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<!-- badges: start --> [![License: MIT](]( [![Travis build status](]( ![documentation build status]( ![os]( <!-- badges: end --> # 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 <b>📒<a href="">sangeranalyseR Documentation</a></b>. <br> ## 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: [](** <br> ## 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: ``` R 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: ``` R install.packages("devtools") ``` Then run the following code in your R console to install the newest version from Github. ``` R 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) ``` <br> ### 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. ``` R 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. ``` R 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. ``` R launchApp(ACHLO_contigs) ``` The following figure shows the *SangerAlignment* Shiny app user interface. <img src="" style="width:100%"> #### (4) Output your aligned contigs Write each contig and the aligned consensus read into FASTA files. ``` R writeFasta(ACHLO_contigs) ``` #### (5) Generate an interactive report Last but not least, generate an Rmarkdown report to store all the sequence information. ``` R generateReport(ACHLO_contigs) ```