Zachary Foster

Building a model: developing genomic resources for common milkweed (Asclepias syriaca) with low coverage genome sequencing.

BackgroundMilkweeds (Asclepias L.) have been extensively investigated in diverse areas of evolutionary biology and ecology; however, there are few genetic resources available to facilitate and compliment these studies. This study explored how low coverage genome sequencing of the common milkweed (Asclepias syriaca L.) could be useful in characterizing the genome of a plant without prior genomic information and for development of genomic resources as a step toward further developing A. syriaca as a model in ecology and evolution.ResultsA 0.5× genome of A. syriaca was produced using Illumina sequencing. A virtually complete chloroplast genome of 158,598 bp was assembled, revealing few repeats and loss of three genes: accD, clpP, and ycf1. A nearly complete rDNA cistron (18S-5.8S-26S; 7,541 bp) and 5S rDNA (120 bp) sequence were obtained. Assessment of polymorphism revealed that the rDNA cistron and 5S rDNA had 0.3% and 26.7% polymorphic sites, respectively. A partial mitochondrial genome sequence (130,764 bp), with identical gene content to tobacco, was also assembled. An initial characterization of repeat content indicated that Ty1/copia-like retroelements are the most common repeat type in the milkweed genome. At least one A. syriaca microread hit 88% of Catharanthus roseus (Apocynaceae) unigenes (median coverage of 0.29×) and 66% of single copy orthologs (COSII) in asterids (median coverage of 0.14×). From this partial characterization of the A. syriaca genome, markers for population genetics (microsatellites) and phylogenetics (low-copy nuclear genes) studies were developed.ConclusionsThe results highlight the promise of next generation sequencing for development of genomic resources for any organism. Low coverage genome sequencing allows characterization of the high copy fraction of the genome and exploration of the low copy fraction of the genome, which facilitate the development of molecular tools for further study of a target species and its relatives. This study represents a first step in the development of a community resource for further study of plant-insect co-evolution, anti-herbivore defense, floral developmental genetics, reproductive biology, chemical evolution, population genetics, and comparative genomics using milkweeds, and A. syriaca in particular, as ecological and evolutionary models.

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data

Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual.

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.Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual. Note: This article was previously submitted as a pre-print: Zachary S. L. Foster, Thomas J. Sharpton, Niklaus J. Grünwald. 2016. Metacoder: An R package for manipulation and heat tree visualization of community taxonomic data from metabar-coding. BioRxiv 071019; doi: http://dx.doi.org/10.1101/071019.