In kaischmid/SangeR: SangeR

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This vignette describes the function and use of the sangeR package

Abstract

In the era of Next Generation Sequencing and beyond, the Sanger technique is still widely used for variant verification of inconclusive or ambiguous high-throughput sequencing results or as a low-cost molecular genetical analysis tool for single targets in many fields of study. Many analysis steps need time-consuming manual intervention. Therefore, we present here a pipeline-capable high-throughput solution with an optional Shiny web interface, that provides a binary mutation decision of hotspots together with plotted chromatograms including annotations via flat files based on R and Nextflow.

Availability

All components of the PARrOT R package and the belonging Python scripts are available for download from the Github repository PARrOT.

Installation

To install the R-package the following commands have to be executed in R.

install.packages(c("BiocManager","stringr","ggplot2","reshape2","seqinr","devtools")
BiocManager::install(c("Biostrings","CrispRVariants","biomaRt","sangerseqR"))
library("devtools")
install_github("https://github.com/kaischmid/SangeR")

Get the Docker container here.

Please make sure to check our other projects at Giessen Institute of Neuropathology.

Usage

Input data

The pipeline needs the following files as input. We divided the pipeline into an online and an offline mode.

Online

The pipeline gathers the necessary reference data on demand from online resources, but needs a stable internet connection.

• In this case you only have to provied the ab1 file which is supposed to be analyzed

• optional you can provide a POI (Point Of Interest) file to investigate every given file for mutations at your desired positions.

• The format of this file shall be a .csv file containing the positions like follows:
• "Points of interest"
• "1" "5:1295228"
• "2" "5:1295250"
• "<count>" "<chromsome>":"<position on chromosome>"

Offline

The pipeline needs a prepared local reference database for the offline use.

• In this case you have to provied the ab1 file which is supposed to be analyzed

• Additional you have to provide the needed mart ressources for the genes you want to analyze

• optional you can provide a POI (Point Of Interest) file to investigate every given file for mutations at your desired positions.

• The format of this file shall be a .csv file containing the positions like follows:
• "Points of interest"
• "1" "5:1295228"
• "2" "5:1295250"
• "<count>" "<chromsome>":"<position on chromosome>"

PARAMETERS

The following parameters can be set:

• filename: location of a ab1 file.
• delimiter: delimiter in genename/ID of ab1 file
• ID_pos: position of ID in genename/ID of ab1 file
• genename_pos: position of genename in genename/ID of ab1 file
• cutoff: cutoff for Basecall of the fastq. Default: 0.05
• min_seq_len: minimum sequence length for Basecall of the fastq. Default: 20
• offset: Offset for Basecall of the fastq. Default: 33
• upstream: region before gene to be loaded into ref_seq
• host: Host to connect to. Defaults to grch37.ensembl.or
• dataset: Dataset you want to use. To see the different datasets available within a biomaRt you can e.g. do: mart = useMart("ensembl"), followed by listDatasets(mart). Default: hsapiens_gene_ensembl
• biomart: BioMart database name you want to connect to. Possible database names can be retrieved with the function listMarts. Default: ensembl
• POI: file with list of points of interest

OUTPUT

The pipeline generates the following output:

• Histogramm for each mutated position/selected POI

• .csv with the found mutations for all given files

Example

Testset

Feel free to test the pipeline with our provided test set which you can find under: https://zenodo.org/record/5865470#.YeXufi9XZpQ

First clone the repository to your machine: git clone https://github.com/kaischmid/SangeR.git

Then run the following command:

./main.nf

kaischmid/SangeR documentation built on April 15, 2022, 2:22 a.m.