Introduction to rhinotypeR

Background

The rhinotypeR package is designed to automate the genotyping of rhinoviruses using the VP4/2 genomic region. Having worked on rhinoviruses for a few years, I noticed that assigning genotypes after sequencing was particularly laborious, and needed several manual interventions. We, therefore, developed this package to address this challenge by streamlining the process.

It provides:

  • Distance computation (pairwiseDistances(), overallMeanDistance())
  • Genotype assignment (assignTypes())
  • Visualization (plotFrequency(), plotDistances(), plotTree(), SNPeek(), plotAA())
  • Data helpers (alignToRefs() for optional alignment in R; deleteMissingDataSites() for global gap deletion)
  • Prototype references (getPrototypeSeqs() if you prefer to align externally)

This vignette walks through two practical workflows:

A. Align externally (Your tool of choice) with getPrototypeSeqs() → merge with new data, align with tool of choice → import → assignTypes().

B. Align in R (with packaged prototypes) using alignToRefs() → assignTypes().

Tip: Genotype assignment relies on distances to prototype strains and requires that those prototypes are present in the alignment you pass to assignTypes(). The alignToRefs() helper makes this easy by appending packaged prototypes and aligning jointly.

Installing the package

You can install rhinotypeR from Bioconductor using:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("rhinotypeR")

Load rhinotypeR

# Load package
library(rhinotypeR)

# Load example data
data(rhinovirusVP4, package = "rhinotypeR")

Input expectations

  • Format: FASTA (DNA for VP4/2; AA only for plotAA())
  • Region: VP4/2; sequences should be homologous
  • Length/QC: We recommend ≥350 bp (typical ~420 bp) and careful trimming to the target region
  • Alignment: Required before genotype assignment. Do it either in R (alignToRefs()) or outside R and then import (e.g., with Biostrings::readDNAStringSet())

Option A: Align externally

Prefer your own alignment tool? Use getPrototypeSeqs() to export the packaged prototypes to your local device. These should then be combined with the newly generated sequences, aligned using suitable software, curated and imported into R. There are a multitude of existing alignment software including MAFFT and MUSCLE.

For example, to download to the Desktop directory:

getPrototypeSeqs("~/Desktop")

# You will get "~/Desktop/RVRefs.fasta"
# 2) Combine RVRefs.fasta with your new sequences and align in your tool (e.g., MAFFT).
# 3) Manually curate (trim to VP4/2 span, resolve poor columns), then save as FASTA.

Use the Biostrings package to read the curated alignment FASTA file into R:

aln_curated <- Biostrings::readDNAStringSet(
  system.file("extdata", "input_aln.fasta", package = "rhinotypeR")
)

Option B: Align in R

Use this when you want a fully scripted path in R. alignToRefs() appends packaged references and runs msa::msa() using ClustalW/ClustalOmega/MUSCLE. It can optionally crop the final alignment to the non-gap span of a chosen prototype (trimToRef = TRUE, default; anchor with refName).

# Use package dataset: rhinovirusVP4
# Align user sequences + prototypes in R (choose method)
aln <- alignToRefs(
  seqData   = rhinovirusVP4,
  method    = "ClustalW",           # or "ClustalOmega", "Muscle"
  trimToRef = TRUE,                 # crop to reference non-gap span
  refName   = "JN855971.1_A107"     # default anchor; change if desired
)

aln

Why trimming and gap handling matter:

  • trimToRef = TRUE crops the alignment to the exact span covered by a chosen prototype — helpful when user reads spill over the target region
  • deleteGapsGlobally() performs global deletion (drop any column with a gap in any sequence). This is different from pairwise deletion used within distance calculations. Use with care — this modifies the alignment

Assign types

# Input A
res <- assignTypes(aln_curated, model = "IUPAC", deleteGapsGlobally = FALSE, threshold = 0.105)
## Computing: [========================================] 100% (done)
head(res)
##        query assignedType   distance       reference
## 1 MT177836.1   unassigned 0.11190476  AY040242.1_B97
## 2 MT177837.1   unassigned 0.11190476  AY040242.1_B97
## 3 MT177838.1          B99 0.08809524  AF343652.1_B99
## 4 MT177793.1          B42 0.07380952  AY016404.1_B42
## 5 MT177794.1         B106 0.05617978 KP736587.1_B106
## 6 MT177795.1         B106 0.05912596 KP736587.1_B106
# OR similarly for input B
res <- assignTypes(aln, model = "IUPAC", deleteGapsGlobally = FALSE, threshold = 0.105)
head(res)

Assigning types: rules and outputs

assignTypes() compares each query to prototypes and returns:

  • query (sequence ID)
  • assignedType (or “unassigned”)
  • distance (to nearest prototype — always reported, even if unassigned)
  • reference (prototype accession/name used)

Thresholds: We default to threshold = 0.105 (~10.5%) in line with common practice for VP4/2 (A/C) and close to B; adjust per your analysis or species.

