README.md
In brendanf/inferrnal: Interface to Call Programs from Infernal RNA Covariance Model Package
inferrnal
R Interface to Call Programs from Infernal RNA Covariance Model Package
Covariance Models (CM) are stochastic models of RNA sequence and
secondary structure. Infernal (INFERence
of RNA ALignment)[^1] is a software package with various command-line
tools related to CMs. inferrnal (with two “r”s) is a lightweight R
interface which calls the Infernal tools and imports the results to R.
It is developed independently from Infernal, and Infernal must be
installed in order for it to function. Note that Infernal does not work
on Windows.
Installation
Installing Infernal
The required Infernal package can be installed from Bioconda:
conda install -c bioconda infernal
or in Debian/Ubuntu Linux using apt:
sudo apt-get install infernal
or using Homebrew in MacOS:
brew tap brewsci/bio
brew install infernal
For other installation options, including source installation, see the
Infernal homepage.
Installing inferrnal
After Infernal is installed, the released version of inferrnal can be
installed from Bioconductor:
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("inferrnal")
Alternatively, the development version of inferrnal can be installed
from GitHub with:
if(!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("brendanf/inferrnal")
Examples
So far three of the tools are implemented: cmsearch, cmalign, and
cmbuild.
cmsearch
In order to search, we need a CM. Rfam has a
wide variety. For this example, we will use the eukaryotic 5.8S rRNA,
with the Rfam ID RF00002. The
CM is available from Rfam,
but it is also included as example data in inferrnal.
library(inferrnal)
cm <- cm_5_8S()
We also need some sequences to search. The sample data is from a soil
metabarcoding study focused on fungi[^2]. The targeted region includes
5.8S as well as some of the surrounding rDNA regions.
sampfasta <- sample_rRNA_fasta()
Use cmsearch() to locate the 5.8S RNA in each sequence.
library(inferrnal)
cmsearch(cm = cm, seq = sampfasta, cpu = 1)
#> target_name target_accession query_name query_accession mdl mdl_from mdl_to
#> 1 seq45 - 5_8S_rRNA RF00002 cm 1 154
#> 2 seq3 - 5_8S_rRNA RF00002 cm 1 154
#> 3 seq2 - 5_8S_rRNA RF00002 cm 1 154
#> 4 seq28 - 5_8S_rRNA RF00002 cm 1 154
#> 5 seq23 - 5_8S_rRNA RF00002 cm 1 154
#> 6 seq9 - 5_8S_rRNA RF00002 cm 1 154
#> 7 seq7 - 5_8S_rRNA RF00002 cm 1 154
#> 8 seq48 - 5_8S_rRNA RF00002 cm 1 154
#> 9 seq5 - 5_8S_rRNA RF00002 cm 1 154
#> 10 seq6 - 5_8S_rRNA RF00002 cm 1 154
#> 11 seq11 - 5_8S_rRNA RF00002 cm 1 154
#> 12 seq12 - 5_8S_rRNA RF00002 cm 1 154
#> 13 seq13 - 5_8S_rRNA RF00002 cm 1 154
#> 14 seq17 - 5_8S_rRNA RF00002 cm 1 154
#> 15 seq18 - 5_8S_rRNA RF00002 cm 1 154
#> 16 seq19 - 5_8S_rRNA RF00002 cm 1 154
#> 17 seq25 - 5_8S_rRNA RF00002 cm 1 154
#> 18 seq47 - 5_8S_rRNA RF00002 cm 1 154
#> 19 seq14 - 5_8S_rRNA RF00002 cm 1 154
#> 20 seq8 - 5_8S_rRNA RF00002 cm 1 154
#> 21 seq21 - 5_8S_rRNA RF00002 cm 1 154
#> 22 seq36 - 5_8S_rRNA RF00002 cm 1 154
#> 23 seq10 - 5_8S_rRNA RF00002 cm 1 154
#> 24 seq22 - 5_8S_rRNA RF00002 cm 1 154
#> 25 seq50 - 5_8S_rRNA RF00002 cm 1 154
#> 26 seq44 - 5_8S_rRNA RF00002 cm 1 154
#> 27 seq27 - 5_8S_rRNA RF00002 cm 1 154
#> 28 seq35 - 5_8S_rRNA RF00002 cm 1 154
#> 29 seq15 - 5_8S_rRNA RF00002 cm 1 154
#> 30 seq38 - 5_8S_rRNA RF00002 cm 1 154
#> 31 seq30 - 5_8S_rRNA RF00002 cm 1 154
#> 32 seq26 - 5_8S_rRNA RF00002 cm 1 154
#> 33 seq33 - 5_8S_rRNA RF00002 cm 1 154
#> 34 seq46 - 5_8S_rRNA RF00002 cm 1 154
#> 35 seq34 - 5_8S_rRNA RF00002 cm 1 154
#> 36 seq39 - 5_8S_rRNA RF00002 cm 1 154
#> 37 seq16 - 5_8S_rRNA RF00002 cm 1 154
#> 38 seq31 - 5_8S_rRNA RF00002 cm 1 154
#> 39 seq1 - 5_8S_rRNA RF00002 cm 1 154
#> 40 seq41 - 5_8S_rRNA RF00002 cm 1 154
#> 41 seq29 - 5_8S_rRNA RF00002 cm 1 154
#> 42 seq49 - 5_8S_rRNA RF00002 cm 1 154
#> 43 seq20 - 5_8S_rRNA RF00002 cm 1 154
#> 44 seq4 - 5_8S_rRNA RF00002 cm 1 154
#> 45 seq32 - 5_8S_rRNA RF00002 cm 1 154
#> 46 seq24 - 5_8S_rRNA RF00002 cm 1 154
#> 47 seq37 - 5_8S_rRNA RF00002 cm 1 154
#> 48 seq43 - 5_8S_rRNA RF00002 cm 1 154
#> 49 seq37 - 5_8S_rRNA RF00002 cm 145 154
#> seq_from seq_to strand trunc pass gc bias score E_value inc description
#> 1 295 448 + no 1 0.47 0 171.3 1.9e-27 ! -
#> 2 236 389 + no 1 0.45 0 171.0 2.1e-27 ! -
#> 3 193 346 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 4 192 345 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 5 194 347 + no 1 0.47 0 168.5 5.3e-27 ! -
#> 6 170 323 + no 1 0.47 0 167.3 7.9e-27 ! -
#> 7 191 344 + no 1 0.50 0 167.3 8.1e-27 ! -
#> 8 192 345 + no 1 0.49 0 167.1 8.7e-27 ! -
#> 9 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 10 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 11 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 12 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 13 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 14 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 15 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 16 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 17 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 18 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 19 157 310 + no 1 0.46 0 165.3 1.6e-26 ! -
#> 20 154 307 + no 1 0.47 0 164.7 2.0e-26 ! -
#> 21 256 409 + no 1 0.42 0 164.7 2.1e-26 ! -
#> 22 256 409 + no 1 0.44 0 164.1 2.5e-26 ! -
#> 23 257 410 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 24 256 409 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 25 257 410 + no 1 0.44 0 163.7 2.8e-26 ! -
#> 26 255 408 + no 1 0.44 0 163.6 3.0e-26 ! -
#> 27 216 368 + no 1 0.46 0 162.5 4.5e-26 ! -
#> 28 178 331 + no 1 0.47 0 162.4 4.6e-26 ! -
#> 29 258 411 + no 1 0.42 0 161.0 7.7e-26 ! -
#> 30 156 309 + no 1 0.47 0 160.6 8.7e-26 ! -
#> 31 195 348 + no 1 0.48 0 160.1 1.0e-25 ! -
#> 32 188 340 + no 1 0.50 0 158.7 1.7e-25 ! -
#> 33 223 376 + no 1 0.50 0 156.6 3.7e-25 ! -
#> 34 230 383 + no 1 0.49 0 156.6 3.7e-25 ! -
#> 35 219 371 + no 1 0.45 0 156.5 3.8e-25 ! -
#> 36 263 416 + no 1 0.44 0 155.7 5.0e-25 ! -
#> 37 256 408 + no 1 0.43 0 154.0 9.2e-25 ! -
#> 38 194 346 + no 1 0.48 0 153.9 9.6e-25 ! -
#> 39 115 268 + no 1 0.45 0 151.2 2.5e-24 ! -
#> 40 258 414 + no 1 0.44 0 143.4 4.1e-23 ! -
#> 41 319 475 + no 1 0.45 0 139.6 1.6e-22 ! -
#> 42 188 340 + no 1 0.46 0 121.1 1.2e-19 ! -
#> 43 234 388 + no 1 0.57 0 119.6 2.1e-19 ! -
#> 44 95 248 + no 1 0.51 0 118.1 3.6e-19 ! -
#> 45 94 246 + no 1 0.50 0 115.1 1.0e-18 ! -
#> 46 95 246 + no 1 0.50 0 108.2 1.3e-17 ! -
#> 47 93 246 + no 1 0.44 0 102.4 9.9e-17 ! -
#> 48 218 373 + no 1 0.51 0 93.5 2.4e-15 ! -
#> 49 1 10 + 5' 2 0.50 0 -5.4 5.9e+00 ? -
Instead of passing a file name, you can also supply a DNAStringSet or
RNAStringSet object from the Biostrings package.
