vignettes/orthology_inference.Rmd
orthology_inference.RmdOrthology Inference is the process of detecting orthologous genes between a
query organism and a set of subject organisms. Motivated by the
discussion on which orthology inference method, paradigm or program is
the most accurate or the fastest one,
the orthologr package provides functions to perform genome
wide orthology inference following two different paradigms:
orthology inference based on 1-1 orthology relationship using
BLAST reciprocal best hitorthology inference based on 1-many homology relationship using orthologous groups (OrthoFinder2)
To perform orthology inference you can start with the
orthologs() function provided by orthologr.
The orthologs() function takes nucleotide or protein
sequences stored in fasta files for a set of organisms as input and
performs orthology inference to detect orthologous genes or orthologous
groups within the given organisms based on selected orthology inference
programs.
The following interfaces are (yet) implemented in the
orthologs() function:
BLASTp best hit (BH): see blast_best().
BLASTp reciprocal best hit (RBH): see
blast_rec().
Using a simple example stored in the package environment of
orthologr you can get an impression on how to use the
orthologs() function.
Note: it is assumed that when using
orthologs() all corresponding programs you want to use are
already installed on your machine and are executable via either the
default execution PATH or you specifically define the location of the
executable file via the path argument that can be passed to
orthologs().
In the following examples, we will use toy fasta files stored within
the orthologr package:
system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr').
# perform orthology inference using BLAST reciprocal best hit
# and fasta sequence files storing protein sequences
orthologr::orthologs(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_files = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
ortho_detection = "RBH",
comp_cores = 1,
clean_folders = FALSE)Running blastp: 2.9.0+ ...
Building a new DB, current time: 07/06/2019 16:22:29
New DB name: blastdb_ortho_lyra_aa.fasta_protein.fasta
New DB title: blastdb_ortho_lyra_aa.fasta_protein.fasta
Sequence type: Protein
Deleted existing BLAST database with identical name.
Keep Linkouts: T
Keep MBits: T
Maximum file size: 1000000000B
Adding sequences from FASTA; added 20 sequences in 0.00440097 seconds.
Running blastp: 2.9.0+ ...
Building a new DB, current time: 07/06/2019 16:22:30
New DB name: blastdb_ortho_thal_aa.fasta_protein.fasta
New DB title: blastdb_ortho_thal_aa.fasta_protein.fasta
Sequence type: Protein
Deleted existing BLAST database with identical name.
Keep Linkouts: T
Keep MBits: T
Maximum file size: 1000000000B
Adding sequences from FASTA; added 20 sequences in 0.003829 seconds.
# A tibble: 20 x 21
# Groups: query_id [20]
query_id subject_id perc_identity num_ident_match... alig_length
<chr> <chr> <dbl> <int> <int>
1 AT1G010... 333554|PA... 74.0 347 469
2 AT1G010... 470181|PA... 91.1 224 246
3 AT1G010... 470180|PA... 95.5 343 359
4 AT1G010... 333551|PA... 92.0 1812 1970
5 AT1G010... 909874|PA... 100 213 213
6 AT1G010... 470177|PA... 89.5 580 648
7 AT1G010... 918864|PA... 95.1 348 366
8 AT1G010... 909871|PA... 90.3 271 300
9 AT1G010... 470171|PA... 96.8 420 434
10 AT1G011... 333544|PA... 93.6 494 528
11 AT1G011... 918858|PA... 99.2 525 529
12 AT1G011... 470161|PA... 98.4 446 453
13 AT1G011... 918855|PA... 72.6 207 285
14 AT1G011... 918854|PA... 84.9 152 179
15 AT1G011... 311317|PA... 85.6 83 97
16 AT1G011... 909860|PA... 92.6 287 310
17 AT1G011... 311315|PA... 94.2 502 533
18 AT1G012... 470156|PA... 95.8 228 238
19 AT1G012... 311313|PA... 95.3 102 107
20 AT1G012... 470155|PA... 96.7 1021 1056
# ... with 16 more variables: mismatches <int>, 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 <dbl>
>This small example returns 20 orthologous genes between
Arabidopsis thaliana and Arabidopsis lyrata. As you
can see, the query_file and subject_files
arguments take the proteomes of Arabidopsis thaliana
(query_file) and Arabidopsis lyrata
(subject_files) stored in fasta files. The
seq_type argument specifies that you will pass protein
sequences (proteomes) to the orthologs() function. In case
you only have either genomes (DNA sequences) or CDS files, you can
modify the seq_type argument to
seq_type = "dna" (when working with only genome data) or
seq_type = "cds" (when working with CDS files). Internally
the orthologs() function will perform a CDS prediction
using predict_cds() and will furthermore translate the
predicted CDS sequences into protein sequences. Analogously when
seq_type = "cds" is specified, internally the
orthologs() function will translate all CDS sequences into
protein sequences to run orthology inference based on protein
sequences.
