Title: Prediction of Antimicrobial Peptides
Description: Predicts antimicrobial peptides using random forests trained on the
n-gram encoded peptides. The implemented algorithm can be accessed from
both the command line and shiny-based GUI. The AmpGram model is too large
for CRAN and it has to be downloaded separately from the repository:
Imports: biogram, devtools, pbapply, ranger, shiny, stringi
Suggests: DT, ggplot2, pander, rmarkdown, shinythemes, spelling

Title: Statistical Analysis Of Amplicon Data Of The Same Sample To
Authors@R: c(person(given = "Anja", family = "Lange", role =
c("aut", "cre"), email = "anja.lange@uni-due.de"),
person(given = "Daniel", family = "Hoffmann", role = "aut", email
Depends: R (>= 2.10), stats, ggplot2, xtable
Description: Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads.

Title: Statistical Analysis of Amplicon Data of the Same Sample to

Author: Anja Lange [aut, cre], Daniel Hoffmann [aut]

Authors@R: c(person("Michal", "Burdukiewicz", email =
"michalburdukiewicz@gmail.com", role = c("cre", "aut")),
person("Piotr", "Sobczyk", email = "sobbombo@gmail.com", role = "ctb"),
person("Stefan", "Roediger", email = "stefan.roedigier@b-tu.de", role = "ctb"))
Description: Predicts amyloid proteins using random forests trained on the
both the command line and shiny-based GUI.
Suggests: DT, ggplot2, knitr, markdown, rmarkdown