sampseqs <- Biostrings::readDNAStringSet(sampfasta)
cmsearch(cm = cm, seq = sampseqs, cpu = 1)
#> target_name target_accession query_name query_accession mdl mdl_from mdl_to
#> 1 seq45 - 5_8S_rRNA RF00002 cm 1 154
#> 2 seq3 - 5_8S_rRNA RF00002 cm 1 154
#> 3 seq2 - 5_8S_rRNA RF00002 cm 1 154
#> 4 seq28 - 5_8S_rRNA RF00002 cm 1 154
#> 5 seq23 - 5_8S_rRNA RF00002 cm 1 154
#> 6 seq9 - 5_8S_rRNA RF00002 cm 1 154
#> 7 seq7 - 5_8S_rRNA RF00002 cm 1 154
#> 8 seq48 - 5_8S_rRNA RF00002 cm 1 154
#> 9 seq5 - 5_8S_rRNA RF00002 cm 1 154
#> 10 seq6 - 5_8S_rRNA RF00002 cm 1 154
#> 11 seq11 - 5_8S_rRNA RF00002 cm 1 154
#> 12 seq12 - 5_8S_rRNA RF00002 cm 1 154
#> 13 seq13 - 5_8S_rRNA RF00002 cm 1 154
#> 14 seq17 - 5_8S_rRNA RF00002 cm 1 154
#> 15 seq18 - 5_8S_rRNA RF00002 cm 1 154
#> 16 seq19 - 5_8S_rRNA RF00002 cm 1 154
#> 17 seq25 - 5_8S_rRNA RF00002 cm 1 154
#> 18 seq47 - 5_8S_rRNA RF00002 cm 1 154
#> 19 seq14 - 5_8S_rRNA RF00002 cm 1 154
#> 20 seq8 - 5_8S_rRNA RF00002 cm 1 154
#> 21 seq21 - 5_8S_rRNA RF00002 cm 1 154
#> 22 seq36 - 5_8S_rRNA RF00002 cm 1 154
#> 23 seq10 - 5_8S_rRNA RF00002 cm 1 154
#> 24 seq22 - 5_8S_rRNA RF00002 cm 1 154
#> 25 seq50 - 5_8S_rRNA RF00002 cm 1 154
#> 26 seq44 - 5_8S_rRNA RF00002 cm 1 154
#> 27 seq27 - 5_8S_rRNA RF00002 cm 1 154
#> 28 seq35 - 5_8S_rRNA RF00002 cm 1 154
#> 29 seq15 - 5_8S_rRNA RF00002 cm 1 154
#> 30 seq38 - 5_8S_rRNA RF00002 cm 1 154
#> 31 seq30 - 5_8S_rRNA RF00002 cm 1 154
#> 32 seq26 - 5_8S_rRNA RF00002 cm 1 154
#> 33 seq33 - 5_8S_rRNA RF00002 cm 1 154
#> 34 seq46 - 5_8S_rRNA RF00002 cm 1 154
#> 35 seq34 - 5_8S_rRNA RF00002 cm 1 154
#> 36 seq39 - 5_8S_rRNA RF00002 cm 1 154
#> 37 seq16 - 5_8S_rRNA RF00002 cm 1 154
#> 38 seq31 - 5_8S_rRNA RF00002 cm 1 154
#> 39 seq1 - 5_8S_rRNA RF00002 cm 1 154
#> 40 seq41 - 5_8S_rRNA RF00002 cm 1 154
#> 41 seq29 - 5_8S_rRNA RF00002 cm 1 154
#> 42 seq49 - 5_8S_rRNA RF00002 cm 1 154
#> 43 seq20 - 5_8S_rRNA RF00002 cm 1 154
#> 44 seq4 - 5_8S_rRNA RF00002 cm 1 154
#> 45 seq32 - 5_8S_rRNA RF00002 cm 1 154
#> 46 seq24 - 5_8S_rRNA RF00002 cm 1 154
#> 47 seq37 - 5_8S_rRNA RF00002 cm 1 154
#> 48 seq43 - 5_8S_rRNA RF00002 cm 1 154
#> 49 seq37 - 5_8S_rRNA RF00002 cm 145 154
#> seq_from seq_to strand trunc pass gc bias score E_value inc description
#> 1 295 448 + no 1 0.47 0 171.3 1.9e-27 ! -
#> 2 236 389 + no 1 0.45 0 171.0 2.1e-27 ! -
#> 3 193 346 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 4 192 345 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 5 194 347 + no 1 0.47 0 168.5 5.3e-27 ! -
#> 6 170 323 + no 1 0.47 0 167.3 7.9e-27 ! -
#> 7 191 344 + no 1 0.50 0 167.3 8.1e-27 ! -
#> 8 192 345 + no 1 0.49 0 167.1 8.7e-27 ! -
#> 9 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 10 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 11 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 12 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 13 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 14 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 15 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 16 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 17 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 18 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 19 157 310 + no 1 0.46 0 165.3 1.6e-26 ! -
#> 20 154 307 + no 1 0.47 0 164.7 2.0e-26 ! -
#> 21 256 409 + no 1 0.42 0 164.7 2.1e-26 ! -
#> 22 256 409 + no 1 0.44 0 164.1 2.5e-26 ! -
#> 23 257 410 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 24 256 409 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 25 257 410 + no 1 0.44 0 163.7 2.8e-26 ! -
#> 26 255 408 + no 1 0.44 0 163.6 3.0e-26 ! -
#> 27 216 368 + no 1 0.46 0 162.5 4.5e-26 ! -
#> 28 178 331 + no 1 0.47 0 162.4 4.6e-26 ! -
#> 29 258 411 + no 1 0.42 0 161.0 7.7e-26 ! -
#> 30 156 309 + no 1 0.47 0 160.6 8.7e-26 ! -
#> 31 195 348 + no 1 0.48 0 160.1 1.0e-25 ! -
#> 32 188 340 + no 1 0.50 0 158.7 1.7e-25 ! -
#> 33 223 376 + no 1 0.50 0 156.6 3.7e-25 ! -
#> 34 230 383 + no 1 0.49 0 156.6 3.7e-25 ! -
#> 35 219 371 + no 1 0.45 0 156.5 3.8e-25 ! -
#> 36 263 416 + no 1 0.44 0 155.7 5.0e-25 ! -
#> 37 256 408 + no 1 0.43 0 154.0 9.2e-25 ! -
#> 38 194 346 + no 1 0.48 0 153.9 9.6e-25 ! -
#> 39 115 268 + no 1 0.45 0 151.2 2.5e-24 ! -
#> 40 258 414 + no 1 0.44 0 143.4 4.1e-23 ! -
#> 41 319 475 + no 1 0.45 0 139.6 1.6e-22 ! -
#> 42 188 340 + no 1 0.46 0 121.1 1.2e-19 ! -
#> 43 234 388 + no 1 0.57 0 119.6 2.1e-19 ! -
#> 44 95 248 + no 1 0.51 0 118.1 3.6e-19 ! -
#> 45 94 246 + no 1 0.50 0 115.1 1.0e-18 ! -
#> 46 95 246 + no 1 0.50 0 108.2 1.3e-17 ! -
#> 47 93 246 + no 1 0.44 0 102.4 9.9e-17 ! -
#> 48 218 373 + no 1 0.51 0 93.5 2.4e-15 ! -
#> 49 1 10 + 5' 2 0.50 0 -5.4 5.9e+00 ? -
cmsearch, by default, returns a table with information about each hit.
