Loading multiple alignments into R

Overview

Most catGenes workflows begin with a set of individual DNA alignments imported into R as a named list. This article explains how to load multiple alignments, organize them consistently, and prepare them for downstream functions such as catfullGenes() and catmultGenes().

The examples below use alignments in NEXUS format read with ape::read.nexus.data(), which is the format most commonly used in catGenes concatenation workflows.

Before you start

Before loading alignments into R, make sure that:

  • taxon labels are consistently formatted across loci
  • file names are informative and stable
  • each alignment corresponds to a single gene or locus
  • all input files are stored in the same folder if you want to import them together

For guidance on sequence label formatting, see the article on standardizing DNA alignments for catGenes.

Loading example alignments included in catGenes

The package includes example alignments for Vataireoid legumes. These files can be loaded directly from the installed package and are useful for learning the basic workflow.

library(catGenes)

genes <- list.files(system.file("DNAlignments/Vataireoids",
                                package = "catGenes"))

Vataireoids <- list()

for (i in genes) {
  Vataireoids[[i]] <- ape::read.nexus.data(
    system.file("DNAlignments/Vataireoids", i, package = "catGenes")
  )
}

names(Vataireoids) <- gsub("[.].*", "", names(Vataireoids))

The resulting object is a named list of DNA alignments.

names(Vataireoids)

Each element of the list corresponds to a single alignment, and the list names indicate the gene or locus.

Loading alignments from your own directory

If your DNA alignments are stored in a folder on your computer, you can import them using list.files() and a loop.

Loading files with shared prefixes

In some datasets, alignment files may include a dataset prefix before the gene name, such as:

Vataireoids_ITS.nex
Vataireoids_matK.nex
Vataireoids_trnDT.nex

These files can still be loaded normally, and the list names can then be cleaned to retain only the locus name.

library(catGenes)

genes <- list.files("path_to_DNA_alignments_folder")
my_alignments <- list()

for (i in genes[grepl("Vataireoids", genes)]) {
  my_alignments[[i]] <- ape::read.nexus.data(
    paste0("path_to_DNA_alignments_folder/", i)
  )
}

names(my_alignments) <- gsub(".*_(.+)[.].*", "\\1", names(my_alignments))

This approach is useful when several projects or clades are stored in the same directory.

Loading alignments one by one

If you prefer, you can import each alignment separately and then combine them into a list.

library(catGenes)

ITS   <- ape::read.nexus.data("ITS.nex")
matK  <- ape::read.nexus.data("matK.nex")
trnDT <- ape::read.nexus.data("trnDT.nex")

my_alignments <- list(
  ITS = ITS,
  matK = matK,
  trnDT = trnDT
)

This approach is practical for small datasets or when you want more explicit control over the list structure.

Common issues

File paths are incorrect

If R cannot find your files, check that the path provided to list.files() or ape::read.nexus.data() is correct.

Files are not in NEXUS format

The examples in this article assume NEXUS input. If your files are in another format, you may need to convert them first using convertAlign().

List names are not informative

If you do not remove file extensions or prefixes, the resulting list names may be difficult to interpret in downstream analyses. Clean list names as early as possible.

Taxa do not match across loci

If concatenation behaves unexpectedly, the problem is often due to inconsistent taxon labels rather than the import step itself. In that case, revisit the alignment standardization tutorial.

Next step

Once your alignments are loaded into R as a named list, the next step is to choose the appropriate concatenation workflow:

  • use catfullGenes() when each species has a single sequence per locus
  • use catmultGenes() when one or more species have multiple accessions

These workflows are described in the dedicated tutorials on concatenating multilocus datasets with catGenes.