", "product_name"),
        interval = "posright"
    )
)
#> [1] 3

8 A note about CDSB

This was a project accomplished by members of the Community of Bioinformatics Software Developers (CDSB in Spanish). In part CDSB was formed to help R users in Latin America become R/Bioconductor developers. For more information about CDSB, the CDSB workshops or its online community, please check the CDSB website which is available in both Spanish and English.

9 Reproducibility

The regutools package (Chávez, Barberena-Jonas, Sotelo-Fonseca, Alquicira-Hernandez, Salgado, Collado-Torres, and Reyes, 2020) was made possible thanks to:

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("regutools.Rmd"))

## Extract the R code
library("knitr")
knit("regutools.Rmd", tangle = TRUE)

Date the vignette was generated.

#> [1] "2020-12-18 21:25:30 EST"

Wallclock time spent generating the vignette.

#> Time difference of 51.078 secs

R session information.

#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>  setting  value                                             
#>  version  R Under development (unstable) (2020-11-14 r79432)
#>  os       Ubuntu 20.04.1 LTS                                
#>  system   x86_64, linux-gnu                                 
#>  ui       X11                                               
#>  language (EN)                                              
#>  collate  C                                                 
#>  ctype    en_US.UTF-8                                       
#>  tz       America/New_York                                  
#>  date     2020-12-18                                        
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
#>  package                * version  date       lib source        
#>  AnnotationDbi            1.53.0   2020-12-18 [2] Bioconductor  
#>  AnnotationFilter         1.15.0   2020-12-18 [2] Bioconductor  
#>  AnnotationHub            2.23.0   2020-12-18 [2] Bioconductor  
#>  askpass                  1.1      2019-01-13 [2] CRAN (R 4.1.0)
#>  assertthat               0.2.1    2019-03-21 [2] CRAN (R 4.1.0)
#>  backports                1.2.1    2020-12-09 [2] CRAN (R 4.1.0)
#>  base64enc                0.1-3    2015-07-28 [2] CRAN (R 4.1.0)
#>  Biobase                  2.51.0   2020-12-18 [2] Bioconductor  
#>  BiocFileCache            1.15.1   2020-12-18 [2] Bioconductor  
#>  BiocGenerics           * 0.37.0   2020-12-18 [2] Bioconductor  
#>  BiocIO                   1.1.2    2020-12-18 [2] Bioconductor  
#>  BiocManager              1.30.10  2019-11-16 [2] CRAN (R 4.1.0)
#>  BiocParallel             1.25.1   2020-12-18 [2] Bioconductor  
#>  BiocStyle              * 2.19.1   2020-12-18 [2] Bioconductor  
#>  BiocVersion              3.13.1   2020-12-18 [2] Bioconductor  
#>  biomaRt                  2.47.1   2020-12-18 [2] Bioconductor  
#>  Biostrings             * 2.59.2   2020-12-18 [2] Bioconductor  
#>  biovizBase               1.39.0   2020-12-18 [2] Bioconductor  
#>  bit                      4.0.4    2020-08-04 [2] CRAN (R 4.1.0)
#>  bit64                    4.0.5    2020-08-30 [2] CRAN (R 4.1.0)
#>  bitops                   1.0-6    2013-08-17 [2] CRAN (R 4.1.0)
#>  blob                     1.2.1    2020-01-20 [2] CRAN (R 4.1.0)
#>  bookdown                 0.21     2020-10-13 [2] CRAN (R 4.1.0)
#>  BSgenome                 1.59.2   2020-12-18 [2] Bioconductor  
#>  checkmate                2.0.0    2020-02-06 [2] CRAN (R 4.1.0)
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10 Bibliography

This vignette was generated using BiocStyle (Oleś, Morgan, and Huber, 2020), knitr (Xie, 2014) and rmarkdown (Allaire, Xie, McPherson, et al., 2020) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

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[2] J. Chávez, C. Barberena-Jonas, J. E. Sotelo-Fonseca, et al. “Programmatic access to bacterial regulatory networks with regutools”. In: Bioinformatics (2020). DOI: 10.1093/bioinformatics/btaa575. URL: https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btaa575/5861528.

[3] J. Chávez, C. Barberena-Jonas, J. E. Sotelo-Fonseca, et al. regutools: an R package for data extraction from RegulonDB. https://github.com/comunidadbioinfo/regutools - R package version 1.3.3. 2020. DOI: 10.18129/B9.bioc.regutools. URL: http://www.bioconductor.org/packages/regutools.

[4] G. Csárdi, R. core, H. Wickham, et al. sessioninfo: R Session Information. R package version 1.1.1. 2018. URL: https://CRAN.R-project.org/package=sessioninfo.

[5] Gustavsen, J. A., Pai, et al. “RCy3: Network Biology using Cytoscape from within R”. In: F1000Research (2019). DOI: 10.12688/f1000research.20887.3.

[6] M. Lawrence, W. Huber, H. Pagès, et al. “Software for Computing and Annotating Genomic Ranges”. In: PLoS Computational Biology 9 (8 2013). DOI: 10.1371/journal.pcbi.1003118. URL: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118}.

[7] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.

[8] M. Morgan and L. Shepherd. AnnotationHub: Client to access AnnotationHub resources. R package version 2.23.0. 2020.

[9] K. Müller, H. Wickham, D. A. James, et al. RSQLite: ‘SQLite’ Interface for R. R package version 2.2.1. 2020. URL: https://CRAN.R-project.org/package=RSQLite.

[10] A. Oleś, M. Morgan, and W. Huber. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.19.1. 2020. URL: https://github.com/Bioconductor/BiocStyle.

[11] H. Pagès, P. Aboyoun, R. Gentleman, et al. Biostrings: Efficient manipulation of biological strings. R package version 2.59.2. 2020. URL: https://bioconductor.org/packages/Biostrings.

[12] H. Pagès, M. Carlson, S. Falcon, et al. AnnotationDbi: Manipulation of SQLite-based annotations in Bioconductor. R package version 1.53.0. 2020. URL: https://bioconductor.org/packages/AnnotationDbi.

[13] H. Pagès, M. Lawrence, and P. Aboyoun. S4Vectors: Foundation of vector-like and list-like containers in Bioconductor. R package version 0.29.6. 2020. URL: https://bioconductor.org/packages/S4Vectors.

[14] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2020. URL: https://www.R-project.org/.

[15] R Special Interest Group on Databases (R-SIG-DB), H. Wickham, and K. Müller. DBI: R Database Interface. R package version 1.1.0. 2019. URL: https://CRAN.R-project.org/package=DBI.

[16] L. Shepherd and M. Morgan. BiocFileCache: Manage Files Across Sessions. R package version 1.15.1. 2020.

[17] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.

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