This package is born out of my own frustration to automate the genomic data retrieval process to create computationally reproducible scripts for large-scale genomics studies. Since I couldn’t find easy-to-use and fully reproducible software libraries I sat down and tried to implement a framework that would enable anyone to automate and standardize the genomic data retrieval process. I hope that this package is useful to others as well and that it helps to promote reproducible research in genomics studies.
I happily welcome anyone who wishes to contribute to this project :) Just drop me an email.
Please find a detailed documentation here.
biomartr in my spare time and would be very grateful if you could cite the following paper in case
biomartr was useful for your own research. I plan on vastly extending the biomartr functionality and usability in the next years to facilitate reproducible genomics research and require citations to back up these efforts. Many thanks in advance :)
Drost HG, Paszkowski J. Biomartr: genomic data retrieval with R. Bioinformatics (2017) 33(8): 1216-1217. doi:10.1093/bioinformatics/btw821.
The vastly growing number of sequenced genomes allows us to perform a new type of biological research. Using a comparative approach these genomes provide us with new insights on how biological information is encoded on the molecular level and how this information changes over evolutionary time.
The first step, however, of any genome based study is to retrieve genomes and their annotation from databases. To automate the retrieval process of this information on a meta-genomic scale, the
biomartr package provides interface functions for genomic sequence retrieval and functional annotation retrieval. The major aim of
biomartr is to facilitate computational reproducibility and large-scale handling of genomic data for (meta-)genomic analyses. In addition,
biomartr aims to address the
genome version crisis. With
biomartr users can now control and be informed about the genome versions they retrieve automatically. Many large scale genomics studies lack this information and thus, reproducibility and data interpretation become nearly impossible when documentation of genome version information gets neglected.
biomartr automates genome, proteome, CDS, RNA, Repeats, GFF/GTF (annotation), genome assembly quality, and metagenome project data retrieval from the major biological databases such as
ENSEMBLGENOMESwere joined - see details here)
Furthermore, an interface to the
Ensembl Biomart database allows users to retrieve functional annotation for genomic loci using a novel and organism centric search strategy. In addition, users can download entire databases such as
with only one command.
The main difference between the BiomaRt package and the biomartr package is that
biomartr extends the
functional annotation retrieval procedure of
BiomaRt and in addition provides useful retrieval functions for genomes, proteomes, coding sequences, gff files, RNA sequences, Repeat Masker annotations files, and functions for the retrieval of entire databases such as
NCBI nr etc.
Please consult the Tutorials section for more details.
In the context of functional annotation retrieval the
biomartr package allows users to screen available marts using only the scientific name of an organism of interest instead of first searching for marts and datasets which support a particular organism of interest (which is required when using the
BiomaRt package). Furthermore,
biomartr allows you to search for particular topics when searching for attributes and filters. I am aware that the similar naming of the packages is unfortunate, but it arose due to historical reasons (please find a detailed explanation here: https://github.com/ropensci/biomartr/blob/master/FAQs.md and here #11).
I also dedicated an entire vignette to compare the
biomartr package functionality in the context of
Functional Annotation (where their functionality overlaps which comprises about only 20% of the overall functionality of the biomartr package).
I truly value your opinion and improvement suggestions. Hence, I would be extremely grateful if you could take this 1 minute and 3 question survey (https://goo.gl/forms/Qaoxxjb1EnNSLpM02) so that I can learn how to improve
biomartrin the best possible way. Many many thanks in advance.
biomartr package relies on some Bioconductor tools and thus requires installation of the following packages:
# Install core Bioconductor packages if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install() # Install package dependencies BiocManager::install("Biostrings") BiocManager::install("biomaRt")
Now users can install
biomartr from CRAN:
# install biomartr 0.9.2 install.packages("biomartr", dependencies = TRUE)
The automated retrieval of collections (= Genome, Proteome, CDS, RNA, GFF, Repeat Masker, AssemblyStats files) will make sure that the genome file of an organism will match the CDS, proteome, RNA, GFF, etc file and was generated using the same genome assembly version. One aspect of why genomics studies fail in computational and biological reproducibility is that it is not clear whether CDS, proteome, RNA, GFF, etc files used in a proposed analysis were generated using the same genome assembly file denoting the same genome assembly version. To avoid this seemingly trivial mistake we encourage users to retrieve genome file collections using the
getCollection() and attach the corresponding output as Supplementary Data to the respective genomics study to ensure computational and biological reproducibility.
# download collection for Saccharomyces cerevisiae biomartr::getCollection( db = "refseq", organism = "Saccharomyces cerevisiae")
getCollection() function will now generate a folder named
refseq/Collection/Saccharomyces_cerevisiae and will store all genome and annotation files for
Saccharomyces cerevisiae in the same folder. In addition, the exact genoem and annotation version will be logged in the
Internally, a text file named
doc_Saccharomyces_cerevisiae_db_refseq.txt is generated. The information stored in this log file is structured as follows:
File Name: Saccharomyces_cerevisiae_assembly_stats_refseq.txt Organism Name: Saccharomyces_cerevisiae Database: NCBI refseq URL: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF_000146045.2_R64/GCF_000146045.2_R64_assembly_stats.txt Download_Date: Wed Jun 27 15:21:51 2018 refseq_category: reference genome assembly_accession: GCF_000146045.2 bioproject: PRJNA128 biosample: NA taxid: 559292 infraspecific_name: strain=S288C version_status: latest release_type: Major genome_rep: Full seq_rel_date: 2014-12-17 submitter: Saccharomyces Genome Database
In an ideal world this reference file could then be included as supplementary information in any life science publication that relies on genomic information so that reproducibility of experiments and analyses becomes achievable.
