Title: Library Driven Compound Profiling in GC-MS Data
Author: Xavier Domingo-Almenara, Alexandre Perera-Lluna, Gabriel Vivo-Truyols, Gabriela Venturini, Maria Vinaixa, Jesus Brezmes.
Imports: osd, HiClimR, XML, signal, methods
Description: Automated quantification of metabolites by targeting mass spectral/retention time libraries into full scan-acquired gas chromatography - mass spectrometry (GC-MS) chromatograms. Baitmet outputs a table with compounds name, spectral matching score, retention index error, and compounds area in each sample. Baitmet can automatically determine the compounds retention indexes with or without co-injection of internal standards with samples.
Packaged: 2017-01-13 21:25:18 UTC; xavidomingo

Title: Library Driven Compound Profiling in Gas Chromatography - Mass
Author: Xavier Domingo-Almenara, Jesus Brezmes, Gabriela Venturini, Gabriel Vivo-Truyols, Alexandre Perera-Lluna, Maria Vinaixa.
Packaged: 2017-05-30 23:05:18 UTC; xavidomingo

Title: Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets
Description: Several implementations of a novel Bayesian hierarchical statistical model of nucleotide
recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.)
for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2022) <doi:10.1101/2022.09.02.505697>).
NR-seq is a powerful extension of RNA-seq that provides information about the kinetics
of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking
in standard RNA-seq data. The statistical model makes maximal use of these high-throughput
datasets by sharing information across transcripts to significantly improve
uncertainty quantification and increase statistical power. 'bakR' includes a maximally
efficient implementation of this model for conservative initial investigations of datasets. 'bakR'
also provides more highly powered implementations using the probabilistic programming language
'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test
adjusted statistical inference with the output of these model implementations to
help biologists separate signal from background. Methods to automatically visualize key
results and detect batch effects are also provided.
Imports: purrr, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1),
rstan, rstantools (>= 2.1.1), dplyr, stats, magrittr, Hmisc,
Suggests: rmarkdown, knitr, DESeq2, pheatmap
Author: Isaac Vock [aut, cre] (<https://orcid.org/0000-0002-7178-6886>)