The advantage of this type of output is that in addition to having the orthology relationship between genes from two different genomes, users also retrieve the BLAST results of the respective orthology relationship which allows them to perform subsequent filtering for either more conservative or more liberal orthology relationships.
Note: future versions of orthologr will
allow to perform orthology inference using DNA sequences. Nevertheless,
since most orthology inference methods or paradigms rely on protein
sequences, the first version of orthologr will follow this
paradigm.
In case you have to specify the path to the corresponding orthology
inference method you can use the path argument as
follows:
# using an external execution path
orthologr::orthologs(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_files = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
ortho_detection = "RBH",
path = "here/path/to/blastp",
clean_folders = FALSE,
comp_cores = 1)When you are working on a multicore machine, you can also specify the
comp_cores argument that will allow you to run all analyses
in parallel (to speed up computations).
# running orthology inference in parallel using 2 cores
orthologr::orthologs(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_files = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
ortho_detection = "RBH",
clean_folders = FALSE,
comp_cores = 2)# A tibble: 20 x 12
# Groups: query_id [20]
query_id subject_id perc_identity alig_length
<chr> <chr> <dbl> <dbl>
1 AT1G010... 333554|PA... 74.0 469
2 AT1G010... 470181|PA... 91.1 246
3 AT1G010... 470180|PA... 95.5 359
4 AT1G010... 333551|PA... 92.0 1970
5 AT1G010... 909874|PA... 100 213
6 AT1G010... 470177|PA... 89.5 648
7 AT1G010... 918864|PA... 95.1 366
8 AT1G010... 909871|PA... 90.3 300
9 AT1G010... 470171|PA... 96.8 434
10 AT1G011... 333544|PA... 93.6 528
11 AT1G011... 918858|PA... 99.2 529
12 AT1G011... 470161|PA... 98.4 453
13 AT1G011... 918855|PA... 72.6 285
14 AT1G011... 918854|PA... 84.9 179
15 AT1G011... 311317|PA... 85.6 97
16 AT1G011... 909860|PA... 92.6 310
17 AT1G011... 311315|PA... 94.2 533
18 AT1G012... 470156|PA... 95.8 238
19 AT1G012... 311313|PA... 95.3 107
20 AT1G012... 470155|PA... 96.7 1056
# ... with 8 more variables: mismatches <dbl>,
# gap_openings <dbl>, q_start <dbl>, q_end <dbl>,
# s_start <dbl>, s_end <dbl>, evalue <dbl>,
# bit_score <dbl>In this case 2 cores are being used to perform parallel processing,
the clean_folders argument specifies that all files
returned by the corresponding orthology inference method are removed
after analyses.
In this section small examples will illustrate the use of the
orthologs() function for each orthology inference
program.
The BLASTp best hit method is a uni-directional BLAST best hit search
of a query organism A against a
subject organism B based on the e-value.
Orthology Inference using BLASTp best hit can be performed by
specifying the argument ortho_detection = "BH" and one
computing core comp_core = 1:
# perform orthology inference using BLAST best hit
# and fasta sequence files storing protein sequences
orthologr::orthologs(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_files = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
ortho_detection = "BH",
clean_folders = TRUE,
comp_cores = 1) # A tibble: 20 x 12
# Groups: query_id [20]
query_id subject_id perc_identity alig_length
<chr> <chr> <dbl> <dbl>
1 AT1G010... 333554|PA... 74.0 469
2 AT1G010... 470181|PA... 91.1 246
3 AT1G010... 470180|PA... 95.5 359
4 AT1G010... 333551|PA... 92.0 1970
5 AT1G010... 909874|PA... 100 213
6 AT1G010... 470177|PA... 89.5 648
7 AT1G010... 918864|PA... 95.1 366
8 AT1G010... 909871|PA... 90.3 300
9 AT1G010... 470171|PA... 96.8 434
10 AT1G011... 333544|PA... 93.6 528
11 AT1G011... 918858|PA... 99.2 529
12 AT1G011... 470161|PA... 98.4 453
13 AT1G011... 918855|PA... 72.6 285
14 AT1G011... 918854|PA... 84.9 179
15 AT1G011... 311317|PA... 85.6 97
16 AT1G011... 909860|PA... 92.6 310
17 AT1G011... 311315|PA... 94.2 533
18 AT1G012... 470156|PA... 95.8 238
19 AT1G012... 311313|PA... 95.3 107
20 AT1G012... 470155|PA... 96.7 1056
# ... with 8 more variables: mismatches <dbl>,
# gap_openings <dbl>, q_start <dbl>, q_end <dbl>,
# s_start <dbl>, s_end <dbl>, evalue <dbl>,
# bit_score <dbl>The resulting table stores the orthologous gene pairs and the
corresponding e-value of the best hit. By specifying
detailed_output = TRUE more information of the BLAST result
can be obtained.
