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Computational biology tools

The documentation below was prepared for worshops and R pacakges we wrote:. Reproducible Science in R. Population Genetics in R. Documentation for graphing heat trees using MetacodeR. Https:/ grunwaldlab.github.io/.

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Computational biology tools | grunwaldlab.github.io Reviews

https://grunwaldlab.github.io

The documentation below was prepared for worshops and R pacakges we wrote:. Reproducible Science in R. Population Genetics in R. Documentation for graphing heat trees using MetacodeR. Https:/ grunwaldlab.github.io/.

INTERNAL PAGES

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About

https://grunwaldlab.github.io/about.html

We hope you enjoy the shared resources developed by the awesome people in our lab. Https:/ grunwaldlab.github.io/.

2

Introduction

https://grunwaldlab.github.io/Reproducible-science-in-R

Reproducible Science in R. The Basics of R. Reproducible science or research provide the ability to replicate research results from a published study. This idea ultimately also includes the full computational environment comprising code and data to replicate the analysis. Over recent years our research group has settled on using the R statistical and computing environment together with R studio, R markdown and latex with some or our own scripts and packages to conduct reproducible science.

3

MetacodeR user documentation

https://grunwaldlab.github.io/metacoder_documentation

Digital PCR: Comparing databases. Zachary S. L. Foster and Niklaus J. Grünwald. Metabarcoding is revolutionizing microbial ecology and presenting new challenges:. Numerous database formats make taxonomic data difficult to parse, combine, and subset. Stacked bar charts, commonly used to depict community diversity, lack taxonomic context and are limited by the use of color to represent taxa. Barcode loci and primers are a source of under-explored bias. Primer specificity can be estimated with. Most databas...

4

Population genetics in R

https://grunwaldlab.github.io/Population_Genetics_in_R

Population Genetics in R. 2 Getting ready to use R. 5 Population strata and clone correction. 6 Locus-based statistics and missing data. 7 Genotypic evenness, richness, and diversity. 10 Minimum Spanning Networks. 12 Discriminant analysis of principal components (DAPC). A3 Introduction to R. Population genetics in R. Niklaus J. Grünwald, Zhian N. Kamvar, and Sydney E. Everhart. 2015, Corvallis, Oregon, USA. Andy Jones, Assistant Professor, Oregon State University, Corvallis, OR, 2014.

5

Computational biology tools

https://grunwaldlab.github.io/index.html

The documentation below was prepared for worshops and R pacakges we wrote:. Reproducible Science in R. Population Genetics in R. Documentation for graphing heat trees using MetacodeR. Https:/ grunwaldlab.github.io/.

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