However, it can optionally also output an alignment of the hits to a
file in Stockholm format.
alnfile <- tempfile("alignment-", fileext = ".stk")
cmsearch(cm = cm, seq = sampseqs, alignment = alnfile)
#> target_name target_accession query_name query_accession mdl mdl_from mdl_to
#> 1 seq45 - 5_8S_rRNA RF00002 cm 1 154
#> 2 seq3 - 5_8S_rRNA RF00002 cm 1 154
#> 3 seq2 - 5_8S_rRNA RF00002 cm 1 154
#> 4 seq28 - 5_8S_rRNA RF00002 cm 1 154
#> 5 seq23 - 5_8S_rRNA RF00002 cm 1 154
#> 6 seq9 - 5_8S_rRNA RF00002 cm 1 154
#> 7 seq7 - 5_8S_rRNA RF00002 cm 1 154
#> 8 seq48 - 5_8S_rRNA RF00002 cm 1 154
#> 9 seq5 - 5_8S_rRNA RF00002 cm 1 154
#> 10 seq6 - 5_8S_rRNA RF00002 cm 1 154
#> 11 seq11 - 5_8S_rRNA RF00002 cm 1 154
#> 12 seq12 - 5_8S_rRNA RF00002 cm 1 154
#> 13 seq13 - 5_8S_rRNA RF00002 cm 1 154
#> 14 seq17 - 5_8S_rRNA RF00002 cm 1 154
#> 15 seq18 - 5_8S_rRNA RF00002 cm 1 154
#> 16 seq19 - 5_8S_rRNA RF00002 cm 1 154
#> 17 seq25 - 5_8S_rRNA RF00002 cm 1 154
#> 18 seq47 - 5_8S_rRNA RF00002 cm 1 154
#> 19 seq14 - 5_8S_rRNA RF00002 cm 1 154
#> 20 seq8 - 5_8S_rRNA RF00002 cm 1 154
#> 21 seq21 - 5_8S_rRNA RF00002 cm 1 154
#> 22 seq36 - 5_8S_rRNA RF00002 cm 1 154
#> 23 seq10 - 5_8S_rRNA RF00002 cm 1 154
#> 24 seq22 - 5_8S_rRNA RF00002 cm 1 154
#> 25 seq50 - 5_8S_rRNA RF00002 cm 1 154
#> 26 seq44 - 5_8S_rRNA RF00002 cm 1 154
#> 27 seq27 - 5_8S_rRNA RF00002 cm 1 154
#> 28 seq35 - 5_8S_rRNA RF00002 cm 1 154
#> 29 seq15 - 5_8S_rRNA RF00002 cm 1 154
#> 30 seq38 - 5_8S_rRNA RF00002 cm 1 154
#> 31 seq30 - 5_8S_rRNA RF00002 cm 1 154
#> 32 seq26 - 5_8S_rRNA RF00002 cm 1 154
#> 33 seq33 - 5_8S_rRNA RF00002 cm 1 154
#> 34 seq46 - 5_8S_rRNA RF00002 cm 1 154
#> 35 seq34 - 5_8S_rRNA RF00002 cm 1 154
#> 36 seq39 - 5_8S_rRNA RF00002 cm 1 154
#> 37 seq16 - 5_8S_rRNA RF00002 cm 1 154
#> 38 seq31 - 5_8S_rRNA RF00002 cm 1 154
#> 39 seq1 - 5_8S_rRNA RF00002 cm 1 154
#> 40 seq41 - 5_8S_rRNA RF00002 cm 1 154
#> 41 seq29 - 5_8S_rRNA RF00002 cm 1 154
#> 42 seq49 - 5_8S_rRNA RF00002 cm 1 154
#> 43 seq20 - 5_8S_rRNA RF00002 cm 1 154
#> 44 seq4 - 5_8S_rRNA RF00002 cm 1 154
#> 45 seq32 - 5_8S_rRNA RF00002 cm 1 154
#> 46 seq24 - 5_8S_rRNA RF00002 cm 1 154
#> 47 seq37 - 5_8S_rRNA RF00002 cm 1 154
#> 48 seq43 - 5_8S_rRNA RF00002 cm 1 154
#> 49 seq37 - 5_8S_rRNA RF00002 cm 145 154
#> seq_from seq_to strand trunc pass gc bias score E_value inc description
#> 1 295 448 + no 1 0.47 0 171.3 1.9e-27 ! -
#> 2 236 389 + no 1 0.45 0 171.0 2.1e-27 ! -
#> 3 193 346 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 4 192 345 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 5 194 347 + no 1 0.47 0 168.5 5.3e-27 ! -
#> 6 170 323 + no 1 0.47 0 167.3 7.9e-27 ! -
#> 7 191 344 + no 1 0.50 0 167.3 8.1e-27 ! -
#> 8 192 345 + no 1 0.49 0 167.1 8.7e-27 ! -
#> 9 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 10 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 11 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 12 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 13 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 14 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 15 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 16 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 17 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 18 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 19 157 310 + no 1 0.46 0 165.3 1.6e-26 ! -
#> 20 154 307 + no 1 0.47 0 164.7 2.0e-26 ! -
#> 21 256 409 + no 1 0.42 0 164.7 2.1e-26 ! -
#> 22 256 409 + no 1 0.44 0 164.1 2.5e-26 ! -
#> 23 257 410 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 24 256 409 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 25 257 410 + no 1 0.44 0 163.7 2.8e-26 ! -
#> 26 255 408 + no 1 0.44 0 163.6 3.0e-26 ! -
#> 27 216 368 + no 1 0.46 0 162.5 4.5e-26 ! -
#> 28 178 331 + no 1 0.47 0 162.4 4.6e-26 ! -
#> 29 258 411 + no 1 0.42 0 161.0 7.7e-26 ! -
#> 30 156 309 + no 1 0.47 0 160.6 8.7e-26 ! -
#> 31 195 348 + no 1 0.48 0 160.1 1.0e-25 ! -
#> 32 188 340 + no 1 0.50 0 158.7 1.7e-25 ! -
#> 33 223 376 + no 1 0.50 0 156.6 3.7e-25 ! -
#> 34 230 383 + no 1 0.49 0 156.6 3.7e-25 ! -
#> 35 219 371 + no 1 0.45 0 156.5 3.8e-25 ! -
#> 36 263 416 + no 1 0.44 0 155.7 5.0e-25 ! -
#> 37 256 408 + no 1 0.43 0 154.0 9.2e-25 ! -
#> 38 194 346 + no 1 0.48 0 153.9 9.6e-25 ! -
#> 39 115 268 + no 1 0.45 0 151.2 2.5e-24 ! -
#> 40 258 414 + no 1 0.44 0 143.4 4.1e-23 ! -
#> 41 319 475 + no 1 0.45 0 139.6 1.6e-22 ! -
#> 42 188 340 + no 1 0.46 0 121.1 1.2e-19 ! -
#> 43 234 388 + no 1 0.57 0 119.6 2.1e-19 ! -
#> 44 95 248 + no 1 0.51 0 118.1 3.6e-19 ! -
#> 45 94 246 + no 1 0.50 0 115.1 1.0e-18 ! -
#> 46 95 246 + no 1 0.50 0 108.2 1.3e-17 ! -
#> 47 93 246 + no 1 0.44 0 102.4 9.9e-17 ! -
#> 48 218 373 + no 1 0.51 0 93.5 2.4e-15 ! -
#> 49 1 10 + 5' 2 0.50 0 -5.4 5.9e+00 ? -
inferrnal includes a parser for Stockholm alignments, which also
imports column annotations.
msa <- read_stockholm_msa(alnfile)
read_stockholm_msa returns an object of class
StockholmRNAMultipleAlignment. It is also possible to load DNA or AA
alignments using an optional type= argument to read_stockholm_msa.
msa
#> StockholmRNAMultipleAlignment with 48 rows and 164 columns
#> aln names
#> [1] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGCAUGCCUGCUUGAGUGUCA seq45/295-448
#> [2] AACUUUCAACAACGGAUCUCUUGGC...GAGGAGCAUGCCUGUUUGAGUGUCA seq3/236-389
#> [3] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq2/193-346
#> [4] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq28/192-345
#> [5] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq23/194-347
#> [6] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq9/170-323
#> [7] AACUUUCAACAAUGGAUCUCUUGGU...GGGGGGCAUGCCUGUCCGAGCGUCA seq7/191-344
#> [8] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq48/192-345
#> [9] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq5/257-410
#> ... ...
