sangeranalyseR

sangeranalseR is an R package that provides fast, flexible, and reproducible workflows for assembling your sanger seuqencing data into contigs. It adds to a list of already widely-used tools, like Geneious, CodonCode Aligner and Phred-Phrap-Consed. What makes it different from these tools is that it’s free, it’s open source, and it’s in R.

For more information, please check our 📒sangeranalyseR Documentation.

Citation

sangeranalyseR is on Genome Biology and Evolution (GBE) and Bioconductor 3.13 now.

If you use sangeranalyseR in your published work, please cite

Kuan-Hao Chao, Kirston Barton, Sarah Palmer, and Robert Lanfear (2021). "sangeranalyseR: simple and interactive processing of Sanger sequencing data in R" in Genome Biology and Evolution. DOI: doi.org/10.1093/gbe/evab028

Quick Start Guide

1. Installation

(1) System requirements

• R >= 4.0.0 (current)
• Rstudio (recommended)

(2) Install from Bioconductor

To install this package, start R (version “4.0”) and enter:

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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("sangeranalyseR")

(3) Install from GitHub

If you haven’t installed the devtools package before, please install it first:

install.packages("devtools")

Then run the following code in your R console to install the newest version from Github.

library(devtools)

# Download it from the master branch
install_github("roblanf/sangeranalyseR", ref = "master")

# Download it from the develop branch
install_github("roblanf/sangeranalyseR", ref = "develop")

library(sangeranalyseR)

2. A Reproducible Example

Here we demonstrate a simple and reproducible example for using sangeranalyseR to generate a consensus read from 8 sanger ab1 files (4 contigs and each includes a forward and a reverse read).

(1) Prepare your input files & loading

The data of this example is in the sangeranalyseR package; thus, you can simply get its path from the library.

rawDataDir <- system.file("extdata", package = "sangeranalyseR")
parentDir <- file.path(rawDataDir, 'Allolobophora_chlorotica', 'ACHLO')

(2) Load and analyse your data

Run the following on-liner to create the SangerAlignment object.

ACHLO_contigs <- SangerAlignment(ABIF_Directory     = parentDir,
                                 REGEX_SuffixForward = "_[0-9]*_F.ab1$",
                                 REGEX_SuffixReverse = "_[0-9]*_R.ab1$")

(3) Explore your data

Launch the Shiny app to check the visualized results.

launchApp(ACHLO_contigs)

The following figure shows the SangerAlignment Shiny app user interface.

(4) Output your aligned contigs

Write each contig and the aligned consensus read into FASTA files.

writeFasta(ACHLO_contigs)

(5) Generate an interactive report

Last but not least, generate an Rmarkdown report to store all the sequence information.

generateReport(ACHLO_contigs)