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Niklaus J Grunwald

Publications and source records attributed to Niklaus J Grunwald.

2 recordsLinked to original sources

MetacodeR: An R package for manipulation and heat tree visualization of community taxonomic data from metabarcoding

Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs; these graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. We developed MetacodeR, an R package for easily parsing, manipulating, and plotting hierarchical data. To accomplish this, MetacodeR provides a function to parse most text-based formats that contain taxonomic classifications, taxon names, taxon IDs, or sequence IDs. This parsed data can then be subset, sampled, and ordered using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function allows for the quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to color and size of tree nodes and edges. MetacodeR also allows exploration of barcode primer bias by integrating functions to run digital PCR. MetacodeR has been designed for data from metabarcoding research, but can easily be applied to any data that has a hierarchical component such as gene ontology, gene expression data, or geographic location data. Our package complements currently available tools for community analysis and is provided open source with extensive online user manuals.

Bioinformatics

VcfR: an R package to manipulate and visualize VCF format data

Software to call single nucleotide polymorphisms or related genetic variants has converged on the variant call format (VCF) as the output format of choice. This has created a need for tools to work with VCF files. While an increasing number of software exists to read VCF data, many only extract the genotypes without including the data associated with each genotype that describes its quality. We created the R package vcfR to address this issue. We developed a VCF file exploration tool implemented in the R language because R provides an interactive experience and an environment that is commonly used for genetic data analysis. Functions to read and write VCF files into R as well as functions to extract portions of the data and to plot summary statistics of the data are implemented. VcfR further provides the ability to visualize how various parameterizations of the data affect the results. Additional tools are included to integrate sequence (FASTA) and annotation data (GFF) for visualization of genomic regions such as chromosomes. Conversion functions translate data from the vcfR data structure to formats used by other R genetics packages. Computationally intensive functions are implemented in C++ to improve performance. Use of these tools is intended to facilitate VCF data exploration, including intuitive methods for data quality control and easy export to other R packages for further analysis. VcfR thus provides essential, novel tools currently not available in R.

Bioinformatics