#> [40] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq41/258-414
#> [41] AACUUUCAGCAACGGAUCUCUUGGC...UGUGAGUACACUUGUUUGAGCGUCA seq29/319-475
#> [42] CACUAUUAGCGAUGGAUGUCUUGGA...CAGCAGUAGGUUGGUCUCAGCAUCU seq49/188-340
#> [43] GACUCCCGGCAACGGAUAUCUCGGC...CGAGGGCACGCCUGCCUCUUGGGCG seq20/234-388
#> [44] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq4/95-248
#> [45] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq32/94-246
#> [46] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq24/95-246
#> [47] UAGCAUCAGCGAUUAACGUCUUGGU...AUUGAGUGCACUUGCUUCAGUGUGG seq37/93-246
#> [48] AACACGCAACGGUGGACCACUCGGC...GCCAGCUCUUGCUUGUUGAGCCUGG seq43/218-373
#>
#> GF (file) annotations:
#> BStringSet object of length 1:
#> width seq names
#> [1] 14 Infernal 1.1.4 AU
#>
#> GR (residue) PP annotations:
#> BStringSet object of length 48:
#> width seq names
#> [1] 164 ***********************...********************** seq45/295-448
#> [2] 164 ***********************...********************** seq3/236-389
#> [3] 164 ***********************...********************** seq2/193-346
#> [4] 164 ***********************...********************** seq28/192-345
#> [5] 164 ***********************...********************** seq23/194-347
#> ... ... ...
#> [44] 164 ***********************...********************** seq4/95-248
#> [45] 164 ***********************...********************** seq32/94-246
#> [46] 164 ***********************...********************** seq24/95-246
#> [47] 164 ***********************...********************** seq37/93-246
#> [48] 164 ***********************...********************** seq43/218-373
#>
#> GC (column) annotations:
#> BStringSet object of length 2:
#> width seq names
#> [1] 164 :::::::::::::::::::::::...>>>>>:::::::::::::::::: SS_cons
#> [2] 164 AACuuUuAgCGAUGGAUguCUuG...ggggCAUgccUGuuugAGUGUCa RF
In addition to the alignment itself, the Stockholm output includes the
consensus secondary structure and the reference annotation, as defined
in the CM, as column (“GC”) annotations named “SS_cons” and “RF”, as
well as the posterior probability that each base is aligned in the
correct position as residue (“GR”) annotations named “PP”. For more
information about these annotations, including the encoding of secondary
structure and posterior probabilities, see the Infernal
documentation.
msa$GC$SS_cons
#> 164-letter "BString" instance
#> seq: ::::::::::::::::::::::::::::::::::::...<<..____.>>>>>>>>>::::::::::::::::::
msa$GC$RF
#> 164-letter "BString" instance
#> seq: AACuuUuAgCGAUGGAUguCUuGGCUCccGuaUCGA...gg..Uuuu.cccgggggCAUgccUGuuugAGUGUCa
cmalign
If you have sequences which have already been trimmed to contain only
the RNA defined by the CM (possibly truncated, but not extended), then
you can align them to the CM using cmalign. This is much faster than
cmsearch. This example uses the results of cmsearch from the
previous section, after removing gaps.
unaln <- sample_rRNA_5_8S()
unaln_seq <- Biostrings::readRNAStringSet(unaln)
unaln_seq
#> RNAStringSet object of length 48:
#> width seq names
#> [1] 154 AACUUUCAGCAACGGAUCUCUUG...GAGCAUGCCUGCUUGAGUGUCA seq45/295-448
#> [2] 154 AACUUUCAACAACGGAUCUCUUG...GAGCAUGCCUGUUUGAGUGUCA seq3/236-389
#> [3] 154 AACUUUCAGCAACGGAUCUCUUG...GGGCAUGCCUGUUUGAGUGUCG seq2/193-346
#> [4] 154 AACUUUCAGCAACGGAUCUCUUG...GGGCAUGCCUGUUUGAGUGUCG seq28/192-345
#> [5] 154 AACUUUCAACAACGGAUCUCUUG...GGGCAUGCCUGUUCGAGCGUCA seq23/194-347
#> ... ... ...
#> [44] 154 AACUCUCAGCGAUGGAUGACUCG...AAGUAUGUUUGGCUCGGUAUCA seq4/95-248
#> [45] 153 AACUCUCAGCGAUGGAUGACUCG...AAGUAUGUUUGGCUCGGUAUCA seq32/94-246
#> [46] 152 AACUCUCAGCGAUGGAUGACUCG...AAGUAUGUUUGGCUCGGUAUCA seq24/95-246
#> [47] 154 UAGCAUCAGCGAUUAACGUCUUG...GAGUGCACUUGCUUCAGUGUGG seq37/93-246
#> [48] 156 AACACGCAACGGUGGACCACUCG...AGCUCUUGCUUGUUGAGCCUGG seq43/218-373
aln <- cmalign(cm, unaln_seq, cpu = 1)
aln
#> StockholmRNAMultipleAlignment with 48 rows and 164 columns
#> aln names
#> [1] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGCAUGCCUGCUUGAGUGUCA seq45/295-448
#> [2] AACUUUCAACAACGGAUCUCUUGGC...GAGGAGCAUGCCUGUUUGAGUGUCA seq3/236-389
#> [3] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq2/193-346
#> [4] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq28/192-345
#> [5] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq23/194-347
#> [6] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq9/170-323
#> [7] AACUUUCAACAAUGGAUCUCUUGGU...GGGGGGCAUGCCUGUCCGAGCGUCA seq7/191-344
#> [8] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq48/192-345
#> [9] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq5/257-410
#> ... ...
#> [40] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq41/258-414
#> [41] AACUUUCAGCAACGGAUCUCUUGGC...UGUGAGUACACUUGUUUGAGCGUCA seq29/319-475
#> [42] CACUAUUAGCGAUGGAUGUCUUGGA...CAGCAGUAGGUUGGUCUCAGCAUCU seq49/188-340
#> [43] GACUCCCGGCAACGGAUAUCUCGGC...CGAGGGCACGCCUGCCUCUUGGGCG seq20/234-388
#> [44] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq4/95-248
#> [45] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq32/94-246
#> [46] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq24/95-246
#> [47] UAGCAUCAGCGAUUAACGUCUUGGU...AUUGAGUGCACUUGCUUCAGUGUGG seq37/93-246
#> [48] AACACGCAACGGUGGACCACUCGGC...GCCAGCUCUUGCUUGUUGAGCCUGG seq43/218-373
#>
#> GF (file) annotations:
#> BStringSet object of length 1:
#> width seq names
#> [1] 14 Infernal 1.1.4 AU
#>
#> GR (residue) PP annotations:
#> BStringSet object of length 48:
#> width seq names
#> [1] 164 ***********************...********************** seq45/295-448
#> [2] 164 ***********************...********************** seq3/236-389
#> [3] 164 ***********************...********************** seq2/193-346
#> [4] 164 ***********************...********************** seq28/192-345
#> [5] 164 ***********************...********************** seq23/194-347
#> ... ... ...
#> [44] 164 ***********************...********************** seq4/95-248
#> [45] 164 ***********************...********************** seq32/94-246
#> [46] 164 ***********************...********************** seq24/95-246
#> [47] 164 ***********************...********************** seq37/93-246
#> [48] 164 ***********************...********************** seq43/218-373
#>
#> GC (column) annotations:
#> BStringSet object of length 2:
#> width seq names
#> [1] 164 :::::::::::::::::::::::...>>>>>:::::::::::::::::: SS_cons
#> [2] 164 AACuuUuAgCGAUGGAUguCUuG...ggggCAUgccUGuuugAGUGUCa RF
cmbuild
cmbuild is used to create new CMs from annotated multiple sequence
alignments. To illustrate the process, we use the seed alignment for the
5.8S rRNA CM from RFAM. It is included as a sample file in inferrnal.
new_cm <- file.path(tempdir(), "5_8S.cm")
cmbuild(new_cm, msafile = stk_5_8S(), force = TRUE, quiet = FALSE)
This CM is not calibrated, so it cannot be used for cmsearch, but it
can be used in cmalign.
aln2 <- cmalign(new_cm, unaln_seq, cpu = 1)
The resulting alignment is the same as the one created using the CM from
RFAM, because they are based on the same seed alignment, and used the
same (default) options for cmbuild.
all.equal(aln, aln2)
#> [1] TRUE
[^1]: Nawrocki, E.P., Eddy, S.R., 2013. Infernal 1.1: 100-fold faster
RNA homology
searches.
Bioinformatics 29, 2933–2935.
[^2]: Furneaux, B., Bahram, M., Rosling, A., Yorou, N.S., Ryberg, M.,
2021. Long- and short-read metabarcoding technologies reveal
similar spatiotemporal structures in fungal
communities. Molecular
Ecology Resources 21, 1833–1849.
brendanf/inferrnal documentation built on Feb. 4, 2023, 4:49 p.m.
