A BLAST interface with R


The exponentially growing number of available sequences in biological databases revolutionizes the way modern life science research is conducted. Approximately hundred thousand genomic sequences spanning diverse species from the tree of life are currently publically available and free to access. It is now possible to access and retrieve this data automatically using the R package biomartr and the next step is to harness this wealth of sequence diversity to explore and detect novel patterns of evolvability, variation, and disease emergence.

The R package biomartr solves the problem of retrieving this vast amount of biological sequence data in a standardized and computationally reproducible way and the metablastr package aims to solve the problem of performing massive scale sequence searches in a standardized and computationally reproducible way.

Both packages, biomartr and metablastr are designed to complement each other seamlessly to provide users with a toolset to automatically retrieve thousands of biological sequences (thousands of genomes, proteomes, annotations, etc) and to use these sequences to perform massive sequence searches with BLAST to extract novel patterns of similarity and divergence between large sets of species.

The most prominent tool to perform sequence searches at scale is the Basic Local Alignment Search Tool (BLAST) which is designed to find regions of sequence similarity between query and a subject sequences or sequence databases.

Short package description

The metablastr package harnesses the power of BLAST by providing interface functions between R and the standalone (command line tool) version of this program.

Together, the metablastr package may enable a new level of data-driven genomics research by providing the computational tools and data science standards needed to perform reproducible research at scale.


The metablastr package is still under development and not formally published yet. However, we did develop parts of metablastr for this Methods chapter which you can cite until a metablastr specific manuscript is prepared.

M Benoit, HG Drost. A Predictive Approach to Infer the Activity and Natural Variation of Retrotransposon Families in Plants. In: Cho J. (eds) Plant Transposable Elements. Methods in Molecular Biology, vol 2250. Humana, New York, NY (2021).

Install metablastr

For Linux Users:

Please install the libpq-dev library on you linux machine by typing into the terminal:

sudo apt-get install libpq-dev

For all systems install metablastr by typing

if (!requireNamespace("BiocManager", quietly = TRUE))

BiocManager::install(c("Biostrings", "GenomicFeatures", "GenomicRanges", "Rsamtools", "IRanges", "rtracklayer", "biomaRt"))

# install.packages("devtools")
# install the current version of metablastr on your system
devtools::install_github("HajkD/metablastr", build_vignettes = TRUE, dependencies = TRUE)

Please follow the Installation Vignette to install all standalone sequence search tools.

Quick start

# run blastn (nucleotide to nucleotide search) between example query and subject sequences
blast_test <- blast_nucleotide_to_nucleotide(
                 query   = system.file('seqs/qry_nn.fa', package = 'metablastr'),
                 subject = system.file('seqs/sbj_nn.fa', package = 'metablastr'),
                 output.path = tempdir(),
                 db.import  = FALSE)
# look at BLAST results
   query_id     subject_id perc_identity num_ident_match… alig_length mismatches
   <chr>        <chr>              <dbl>            <int>       <int>      <int>
 1 333554|PACi… AT1G01010…          84.2              640         760         63
 2 333554|PACi… AT1G01010…          84.0              536         638         90
 3 333554|PACi… AT1G01010…          78.6               44          56         12
 4 470181|PACi… AT1G01020…          94.7              699         738         39
 5 470180|PACi… AT1G01030…          95.2             1025        1077         40
 6 333551|PACi… AT1G01040…          96.0             3627        3779        125
 7 333551|PACi… AT1G01040…          95.5             1860        1948         82
 8 909874|PACi… AT1G01050…          96.6              617         639         22
 9 470177|PACi… AT1G01060…          92.8             1804        1944        110
10 918864|PACi… AT1G01070…          95.3             1046        1098         40
# ... with 13 more rows, and 15 more variables: gap_openings <int>,
#   n_gaps <int>, pos_match <int>, ppos <dbl>, q_start <int>, q_end <int>,
#   q_len <int>, qcov <int>, qcovhsp <int>, s_start <int>, s_end <dbl>,
#   s_len <dbl>, evalue <dbl>, bit_score <chr>, score_raw <int>

Discussions and Bug Reports

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, need additional (more flexible) functionality of parts of this package, or want to contribute to this project please let me know:


Interfaces implemented in metablastr:

Perform BLAST searches

BLAST against common NCBI databases

  • blast_protein_to_nr_database(): Perform Protein to Protein BLAST Searches against the NCBI non-redundant database
  • blast_nt(): Perform Nucleotide to Nucleotide BLAST Searches against the NCBI non-redundant database
  • blast_est(): Perform Nucleotide to Nucleotide BLAST Searches against the NCBI expressed sequence tags database
  • blast_pdb_protein():
  • blast_pdb_nucleotide():
  • blast_swissprot():
  • blast_delta():
  • blast_refseq_rna():
  • blast_refseq_gene():
  • blast_refseq_protein():

BLAST against a set of organisms

Analyze BLAST Report

  • filter_blast_: