Genetic Analysis of Pooled Samples

Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) , Jost (2008) , Nei (1973) , Foulley et al. (2006) , Chao et al. (2008) .

Package Overview

pooledpeaks is designed for analyzing genetic data obtained from Fragment Analysis output files (.fsa) of pooled biological samples. It provides functions for a comprehensive analysis pipeline from processing .fsa files, to cleaning the peak data, and conducting population genetic analyses. Some features are listed below and a usage example of the entire pipeline is included as a vignette. Please check out the Contributing Guidelines for information on how to add to this package.

Installation Instructions

You can install the package directly from GitHub using the following instructions:

Open R and copy the following code into your console

Install devtools and pooledpeaks from GitHub

install.packages("devtools")
devtools::install_github("kmkuesters/pooledpeaks")

Install pooledpeaks directly from CRAN

install.packages("pooledpeaks")

Features

For a detailed example of how to apply the functions contained in this package please see the Introduction to Using the pooledpeaks Workflow. Example data can be found on GitHub under the inst/extdata folder including .fsa files and a formatted "Multiplex_frequencies.txt" file for the Genetic Analysis portion.

• Peak Scoring: Process .fsa files and score peaks contained therein.

check_fsa_v_batch()
fsa_metadata()
fsa_batch_imp()
associate_dyes()
score_markers_rev3()

• Data Manipulation: Clean and prepare peak data for downstream analyses.

clean_scores()
lf_to_tdf()
data_manipulation()
Rep_check()
PCDM()
LoadData()

• Population Genetics Analysis:

  • Calculate Gene Identity Matrix and Genetic Distance Matrix

  • Calculate diversity indices

  • Calculate differentiation indices

  • Perform cluster analysis

TypedLoci()
GeneIdentityMatrix()
GeneticDistanceMatrix()
GST()
JostD()
cluster()

• Visualization: Visualize the peak scoring and genetic analysis results.

MDSplot()

Sample Data

The sample .fsa files included in this package are provided for demonstration purposes and originate from two sources:

• Schistosoma haematobium laboratory isolates, used for preliminary testing of the pooledpeaks workflow. These data contain no identifiable or human subject information.
• De-identified Schistosoma mansoni samples from a three studies conducted in Brazil, extracted from discarded human waste. These files were originally used for genetic analysis and are shared here in anonymized form to illustrate compatibility with additional species and data sources.These studies are described in detail by Long et al. (2022), available at https://www.nature.com/articles/s41598-022-04776-0:

These files are intended solely to demonstrate the functionality of the pooledpeaks package and are not for diagnostic or clinical use.To access the example .fsa files included with the package, use the following path within R:

system.file("extdata", package = "pooledpeaks")

The pooledpeaks package was developed by the Blanton Lab as part of Kathleen Kuesters' dissertation.

References:

• Covarrubias-Pazaran et al. (2016) doi:10.1186/s12863-016-0365-6
• Long et al. (2022) doi:10.1038/s41598-022-04776-0
• Jost (2008) doi:10.1111/j.1365-294x.2008.03887.x
• Nei (1973) doi:10.1073/pnas.70.12.3321
• Foulley et al. (2006) doi:10.1016/j.livprodsci.2005.10.021
• Chao et al. (2008) doi:10.1111/j.1541-0420.2008.01010.x