R Package Documentation
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inferrnal
R Interface to Call Programs from Infernal RNA Covariance Model Package
Covariance Models (CM) are stochastic models of RNA sequence and
secondary structure. Infernal (INFERence
of RNA ALignment)[^1] is a software package with various command-line
tools related to CMs. inferrnal (with two “r”s) is a lightweight R
interface which calls the Infernal tools and imports the results to R.
It is developed independently from Infernal, and Infernal must be
installed in order for it to function. Note that Infernal does not work
on Windows.
Installation
Installing Infernal
The required Infernal package can be installed from Bioconda:
conda install -c bioconda infernal
or in Debian/Ubuntu Linux using apt:
sudo apt-get install infernal
or using Homebrew in MacOS:
brew tap brewsci/bio
brew install infernal
For other installation options, including source installation, see the Infernal homepage.
Installing inferrnal
After Infernal is installed, the released version of inferrnal can be
installed from Bioconductor:
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("inferrnal")
Alternatively, the development version of inferrnal can be installed
from GitHub with:
if(!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("brendanf/inferrnal")
Examples
So far three of the tools are implemented: cmsearch, cmalign, and
cmbuild.
cmsearch
In order to search, we need a CM. Rfam has a
wide variety. For this example, we will use the eukaryotic 5.8S rRNA,
with the Rfam ID RF00002. The
CM is available from Rfam,
but it is also included as example data in inferrnal.
library(inferrnal)
cm <- cm_5_8S()
We also need some sequences to search. The sample data is from a soil metabarcoding study focused on fungi[^2]. The targeted region includes 5.8S as well as some of the surrounding rDNA regions.
sampfasta <- sample_rRNA_fasta()
Use cmsearch() to locate the 5.8S RNA in each sequence.
library(inferrnal)
cmsearch(cm = cm, seq = sampfasta, cpu = 1)
#> target_name target_accession query_name query_accession mdl mdl_from mdl_to
#> 1 seq45 - 5_8S_rRNA RF00002 cm 1 154
#> 2 seq3 - 5_8S_rRNA RF00002 cm 1 154
#> 3 seq2 - 5_8S_rRNA RF00002 cm 1 154
#> 4 seq28 - 5_8S_rRNA RF00002 cm 1 154
#> 5 seq23 - 5_8S_rRNA RF00002 cm 1 154
#> 6 seq9 - 5_8S_rRNA RF00002 cm 1 154
#> 7 seq7 - 5_8S_rRNA RF00002 cm 1 154
#> 8 seq48 - 5_8S_rRNA RF00002 cm 1 154
#> 9 seq5 - 5_8S_rRNA RF00002 cm 1 154
#> 10 seq6 - 5_8S_rRNA RF00002 cm 1 154
#> 11 seq11 - 5_8S_rRNA RF00002 cm 1 154
#> 12 seq12 - 5_8S_rRNA RF00002 cm 1 154
#> 13 seq13 - 5_8S_rRNA RF00002 cm 1 154
#> 14 seq17 - 5_8S_rRNA RF00002 cm 1 154
#> 15 seq18 - 5_8S_rRNA RF00002 cm 1 154
#> 16 seq19 - 5_8S_rRNA RF00002 cm 1 154
#> 17 seq25 - 5_8S_rRNA RF00002 cm 1 154
#> 18 seq47 - 5_8S_rRNA RF00002 cm 1 154
#> 19 seq14 - 5_8S_rRNA RF00002 cm 1 154
#> 20 seq8 - 5_8S_rRNA RF00002 cm 1 154
#> 21 seq21 - 5_8S_rRNA RF00002 cm 1 154
#> 22 seq36 - 5_8S_rRNA RF00002 cm 1 154
#> 23 seq10 - 5_8S_rRNA RF00002 cm 1 154
#> 24 seq22 - 5_8S_rRNA RF00002 cm 1 154
#> 25 seq50 - 5_8S_rRNA RF00002 cm 1 154
#> 26 seq44 - 5_8S_rRNA RF00002 cm 1 154
#> 27 seq27 - 5_8S_rRNA RF00002 cm 1 154
#> 28 seq35 - 5_8S_rRNA RF00002 cm 1 154
#> 29 seq15 - 5_8S_rRNA RF00002 cm 1 154
#> 30 seq38 - 5_8S_rRNA RF00002 cm 1 154
#> 31 seq30 - 5_8S_rRNA RF00002 cm 1 154
#> 32 seq26 - 5_8S_rRNA RF00002 cm 1 154
#> 33 seq33 - 5_8S_rRNA RF00002 cm 1 154
#> 34 seq46 - 5_8S_rRNA RF00002 cm 1 154
#> 35 seq34 - 5_8S_rRNA RF00002 cm 1 154
#> 36 seq39 - 5_8S_rRNA RF00002 cm 1 154
#> 37 seq16 - 5_8S_rRNA RF00002 cm 1 154
#> 38 seq31 - 5_8S_rRNA RF00002 cm 1 154
#> 39 seq1 - 5_8S_rRNA RF00002 cm 1 154
#> 40 seq41 - 5_8S_rRNA RF00002 cm 1 154
#> 41 seq29 - 5_8S_rRNA RF00002 cm 1 154
#> 42 seq49 - 5_8S_rRNA RF00002 cm 1 154
#> 43 seq20 - 5_8S_rRNA RF00002 cm 1 154
#> 44 seq4 - 5_8S_rRNA RF00002 cm 1 154
#> 45 seq32 - 5_8S_rRNA RF00002 cm 1 154
#> 46 seq24 - 5_8S_rRNA RF00002 cm 1 154
#> 47 seq37 - 5_8S_rRNA RF00002 cm 1 154
#> 48 seq43 - 5_8S_rRNA RF00002 cm 1 154
#> 49 seq37 - 5_8S_rRNA RF00002 cm 145 154
#> seq_from seq_to strand trunc pass gc bias score E_value inc description
#> 1 295 448 + no 1 0.47 0 171.3 1.9e-27 ! -
#> 2 236 389 + no 1 0.45 0 171.0 2.1e-27 ! -
#> 3 193 346 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 4 192 345 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 5 194 347 + no 1 0.47 0 168.5 5.3e-27 ! -
#> 6 170 323 + no 1 0.47 0 167.3 7.9e-27 ! -
#> 7 191 344 + no 1 0.50 0 167.3 8.1e-27 ! -
#> 8 192 345 + no 1 0.49 0 167.1 8.7e-27 ! -
#> 9 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 10 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 11 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 12 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 13 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 14 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 15 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 16 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 17 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 18 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 19 157 310 + no 1 0.46 0 165.3 1.6e-26 ! -
#> 20 154 307 + no 1 0.47 0 164.7 2.0e-26 ! -
#> 21 256 409 + no 1 0.42 0 164.7 2.1e-26 ! -
#> 22 256 409 + no 1 0.44 0 164.1 2.5e-26 ! -
#> 23 257 410 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 24 256 409 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 25 257 410 + no 1 0.44 0 163.7 2.8e-26 ! -
#> 26 255 408 + no 1 0.44 0 163.6 3.0e-26 ! -
#> 27 216 368 + no 1 0.46 0 162.5 4.5e-26 ! -
#> 28 178 331 + no 1 0.47 0 162.4 4.6e-26 ! -
#> 29 258 411 + no 1 0.42 0 161.0 7.7e-26 ! -
#> 30 156 309 + no 1 0.47 0 160.6 8.7e-26 ! -
#> 31 195 348 + no 1 0.48 0 160.1 1.0e-25 ! -
#> 32 188 340 + no 1 0.50 0 158.7 1.7e-25 ! -
#> 33 223 376 + no 1 0.50 0 156.6 3.7e-25 ! -
#> 34 230 383 + no 1 0.49 0 156.6 3.7e-25 ! -
#> 35 219 371 + no 1 0.45 0 156.5 3.8e-25 ! -
#> 36 263 416 + no 1 0.44 0 155.7 5.0e-25 ! -
#> 37 256 408 + no 1 0.43 0 154.0 9.2e-25 ! -
#> 38 194 346 + no 1 0.48 0 153.9 9.6e-25 ! -
#> 39 115 268 + no 1 0.45 0 151.2 2.5e-24 ! -
#> 40 258 414 + no 1 0.44 0 143.4 4.1e-23 ! -
#> 41 319 475 + no 1 0.45 0 139.6 1.6e-22 ! -
#> 42 188 340 + no 1 0.46 0 121.1 1.2e-19 ! -
#> 43 234 388 + no 1 0.57 0 119.6 2.1e-19 ! -
#> 44 95 248 + no 1 0.51 0 118.1 3.6e-19 ! -
#> 45 94 246 + no 1 0.50 0 115.1 1.0e-18 ! -
#> 46 95 246 + no 1 0.50 0 108.2 1.3e-17 ! -
#> 47 93 246 + no 1 0.44 0 102.4 9.9e-17 ! -
#> 48 218 373 + no 1 0.51 0 93.5 2.4e-15 ! -
#> 49 1 10 + 5' 2 0.50 0 -5.4 5.9e+00 ? -
Instead of passing a file name, you can also supply a DNAStringSet or
RNAStringSet object from the Biostrings package.