Plot results

plotFrequency(res)

Distances and summaries

# Distances among all sequences
d <- pairwiseDistances(
  fastaData = rhinovirusVP4,
  model = "IUPAC",
  deleteGapsGlobally = FALSE   # set TRUE to apply global deletion inside the function
)
## Computing: [========================================] 100% (done)

The distance matrix looks like:

##            MT177836.1 MT177837.1 MT177838.1 MT177793.1 MT177794.1 MT177795.1
## MT177836.1  0.0000000  0.0000000  0.2380952  0.2119048  0.1938202  0.2113022
## MT177837.1  0.0000000  0.0000000  0.2380952  0.2119048  0.1938202  0.2113022
## MT177838.1  0.2380952  0.2380952  0.0000000  0.1476190  0.2191011  0.2260442
## MT177793.1  0.2119048  0.2119048  0.1476190  0.0000000  0.2050562  0.2186732
## MT177794.1  0.1938202  0.1938202  0.2191011  0.2050562  0.0000000  0.0000000
##            MT177796.1
## MT177836.1 0.21190476
## MT177837.1 0.21190476
## MT177838.1 0.22380952
## MT177793.1 0.21666667
## MT177794.1 0.01404494

To visualize the pairwise distances using a heatmap or a phylogenetic tree:

# Mean distance (overall diversity)
overallMeanDistance(rhinovirusVP4, model = "IUPAC")
## Computing: [========================================] 100% (done)
## [1] 0.3077591
# Visual summaries
## Heatmap
plotDistances(d)  

## Simple tree (from distances)
sampled_distances <- d[1:30, 1:30]
plotTree(sampled_distances, hang = -1, cex = 0.6, 
         main = "A simple tree", xlab = "", ylab = "Genetic distance")

SNP and amino acid views

The SNPeek() function visualizes single nucleotide polymorphisms (SNPs) in the sequences, with a selected sequence acting as the reference. To specify the reference sequence, move it to the bottom of the alignment before importing into R. Substitutions are color-coded by nucleotide:

  • A = green
  • T = red
  • C = blue
  • G = yellow

SNPeek() (nucleotide) and plotAA() (protein) support zooming and highlighting. To show all sequence names, increase the plotting device height (RStudio plot pane or png(height=...)).

# SNP view (nucleotide)
SNPeek(rhinovirusVP4)

# AA view (requires an AAStringSet)

## Option 1 -- read an aligned amino acid sequence
aa_seq <- Biostrings::readAAStringSet(system.file("extdata", "test_translated.fasta", package = "rhinotypeR"))
plotAA(aa_seq)

## Option 2 -- translating DNA
# Remove gaps before translate()
aln_no_gaps <- Biostrings::DNAStringSet(
  gsub("-", "", as.character(rhinovirusVP4))
)
#translate
aa <- Biostrings::translate(aln_no_gaps)
aa_aln <- msa::msa(aa)  # align as plotAA expects equal width
aa_aln <- as(aa_aln, "AAStringSet") # transform to AAStringSet
plotAA(aa_aln)

Tip: show_only_highlighted = TRUE focuses on chosen rows while keeping mismatch context relative to the reference.

Quality and troubleshooting

  • Prototypes missing? assignTypes() will error if prototypes are not present in your alignment. Use Option A (alignToRefs()) or Option B (getPrototypeSeqs() + external align).
  • Misaligned region? Use trimToRef = TRUE and select an appropriate refName that spans your intended VP4/2 region.
  • Gaps distort distances? Consider deleteMissingDataSites() (global deletion), or use a pairwise deletion model where appropriate in distance routines.
  • SNP/AA plots truncated? Increase device height or use the highlight_* options and show_only_highlighted = TRUE.

Conclusion

The rhinotypeR package simplifies the process of genotyping rhinoviruses and analyzing their genetic data. By automating various steps and providing visualization tools, it enhances the efficiency and accuracy of rhinovirus epidemiological studies.

Session info

sessionInfo()
## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] rhinotypeR_1.7.0 BiocStyle_2.41.0
## 
## loaded via a namespace (and not attached):
##  [1] tidyr_1.3.2          sass_0.4.10          generics_0.1.4      
##  [4] stringi_1.8.7        lattice_0.22-9       digest_0.6.39       
##  [7] magrittr_2.0.5       evaluate_1.0.5       grid_4.6.0          
## [10] iterators_1.0.14     fastmap_1.2.0        seqinr_4.2-44       
## [13] foreach_1.5.2        doParallel_1.0.17    jsonlite_2.0.0      
## [16] ape_5.8-1            BiocManager_1.30.27  purrr_1.2.2         
## [19] Biostrings_2.81.2    ade4_1.7-24          codetools_0.2-20    
## [22] jquerylib_0.1.4      cli_3.6.6            rlang_1.2.0         
## [25] crayon_1.5.3         XVector_0.53.0       cachem_1.1.0        
## [28] yaml_2.3.12          tools_4.6.0          parallel_4.6.0      
## [31] dplyr_1.2.1          BiocGenerics_0.59.6  msa_1.45.0          
## [34] buildtools_1.0.0     vctrs_0.7.3          R6_2.6.1            
## [37] stats4_4.6.0         lifecycle_1.0.5      stringr_1.6.0       
## [40] pwalign_1.9.1        Seqinfo_1.3.0        S4Vectors_0.51.3    
## [43] IRanges_2.47.2       MASS_7.3-65          pkgconfig_2.0.3     
## [46] bslib_0.11.0         pillar_1.11.1        glue_1.8.1          
## [49] Rcpp_1.1.1-1.1       xfun_0.57            tibble_3.3.1        
## [52] GenomicRanges_1.65.0 tidyselect_1.2.1     sys_3.4.3           
## [55] knitr_1.51           htmltools_0.5.9      nlme_3.1-169        
## [58] rmarkdown_2.31       maketools_1.3.2      compiler_4.6.0      
## [61] MSA2dist_1.17.0