Download all mammalian vertebrate genomes from
NCBI RefSeq via:
# download all vertebrate genomes meta.retrieval(kingdom = "vertebrate_mammalian", db = "refseq", type = "genome")
All geneomes are stored in the folder named according to the kingdom. In this case
vertebrate_mammalian. Alternatively, users can specify the
out.folder argument to define a custom output folder path.
biomartralso at OmicTools.
Please find all FAQs here.
I would be very happy to learn more about potential improvements of the concepts and functions provided in this package.
Furthermore, in case you find some bugs or need additional (more flexible) functionality of parts of this package, please let me know:
Getting Started with
Users can also read the tutorials within (RStudio) :
The current status of the package as well as a detailed history of the functionality of each version of
biomartr can be found in the NEWS section.
Some bug fixes or new functionality will not be available on CRAN yet, but in the developer version here on GitHub. To download and install the most recent version of
meta.retrieval(): Perform Meta-Genome Retieval from NCBI of species belonging to the same kingdom of life or to the same taxonomic subgroup
meta.retrieval.all(): Perform Meta-Genome Retieval from NCBI of the entire kingdom of life
getMetaGenomes(): Retrieve metagenomes from NCBI Genbank
getMetaGenomeAnnotations(): Retrieve annotation *.gff files for metagenomes from NCBI Genbank
listMetaGenomes(): List available metagenomes on NCBI Genbank
getMetaGenomeSummary(): Helper function to retrieve the assembly_summary.txt file from NCBI genbank metagenomes
clean.retrieval(): Format meta.retrieval output
listGenomes(): List all genomes available on NCBI and ENSEMBL servers
listKingdoms(): list the number of available species per kingdom of life on NCBI and ENSEMBL servers
listGroups(): list the number of available species per group on NCBI and ENSEMBL servers
getKingdoms(): Retrieve available kingdoms of life
getGroups(): Retrieve available groups for a kingdom of life
is.genome.available(): Check Genome Availability NCBI and ENSEMBL servers
getCollection(): Retrieve a Collection: Genome, Proteome, CDS, RNA, GFF, Repeat Masker, AssemblyStats
getGenome(): Download a specific genome stored on NCBI and ENSEMBL servers
getGenomeSet(): Genome Retrieval of multiple species
getProteome(): Download a specific proteome stored on NCBI and ENSEMBL servers
getProteomeSet(): Proteome Retrieval of multiple species
getCDS(): Download a specific CDS file (genome) stored on NCBI and ENSEMBL servers
getCDSSet(): CDS Retrieval of multiple species
getRNA(): Download a specific RNA file stored on NCBI and ENSEMBL servers
getRNASet(): RNA Retrieval of multiple species
getGFF(): Genome Annotation Retrieval from NCBI (
*.gff) and ENSEMBL (
getGTF(): Genome Annotation Retrieval (
*.gtf) from ENSEMBL servers
getRepeatMasker() :Repeat Masker TE Annotation Retrieval
getAssemblyStats(): Genome Assembly Stats Retrieval from NCBI
getKingdomAssemblySummary(): Helper function to retrieve the assembly_summary.txt files from NCBI for all kingdoms
getMetaGenomeSummary(): Helper function to retrieve the assembly_summary.txt files from NCBI genbank metagenomes
getSummaryFile(): Helper function to retrieve the assembly_summary.txt file from NCBI for a specific kingdom
getENSEMBLInfo(): Retrieve ENSEMBL info file
getGENOMEREPORT(): Retrieve GENOME_REPORTS file from NCBI
read_genome(): Import genomes as Biostrings or data.table object
read_proteome(): Import proteome as Biostrings or data.table object
read_cds(): Import CDS as Biostrings or data.table object
read_gff(): Import GFF file
read_rna(): Import RNA file
read_rm(): Import Repeat Masker output file
read_assemblystats(): Import Genome Assembly Stats File
biomart(): Main function to query the BioMart database
getMarts(): Retrieve All Available BioMart Databases
getDatasets(): Retrieve All Available Datasets for a BioMart Database
getAttributes(): Retrieve All Available Attributes for a Specific Dataset
getFilters(): Retrieve All Available Filters for a Specific Dataset
organismBM(): Function for organism specific retrieval of available BioMart marts and datasets
organismAttributes(): Function for organism specific retrieval of available BioMart attributes
organismFilters(): Function for organism specific retrieval of available BioMart filters
getGO(): Function to retrieve GO terms for a given set of genes
# On Windows, this won't work - see ?build_github_devtools install_github("HajkD/biomartr", build_vignettes = TRUE, dependencies = TRUE) # When working with Windows, first you need to install the # R package: rtools -> install.packages("rtools") # Afterwards you can install devtools -> install.packages("devtools") # and then you can run: devtools::install_github("HajkD/biomartr", build_vignettes = TRUE, dependencies = TRUE) # and then call it from the library library("biomartr", lib.loc = "C:/Program Files/R/R-3.1.1/library")
biomartron a Win 8 laptop: solution ( Thanks to Andres Romanowski )
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.