sampseqs <- Biostrings::readDNAStringSet(sampfasta)
cmsearch(cm = cm, seq = sampseqs, cpu = 1)
#> target_name target_accession query_name query_accession mdl mdl_from mdl_to
#> 1 seq45 - 5_8S_rRNA RF00002 cm 1 154
#> 2 seq3 - 5_8S_rRNA RF00002 cm 1 154
#> 3 seq2 - 5_8S_rRNA RF00002 cm 1 154
#> 4 seq28 - 5_8S_rRNA RF00002 cm 1 154
#> 5 seq23 - 5_8S_rRNA RF00002 cm 1 154
#> 6 seq9 - 5_8S_rRNA RF00002 cm 1 154
#> 7 seq7 - 5_8S_rRNA RF00002 cm 1 154
#> 8 seq48 - 5_8S_rRNA RF00002 cm 1 154
#> 9 seq5 - 5_8S_rRNA RF00002 cm 1 154
#> 10 seq6 - 5_8S_rRNA RF00002 cm 1 154
#> 11 seq11 - 5_8S_rRNA RF00002 cm 1 154
#> 12 seq12 - 5_8S_rRNA RF00002 cm 1 154
#> 13 seq13 - 5_8S_rRNA RF00002 cm 1 154
#> 14 seq17 - 5_8S_rRNA RF00002 cm 1 154
#> 15 seq18 - 5_8S_rRNA RF00002 cm 1 154
#> 16 seq19 - 5_8S_rRNA RF00002 cm 1 154
#> 17 seq25 - 5_8S_rRNA RF00002 cm 1 154
#> 18 seq47 - 5_8S_rRNA RF00002 cm 1 154
#> 19 seq14 - 5_8S_rRNA RF00002 cm 1 154
#> 20 seq8 - 5_8S_rRNA RF00002 cm 1 154
#> 21 seq21 - 5_8S_rRNA RF00002 cm 1 154
#> 22 seq36 - 5_8S_rRNA RF00002 cm 1 154
#> 23 seq10 - 5_8S_rRNA RF00002 cm 1 154
#> 24 seq22 - 5_8S_rRNA RF00002 cm 1 154
#> 25 seq50 - 5_8S_rRNA RF00002 cm 1 154
#> 26 seq44 - 5_8S_rRNA RF00002 cm 1 154
#> 27 seq27 - 5_8S_rRNA RF00002 cm 1 154
#> 28 seq35 - 5_8S_rRNA RF00002 cm 1 154
#> 29 seq15 - 5_8S_rRNA RF00002 cm 1 154
#> 30 seq38 - 5_8S_rRNA RF00002 cm 1 154
#> 31 seq30 - 5_8S_rRNA RF00002 cm 1 154
#> 32 seq26 - 5_8S_rRNA RF00002 cm 1 154
#> 33 seq33 - 5_8S_rRNA RF00002 cm 1 154
#> 34 seq46 - 5_8S_rRNA RF00002 cm 1 154
#> 35 seq34 - 5_8S_rRNA RF00002 cm 1 154
#> 36 seq39 - 5_8S_rRNA RF00002 cm 1 154
#> 37 seq16 - 5_8S_rRNA RF00002 cm 1 154
#> 38 seq31 - 5_8S_rRNA RF00002 cm 1 154
#> 39 seq1 - 5_8S_rRNA RF00002 cm 1 154
#> 40 seq41 - 5_8S_rRNA RF00002 cm 1 154
#> 41 seq29 - 5_8S_rRNA RF00002 cm 1 154
#> 42 seq49 - 5_8S_rRNA RF00002 cm 1 154
#> 43 seq20 - 5_8S_rRNA RF00002 cm 1 154
#> 44 seq4 - 5_8S_rRNA RF00002 cm 1 154
#> 45 seq32 - 5_8S_rRNA RF00002 cm 1 154
#> 46 seq24 - 5_8S_rRNA RF00002 cm 1 154
#> 47 seq37 - 5_8S_rRNA RF00002 cm 1 154
#> 48 seq43 - 5_8S_rRNA RF00002 cm 1 154
#> 49 seq37 - 5_8S_rRNA RF00002 cm 145 154
#> seq_from seq_to strand trunc pass gc bias score E_value inc description
#> 1 295 448 + no 1 0.47 0 171.3 1.9e-27 ! -
#> 2 236 389 + no 1 0.45 0 171.0 2.1e-27 ! -
#> 3 193 346 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 4 192 345 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 5 194 347 + no 1 0.47 0 168.5 5.3e-27 ! -
#> 6 170 323 + no 1 0.47 0 167.3 7.9e-27 ! -
#> 7 191 344 + no 1 0.50 0 167.3 8.1e-27 ! -
#> 8 192 345 + no 1 0.49 0 167.1 8.7e-27 ! -
#> 9 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 10 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 11 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 12 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 13 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 14 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 15 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 16 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 17 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 18 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 19 157 310 + no 1 0.46 0 165.3 1.6e-26 ! -
#> 20 154 307 + no 1 0.47 0 164.7 2.0e-26 ! -
#> 21 256 409 + no 1 0.42 0 164.7 2.1e-26 ! -
#> 22 256 409 + no 1 0.44 0 164.1 2.5e-26 ! -
#> 23 257 410 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 24 256 409 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 25 257 410 + no 1 0.44 0 163.7 2.8e-26 ! -
#> 26 255 408 + no 1 0.44 0 163.6 3.0e-26 ! -
#> 27 216 368 + no 1 0.46 0 162.5 4.5e-26 ! -
#> 28 178 331 + no 1 0.47 0 162.4 4.6e-26 ! -
#> 29 258 411 + no 1 0.42 0 161.0 7.7e-26 ! -
#> 30 156 309 + no 1 0.47 0 160.6 8.7e-26 ! -
#> 31 195 348 + no 1 0.48 0 160.1 1.0e-25 ! -
#> 32 188 340 + no 1 0.50 0 158.7 1.7e-25 ! -
#> 33 223 376 + no 1 0.50 0 156.6 3.7e-25 ! -
#> 34 230 383 + no 1 0.49 0 156.6 3.7e-25 ! -
#> 35 219 371 + no 1 0.45 0 156.5 3.8e-25 ! -
#> 36 263 416 + no 1 0.44 0 155.7 5.0e-25 ! -
#> 37 256 408 + no 1 0.43 0 154.0 9.2e-25 ! -
#> 38 194 346 + no 1 0.48 0 153.9 9.6e-25 ! -
#> 39 115 268 + no 1 0.45 0 151.2 2.5e-24 ! -
#> 40 258 414 + no 1 0.44 0 143.4 4.1e-23 ! -
#> 41 319 475 + no 1 0.45 0 139.6 1.6e-22 ! -
#> 42 188 340 + no 1 0.46 0 121.1 1.2e-19 ! -
#> 43 234 388 + no 1 0.57 0 119.6 2.1e-19 ! -
#> 44 95 248 + no 1 0.51 0 118.1 3.6e-19 ! -
#> 45 94 246 + no 1 0.50 0 115.1 1.0e-18 ! -
#> 46 95 246 + no 1 0.50 0 108.2 1.3e-17 ! -
#> 47 93 246 + no 1 0.44 0 102.4 9.9e-17 ! -
#> 48 218 373 + no 1 0.51 0 93.5 2.4e-15 ! -
#> 49 1 10 + 5' 2 0.50 0 -5.4 5.9e+00 ? -
cmsearch, by default, returns a table with information about each hit.
