This function performs a DIAMOND2 search (best hit) of a given set of protein sequences against a given database.
diamond_best(
query_file,
subject_file,
seq_type = "cds",
format = "fasta",
diamond_algorithm = "blastp",
sensitivity_mode = "fast",
delete_corrupt_cds = TRUE,
eval = "1E-5",
max.target.seqs = 10000,
path = NULL,
comp_cores = 1,
diamond_params = NULL,
clean_folders = FALSE,
save.output = NULL
)a character string specifying the path to the CDS file of interest (query organism).
a character string specifying the path to the CDS file of interest (subject organism).
a character string specifying the sequence type stored in the input file.
Options are are: "cds", "protein", or "dna". In case of "cds", sequence are translated to protein sequences,
in case of "dna", cds prediction is performed on the corresponding sequences which subsequently are
translated to protein sequences. Default is seq_type = "cds".
a character string specifying the file format of the sequence file, e.g. format = "fasta".
Default is format = "fasta".
a character string specifying the DIAMOND2 algorithm that shall be used, option is currently limited to: diamond_algorithm = "blastp"
specify the level of alignment sensitivity. The higher the sensitivity level, the more deep homologs can be found, but at the cost of reduced computational speed. - sensitivity_mode = "faster" : fastest alignment mode, but least sensitive (default). Designed for finding hits of >70 - sensitivity_mode = "default" : Default mode. Designed for finding hits of >70 - sensitivity_mode = "fast" : fast alignment mode, but least sensitive (default). Designed for finding hits of >70 - sensitivity_mode = "mid-sensitive" : fast alignments between the fast mode and the sensitive mode in sensitivity. - sensitivity_mode = "sensitive" : fast alignments, but full sensitivity for hits >40 - sensitivity_mode = "more-sensitive" : more sensitive than the sensitive mode. - sensitivity_mode = "very-sensitive" : sensitive alignment mode. - sensitivity_mode = "ultra-sensitive" : most sensitive alignment mode (sensitivity as high as BLASTP).
a logical value indicating whether sequences with corrupt base triplets should be removed from the input file. This is the case when the length of coding sequences cannot be divided by 3 and thus the coding sequence contains at least one corrupt base triplet.
a numeric value specifying the E-Value cutoff for DIAMOND2 hit detection.
a numeric value specifying the number of aligned sequences to keep.
Please be aware that max.target.seqs selects best hits based on the database entry and not by the best e-value. See details here: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty833/5106166 .
a character string specifying the path to the DIAMOND2 program (in case you don't use the default path).
a numeric value specifying the number of cores to be used for multicore DIAMOND2 computations.
a character string listing the input paramters that shall be passed to the executing DIAMOND2 program. Default is NULL, implicating
that a set of default parameters is used when running DIAMOND2.
a boolean value spefiying whether all internall folders storing the output of used programs
shall be removed. Default is clean_folders = FALSE.
a path to the location were the DIAMOND2 output shall be stored. E.g. save.output = getwd()
to store it in the current working directory, or save.output = file.path(put,your,path,here).
A data.table as returned by the diamond function, storing the geneids
of orthologous genes (best hit) in the first column and the amino acid sequences in the second column.
Given a set of protein sequences (query sequences), a best hit diamond search (BH DIAMOND2) is being performed.
Internally to perform best hit searches, the DIAMOND2 parameter settings:
"-best_hit_score_edge 0.05 -best_hit_overhang 0.25 -max_target_seqs 1"
are used to speed up best hit computations.
This function gives the same output as blast_best while being much much faster.
Buchfink, B., Reuter, K., & Drost, H. G. (2021) "Sensitive protein alignments at tree-of-life scale using DIAMOND." Nature methods, 18(4), 366-368.
https://github.com/bbuchfink/diamond/wiki/3.-Command-line-options
if (FALSE) {
# performing gene orthology inference using the best hit (BH) method
diamond_best(query_file = system.file('seqs/ortho_thal_cds.fasta', package = 'orthologr'),
subject_file = system.file('seqs/ortho_lyra_cds.fasta', package = 'orthologr'))
# performing gene orthology inference using the best hit (BH) method starting with protein sequences
diamond_best(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_file = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein")
# save the DIAMOND2 output file to the current working directory
diamond_best(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_file = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
save.output = getwd())
# use multicore processing
diamond_best(query_file = system.file('seqs/ortho_thal_cds.fasta', package = 'orthologr'),
subject_file = system.file('seqs/ortho_lyra_cds.fasta', package = 'orthologr'),
comp_cores = 2)
# performing gene orthology inference using the best hit (BH) method and external
# diamond path
diamond_best(query_file = system.file('seqs/ortho_thal_cds.fasta', package = 'orthologr'),
subject_file = system.file('seqs/ortho_lyra_cds.fasta', package = 'orthologr'),
path = "path/to/diamond/")
}