paper.md

In FranzKrah/rGUIDANCE: rGUIDANCE – alignment confidence score computation in R

title: 'rGUIDANCE – alignment confidence score computation in R' authors: - affiliation: 1, 2 name: Franz-Sebastian Krah orcid: 0000-0001-7866-7508 - affiliation: 2 name: Christoph Heibl orcid: 0000-0002-7655-3299 date: "06 August 2019" output: pdf_document: default html_document: df_print: paged bibliography: paper.bib tags: - multiple sequence alignment - phylogeny - genome - guide tree - R toolbox - evolution affiliations: - index: 1 name: Plant Biodiversity Research Group, Department of Ecology & Ecosystem Management, Technical University of Munich, 85354 Freising, Germany - index: 2 name: Bavarian Forest National Park, 94481 Grafenau, Germany

Summary

R has become the toolbox of many researchers in ecology and evolutionary biology, both fields increasingly integrating molecular phylogenetic information. This modular toolbox allows to implement pipelines from sequence retrieval and alignment, to phylogeny estimation and high-end visualization of results. One essential step within this pipeline is currently missing from the R package ecosystem: detection of unreliably aligned regions within multiple sequence alignment (MSA). Although alignments are fundamental to phylogenetically informed analyses, they often contain extended unreliable regions. The alignment confidence score GUIDANCE demonstrated high accuracy in detecting such regions of low quality. Here we introduce the R package rGUIDANCE, which fully implements the alignment confidence algorithms GUIDANCE, HoT and GUIDANCE2. We will demonstrate the core functionality of rGUIDANCE and how it can be easily integrated into a phylogeny inference pipeline. rGUIDANCE is a free and open-source R package, available via GitHub at https://github.com/FranzKrah/rGUIDANCE\

Introduction

The free software environment for statistical computing and graphics, R, has developed as an indispensable toolbox in ecology and evolution and in data science in general [@Muenchen2014]. A myriad of introductory books to statistics, ecological and evolutionary analysis are based on R (e.g., https://www.r-project.org/doc/bib/R-books.html, UseR! series). Within ecology and evolution, a growing body of research relies on phylogenetic information [@Cavender-BaresKozak2009], e.g., phylogenetic diversity or ancestral character reconstruction. Once users have gained basic knowledge, the modular R toolbox allows to code pipelines, e.g., for phylogeny inference, meeting individual’s needs. Such a pipeline may contain the following steps and R packages (only some are listed): (1) sequence retrieval, either manual, semi-automated (ape) or automated (phylotaR), (2) sequence handling (ape, seqinr), (3) multiple sequence alignments (MSA) via interface functions to MSA programs (ape, ips) (4) phylogeny estimation based on MSA via interface functions to tree inference programs (ape, phyclust, ips) (5) divergence time estimation (ape); (6) comparative phylogenetic methods such as ancestral character estimation (ape, phytools, geiger) or phylogenetic community ecology such as phylogenetic diversity (picante, vegan, MicEco) and (7) phylogeny visualization (ape, phytools or ggtree).

Although it is possible to implement such a pipeline in the R framework, an essential step is currently not implemented: MSA confidence estimation, which would be situated between step (3) and (4). Currently a typical workflow as outlined above includes a single MSA, which is assumed to be correct. However, benchmark studies show that alignment accuracy is often low, depending on the MSA program and settings used [@NuinWang2006; @ThompsonLinard2011]. Numerous programs have been developed to filter MSA (e.g., trimAI, GBlocks) from ambiguously aligned regions, however simple filtering of alignments was shown to worsen phylogeny estimation in many cases [@TanMuffato2015]. One promising option is to include alignment uncertainty in the phylogeny inference to down-weight sites of low reliability. Such an option is available via a combination of GUIDANCE [@PennPrivman10] and RAxML [@Stamatakis14]. The GUIDANCE column score can be passed as individual weights to each column of the alignment to RAxML via the –a flag (for a practical example: @KrahBassler18). However, GUIDANCE is currently not available in R and thus integration of alignment reliability is hampered. Here we thus introduce the R package rGUIDANCE and its core functionality. rGUIDANCE fully implements GUIDANCE, HoT and GUIDANCE2, which were shown to detect unreliable areas with high accuracy [@PennPrivman10; @SelaAshkenazy15; @LandanGraur08]. rGUIDANCE further implements basic MSA comparison tools using Rcpp to facilitate further development of the R toolbox in ecology and evolution.

Core package functionality

Here, we present the core functions of the rGUIDANCE package. rGUIDANCE makes use of several R packages that allow interaction with sequence data, e.g., ape, ips, adephylo. Further, rGUIDANCE uses Rcpp to implement MSA comparison tools, which are the basis for GUIDANCE computations. This includes the Cmatrix computation as well as the sum-of-pairs score (SP) [@PennPrivman10]. At the core of rGUIDANCE are the functions guidance, HoT and guidance2, which implement algorithms of the same name. Please note that the accuracy of GUIDANCE and rGUIDANCE scores in identifying alignment errors is almost identical (Fig. 1). Here we present how rGUIDANCE fits into the R ecosystem to build a basic pipeline for phylogeny inference integrating GUIDANCE column confidence scores (Fig. 2).

####################### Pipeline ####################### 
## [1] Use phylotaR to retrieve sequence clusters
# for detailed code see Vignette of R package

## [2] Load sequences for the first sequence cluster
cl0 <- read.FASTA("PATHTO/cl0.fas")

## [3] Use GUIDANCE to calculate column score (CS)
g <- guidance(cl0, ncore = 1, msa.exec = "/usr/local/bin/mafft")
msa0 <- g@msa
sc <- scores(g, "column_raxml", na.rm = FALSE)

## [4] Use alignment and CS for phylogeny inference
tr.w <- raxml(msa0, m = "GTRGAMMA", f = "a", 
              N = 100, p = 1234, x = 1234,
              exec = "PATHTO/raxmlHPC-PTHREADS-AVX", 
              threads = 12, weights = sc$column_raxml,
              outgroup = "Helvella_aestivalis")

## [5] Divergence time estimation
tr.w.ult <- chronos(tr.w$bipartitions)

## [6] Phylogeny visualization
plot(tr.w.ult)

For more detailed and up-to-date examples and tutorials, see the rGUIDANCE GitHub page and vignettes therein.

Conclusions

With the R package rGUIDANCE we hope to enrich the R toolbox for ecological and evolutionary research. rGUIDANCE provides implementations of well-performing MSA reliability score programs, GUDIANCE, HoT and GUIDANCE2. Alignment column scores can be computed and easily integrated into a phylogeny pipeline as was exemplarily demonstrated. The R package further provides further functions such as sum-of-pairs scores from MSA comparisons. These functions facilitate the modular development of further MSA reliability scores. Finally, we hope that more phylogeny-based analyses will integrate alignment uncertainty in the phylogeny inference step and thus decrease bias in ecological and evolutionary studies.

References

FranzKrah/rGUIDANCE documentation built on Jan. 27, 2024, 10:42 a.m.