However, it can optionally also output an alignment of the hits to a
file in Stockholm format.
alnfile <- tempfile("alignment-", fileext = ".stk")
cmsearch(cm = cm, seq = sampseqs, alignment = alnfile)
#> target_name target_accession query_name query_accession mdl mdl_from mdl_to
#> 1 seq45 - 5_8S_rRNA RF00002 cm 1 154
#> 2 seq3 - 5_8S_rRNA RF00002 cm 1 154
#> 3 seq2 - 5_8S_rRNA RF00002 cm 1 154
#> 4 seq28 - 5_8S_rRNA RF00002 cm 1 154
#> 5 seq23 - 5_8S_rRNA RF00002 cm 1 154
#> 6 seq9 - 5_8S_rRNA RF00002 cm 1 154
#> 7 seq7 - 5_8S_rRNA RF00002 cm 1 154
#> 8 seq48 - 5_8S_rRNA RF00002 cm 1 154
#> 9 seq5 - 5_8S_rRNA RF00002 cm 1 154
#> 10 seq6 - 5_8S_rRNA RF00002 cm 1 154
#> 11 seq11 - 5_8S_rRNA RF00002 cm 1 154
#> 12 seq12 - 5_8S_rRNA RF00002 cm 1 154
#> 13 seq13 - 5_8S_rRNA RF00002 cm 1 154
#> 14 seq17 - 5_8S_rRNA RF00002 cm 1 154
#> 15 seq18 - 5_8S_rRNA RF00002 cm 1 154
#> 16 seq19 - 5_8S_rRNA RF00002 cm 1 154
#> 17 seq25 - 5_8S_rRNA RF00002 cm 1 154
#> 18 seq47 - 5_8S_rRNA RF00002 cm 1 154
#> 19 seq14 - 5_8S_rRNA RF00002 cm 1 154
#> 20 seq8 - 5_8S_rRNA RF00002 cm 1 154
#> 21 seq21 - 5_8S_rRNA RF00002 cm 1 154
#> 22 seq36 - 5_8S_rRNA RF00002 cm 1 154
#> 23 seq10 - 5_8S_rRNA RF00002 cm 1 154
#> 24 seq22 - 5_8S_rRNA RF00002 cm 1 154
#> 25 seq50 - 5_8S_rRNA RF00002 cm 1 154
#> 26 seq44 - 5_8S_rRNA RF00002 cm 1 154
#> 27 seq27 - 5_8S_rRNA RF00002 cm 1 154
#> 28 seq35 - 5_8S_rRNA RF00002 cm 1 154
#> 29 seq15 - 5_8S_rRNA RF00002 cm 1 154
#> 30 seq38 - 5_8S_rRNA RF00002 cm 1 154
#> 31 seq30 - 5_8S_rRNA RF00002 cm 1 154
#> 32 seq26 - 5_8S_rRNA RF00002 cm 1 154
#> 33 seq33 - 5_8S_rRNA RF00002 cm 1 154
#> 34 seq46 - 5_8S_rRNA RF00002 cm 1 154
#> 35 seq34 - 5_8S_rRNA RF00002 cm 1 154
#> 36 seq39 - 5_8S_rRNA RF00002 cm 1 154
#> 37 seq16 - 5_8S_rRNA RF00002 cm 1 154
#> 38 seq31 - 5_8S_rRNA RF00002 cm 1 154
#> 39 seq1 - 5_8S_rRNA RF00002 cm 1 154
#> 40 seq41 - 5_8S_rRNA RF00002 cm 1 154
#> 41 seq29 - 5_8S_rRNA RF00002 cm 1 154
#> 42 seq49 - 5_8S_rRNA RF00002 cm 1 154
#> 43 seq20 - 5_8S_rRNA RF00002 cm 1 154
#> 44 seq4 - 5_8S_rRNA RF00002 cm 1 154
#> 45 seq32 - 5_8S_rRNA RF00002 cm 1 154
#> 46 seq24 - 5_8S_rRNA RF00002 cm 1 154
#> 47 seq37 - 5_8S_rRNA RF00002 cm 1 154
#> 48 seq43 - 5_8S_rRNA RF00002 cm 1 154
#> 49 seq37 - 5_8S_rRNA RF00002 cm 145 154
#> seq_from seq_to strand trunc pass gc bias score E_value inc description
#> 1 295 448 + no 1 0.47 0 171.3 1.9e-27 ! -
#> 2 236 389 + no 1 0.45 0 171.0 2.1e-27 ! -
#> 3 193 346 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 4 192 345 + no 1 0.48 0 170.7 2.4e-27 ! -
#> 5 194 347 + no 1 0.47 0 168.5 5.3e-27 ! -
#> 6 170 323 + no 1 0.47 0 167.3 7.9e-27 ! -
#> 7 191 344 + no 1 0.50 0 167.3 8.1e-27 ! -
#> 8 192 345 + no 1 0.49 0 167.1 8.7e-27 ! -
#> 9 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 10 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 11 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 12 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 13 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 14 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 15 258 411 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 16 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 17 256 409 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 18 257 410 + no 1 0.43 0 165.5 1.5e-26 ! -
#> 19 157 310 + no 1 0.46 0 165.3 1.6e-26 ! -
#> 20 154 307 + no 1 0.47 0 164.7 2.0e-26 ! -
#> 21 256 409 + no 1 0.42 0 164.7 2.1e-26 ! -
#> 22 256 409 + no 1 0.44 0 164.1 2.5e-26 ! -
#> 23 257 410 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 24 256 409 + no 1 0.44 0 163.9 2.7e-26 ! -
#> 25 257 410 + no 1 0.44 0 163.7 2.8e-26 ! -
#> 26 255 408 + no 1 0.44 0 163.6 3.0e-26 ! -
#> 27 216 368 + no 1 0.46 0 162.5 4.5e-26 ! -
#> 28 178 331 + no 1 0.47 0 162.4 4.6e-26 ! -
#> 29 258 411 + no 1 0.42 0 161.0 7.7e-26 ! -
#> 30 156 309 + no 1 0.47 0 160.6 8.7e-26 ! -
#> 31 195 348 + no 1 0.48 0 160.1 1.0e-25 ! -
#> 32 188 340 + no 1 0.50 0 158.7 1.7e-25 ! -
#> 33 223 376 + no 1 0.50 0 156.6 3.7e-25 ! -
#> 34 230 383 + no 1 0.49 0 156.6 3.7e-25 ! -
#> 35 219 371 + no 1 0.45 0 156.5 3.8e-25 ! -
#> 36 263 416 + no 1 0.44 0 155.7 5.0e-25 ! -
#> 37 256 408 + no 1 0.43 0 154.0 9.2e-25 ! -
#> 38 194 346 + no 1 0.48 0 153.9 9.6e-25 ! -
#> 39 115 268 + no 1 0.45 0 151.2 2.5e-24 ! -
#> 40 258 414 + no 1 0.44 0 143.4 4.1e-23 ! -
#> 41 319 475 + no 1 0.45 0 139.6 1.6e-22 ! -
#> 42 188 340 + no 1 0.46 0 121.1 1.2e-19 ! -
#> 43 234 388 + no 1 0.57 0 119.6 2.1e-19 ! -
#> 44 95 248 + no 1 0.51 0 118.1 3.6e-19 ! -
#> 45 94 246 + no 1 0.50 0 115.1 1.0e-18 ! -
#> 46 95 246 + no 1 0.50 0 108.2 1.3e-17 ! -
#> 47 93 246 + no 1 0.44 0 102.4 9.9e-17 ! -
#> 48 218 373 + no 1 0.51 0 93.5 2.4e-15 ! -
#> 49 1 10 + 5' 2 0.50 0 -5.4 5.9e+00 ? -
inferrnal includes a parser for Stockholm alignments, which also
imports column annotations.
msa <- read_stockholm_msa(alnfile)
read_stockholm_msa returns an object of class
StockholmRNAMultipleAlignment. It is also possible to load DNA or AA
alignments using an optional type= argument to read_stockholm_msa.
msa
#> StockholmRNAMultipleAlignment with 48 rows and 164 columns
#> aln names
#> [1] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGCAUGCCUGCUUGAGUGUCA seq45/295-448
#> [2] AACUUUCAACAACGGAUCUCUUGGC...GAGGAGCAUGCCUGUUUGAGUGUCA seq3/236-389
#> [3] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq2/193-346
#> [4] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq28/192-345
#> [5] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq23/194-347
#> [6] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq9/170-323
#> [7] AACUUUCAACAAUGGAUCUCUUGGU...GGGGGGCAUGCCUGUCCGAGCGUCA seq7/191-344
#> [8] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq48/192-345
#> [9] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq5/257-410
#> ... ...