The BLAST best reciprocal hit is a bi-directional BLAST best hit
search of a query organism A against a
subject organism B based on the e-value.
The Algorithm for BLAST best reciprocal hit runs as follows:
bh_A <- best_hit(A,B)
bh_B <- best_hit(B,A)
join(bh_A,bh_B) by tupel
(query_id, subject_id)
In other words, only in case the tuple
(query_id, subject_id) is returned as best hit based on the
e-value in both BLAST directions, the corresponding tupel
(query_id, subject_id) is retained as orthologous gene
pair.
As demonstrated before a simple call of orthologs()
using ortho_detection = "RBH" and one computing core
comp_core = 1 can be performed as follows:
library(orthologr)
# perform orthology inference using BLAST reciprocal best hit
# and fasta sequence files storing protein sequences
orthologs(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_files = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
ortho_detection = "RBH",
clean_folders = FALSE,
comp_cores = 1)
query_id subject_id evalue
1 AT1G01010.1 333554|PACid:16033839 0e+00
2 AT1G01020.1 470181|PACid:16064328 7e-166
3 AT1G01030.1 470180|PACid:16054974 0e+00
4 AT1G01040.1 333551|PACid:16057793 0e+00
5 AT1G01050.1 909874|PACid:16064489 2e-160
6 AT1G01060.3 470177|PACid:16043374 0e+00
7 AT1G01070.1 918864|PACid:16052578 0e+00
8 AT1G01080.1 909871|PACid:16053217 1e-178
9 AT1G01090.1 470171|PACid:16052860 0e+00
10 AT1G01110.2 333544|PACid:16034284 0e+00
11 AT1G01120.1 918858|PACid:16049140 0e+00
12 AT1G01140.3 470161|PACid:16036015 0e+00
13 AT1G01150.1 918855|PACid:16037307 3e-150
14 AT1G01160.1 918854|PACid:16044153 1e-93
15 AT1G01170.2 311317|PACid:16052302 3e-54
16 AT1G01180.1 909860|PACid:16056125 0e+00
17 AT1G01190.1 311315|PACid:16059488 0e+00
18 AT1G01200.1 470156|PACid:16041002 3e-172
19 AT1G01210.1 311313|PACid:16057125 7e-76
20 AT1G01220.1 470155|PACid:16047984 0e+00
When you need more parameters returned by RBH, you can
specify the detailed_output = TRUE argument to obtain all
BLAST parameters.
library(orthologr)
library(dplyr)
# perform orthology inference using BLAST reciprocal best hit
# and fasta sequence files storing protein sequences
RBH <- orthologs(query_file = system.file('seqs/ortho_thal_aa.fasta', package = 'orthologr'),
subject_files = system.file('seqs/ortho_lyra_aa.fasta', package = 'orthologr'),
seq_type = "protein",
ortho_detection = "RBH",
detailed_output = TRUE,
clean_folders = FALSE,
comp_cores = 1)
glimpse(RBH)Variables:
$ query_id (chr) "AT1G01010.1", "AT1G01020.1", "AT1G01030.1", "AT1G01040.1"...
$ subject_id (chr) "333554|PACid:16033839", "470181|PACid:16064328", "470180|...
$ perc_identity (dbl) 73.99, 91.06, 95.54, 91.98, 100.00, 89.51, 95.08, 90.33, 9...
$ alig_length (dbl) 469, 246, 359, 1970, 213, 648, 366, 300, 434, 528, 529, 45...
$ mismatches (dbl) 80, 22, 12, 85, 0, 58, 14, 22, 8, 34, 4, 6, 68, 19, 0, 20,...
$ gap_openings (dbl) 8, 0, 2, 10, 0, 5, 2, 2, 3, 0, 0, 1, 3, 2, 1, 1, 1, 0, 0, 0
$ q_start (dbl) 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 5, 4, 2, 1, 1, 1, 1, 1
$ q_end (dbl) 430, 246, 359, 1910, 213, 646, 366, 294, 429, 528, 529, 45...
$ s_start (dbl) 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 16, 2, 4, 1, 6, 1, 1, 1
$ s_end (dbl) 466, 246, 355, 1963, 213, 640, 362, 299, 433, 528, 529, 45...
$ evalue (dbl) 0e+00, 7e-166, 0e+00, 0e+00, 2e-160, 0e+00, 0e+00, 1e-178,...
$ bit_score (dbl) 627, 454, 698, 3704, 437, 1037, 696, 491, 859, 972, 1092, ...In case you would like to store the corresponding
hit tables returned by BLAST for subsequent analyses, you
can specify the clean_folders = FALSE argument. The
corresponding BLAST hit table can then be found in
file.path(tempdir(),"_blast_db").
A detailed overview of further analyses that can be done with the corresponding BLAST output can be found in the BLAST vignette.