#> [40] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq41/258-414
#> [41] AACUUUCAGCAACGGAUCUCUUGGC...UGUGAGUACACUUGUUUGAGCGUCA seq29/319-475
#> [42] CACUAUUAGCGAUGGAUGUCUUGGA...CAGCAGUAGGUUGGUCUCAGCAUCU seq49/188-340
#> [43] GACUCCCGGCAACGGAUAUCUCGGC...CGAGGGCACGCCUGCCUCUUGGGCG seq20/234-388
#> [44] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq4/95-248
#> [45] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq32/94-246
#> [46] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq24/95-246
#> [47] UAGCAUCAGCGAUUAACGUCUUGGU...AUUGAGUGCACUUGCUUCAGUGUGG seq37/93-246
#> [48] AACACGCAACGGUGGACCACUCGGC...GCCAGCUCUUGCUUGUUGAGCCUGG seq43/218-373
#>
#> GF (file) annotations:
#> BStringSet object of length 1:
#> width seq names
#> [1] 14 Infernal 1.1.4 AU
#>
#> GR (residue) PP annotations:
#> BStringSet object of length 48:
#> width seq names
#> [1] 164 ***********************...********************** seq45/295-448
#> [2] 164 ***********************...********************** seq3/236-389
#> [3] 164 ***********************...********************** seq2/193-346
#> [4] 164 ***********************...********************** seq28/192-345
#> [5] 164 ***********************...********************** seq23/194-347
#> ... ... ...
#> [44] 164 ***********************...********************** seq4/95-248
#> [45] 164 ***********************...********************** seq32/94-246
#> [46] 164 ***********************...********************** seq24/95-246
#> [47] 164 ***********************...********************** seq37/93-246
#> [48] 164 ***********************...********************** seq43/218-373
#>
#> GC (column) annotations:
#> BStringSet object of length 2:
#> width seq names
#> [1] 164 :::::::::::::::::::::::...>>>>>:::::::::::::::::: SS_cons
#> [2] 164 AACuuUuAgCGAUGGAUguCUuG...ggggCAUgccUGuuugAGUGUCa RF
In addition to the alignment itself, the Stockholm output includes the consensus secondary structure and the reference annotation, as defined in the CM, as column (“GC”) annotations named “SS_cons” and “RF”, as well as the posterior probability that each base is aligned in the correct position as residue (“GR”) annotations named “PP”. For more information about these annotations, including the encoding of secondary structure and posterior probabilities, see the Infernal documentation.
msa$GC$SS_cons
#> 164-letter "BString" instance
#> seq: ::::::::::::::::::::::::::::::::::::...<<..____.>>>>>>>>>::::::::::::::::::
msa$GC$RF
#> 164-letter "BString" instance
#> seq: AACuuUuAgCGAUGGAUguCUuGGCUCccGuaUCGA...gg..Uuuu.cccgggggCAUgccUGuuugAGUGUCa
cmalign
If you have sequences which have already been trimmed to contain only
the RNA defined by the CM (possibly truncated, but not extended), then
you can align them to the CM using cmalign. This is much faster than
cmsearch. This example uses the results of cmsearch from the
previous section, after removing gaps.
unaln <- sample_rRNA_5_8S()
unaln_seq <- Biostrings::readRNAStringSet(unaln)
unaln_seq
#> RNAStringSet object of length 48:
#> width seq names
#> [1] 154 AACUUUCAGCAACGGAUCUCUUG...GAGCAUGCCUGCUUGAGUGUCA seq45/295-448
#> [2] 154 AACUUUCAACAACGGAUCUCUUG...GAGCAUGCCUGUUUGAGUGUCA seq3/236-389
#> [3] 154 AACUUUCAGCAACGGAUCUCUUG...GGGCAUGCCUGUUUGAGUGUCG seq2/193-346
#> [4] 154 AACUUUCAGCAACGGAUCUCUUG...GGGCAUGCCUGUUUGAGUGUCG seq28/192-345
#> [5] 154 AACUUUCAACAACGGAUCUCUUG...GGGCAUGCCUGUUCGAGCGUCA seq23/194-347
#> ... ... ...
#> [44] 154 AACUCUCAGCGAUGGAUGACUCG...AAGUAUGUUUGGCUCGGUAUCA seq4/95-248
#> [45] 153 AACUCUCAGCGAUGGAUGACUCG...AAGUAUGUUUGGCUCGGUAUCA seq32/94-246
#> [46] 152 AACUCUCAGCGAUGGAUGACUCG...AAGUAUGUUUGGCUCGGUAUCA seq24/95-246
#> [47] 154 UAGCAUCAGCGAUUAACGUCUUG...GAGUGCACUUGCUUCAGUGUGG seq37/93-246
#> [48] 156 AACACGCAACGGUGGACCACUCG...AGCUCUUGCUUGUUGAGCCUGG seq43/218-373
aln <- cmalign(cm, unaln_seq, cpu = 1)
aln
#> StockholmRNAMultipleAlignment with 48 rows and 164 columns
#> aln names
#> [1] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGCAUGCCUGCUUGAGUGUCA seq45/295-448
#> [2] AACUUUCAACAACGGAUCUCUUGGC...GAGGAGCAUGCCUGUUUGAGUGUCA seq3/236-389
#> [3] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq2/193-346
#> [4] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq28/192-345
#> [5] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq23/194-347
#> [6] AACUUUCAACAACGGAUCUCUUGGU...GAGGGGCAUGCCUGUUCGAGCGUCA seq9/170-323
#> [7] AACUUUCAACAAUGGAUCUCUUGGU...GGGGGGCAUGCCUGUCCGAGCGUCA seq7/191-344
#> [8] AACUUUCAGCAACGGAUCUCUUGGC...GGAGGGCAUGCCUGUUUGAGUGUCG seq48/192-345
#> [9] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq5/257-410
#> ... ...
#> [40] AACUUUCAGCAACGGAUCUCUUGGC...GAGGAGUAUGCCUGUUUGAGUAUCA seq41/258-414
#> [41] AACUUUCAGCAACGGAUCUCUUGGC...UGUGAGUACACUUGUUUGAGCGUCA seq29/319-475
#> [42] CACUAUUAGCGAUGGAUGUCUUGGA...CAGCAGUAGGUUGGUCUCAGCAUCU seq49/188-340
#> [43] GACUCCCGGCAACGGAUAUCUCGGC...CGAGGGCACGCCUGCCUCUUGGGCG seq20/234-388
#> [44] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq4/95-248
#> [45] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq32/94-246
#> [46] AACUCUCAGCGAUGGAUGACUCGAC...CUGAAGUAUGUUUGGCUCGGUAUCA seq24/95-246
#> [47] UAGCAUCAGCGAUUAACGUCUUGGU...AUUGAGUGCACUUGCUUCAGUGUGG seq37/93-246
#> [48] AACACGCAACGGUGGACCACUCGGC...GCCAGCUCUUGCUUGUUGAGCCUGG seq43/218-373
#>
#> GF (file) annotations:
#> BStringSet object of length 1:
#> width seq names
#> [1] 14 Infernal 1.1.4 AU
#>
#> GR (residue) PP annotations:
#> BStringSet object of length 48:
#> width seq names
#> [1] 164 ***********************...********************** seq45/295-448
#> [2] 164 ***********************...********************** seq3/236-389
#> [3] 164 ***********************...********************** seq2/193-346
#> [4] 164 ***********************...********************** seq28/192-345
#> [5] 164 ***********************...********************** seq23/194-347
#> ... ... ...
#> [44] 164 ***********************...********************** seq4/95-248
#> [45] 164 ***********************...********************** seq32/94-246
#> [46] 164 ***********************...********************** seq24/95-246
#> [47] 164 ***********************...********************** seq37/93-246
#> [48] 164 ***********************...********************** seq43/218-373
#>
#> GC (column) annotations:
#> BStringSet object of length 2:
#> width seq names
#> [1] 164 :::::::::::::::::::::::...>>>>>:::::::::::::::::: SS_cons
#> [2] 164 AACuuUuAgCGAUGGAUguCUuG...ggggCAUgccUGuuugAGUGUCa RF
cmbuild
cmbuild is used to create new CMs from annotated multiple sequence
alignments. To illustrate the process, we use the seed alignment for the
5.8S rRNA CM from RFAM. It is included as a sample file in inferrnal.
new_cm <- file.path(tempdir(), "5_8S.cm")
cmbuild(new_cm, msafile = stk_5_8S(), force = TRUE, quiet = FALSE)
This CM is not calibrated, so it cannot be used for cmsearch, but it
can be used in cmalign.
aln2 <- cmalign(new_cm, unaln_seq, cpu = 1)
The resulting alignment is the same as the one created using the CM from
RFAM, because they are based on the same seed alignment, and used the
same (default) options for cmbuild.
all.equal(aln, aln2)
#> [1] TRUE
[^1]: Nawrocki, E.P., Eddy, S.R., 2013. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935.
[^2]: Furneaux, B., Bahram, M., Rosling, A., Yorou, N.S., Ryberg, M., 2021. Long- and short-read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities. Molecular Ecology Resources 21, 1833–1849.
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