Description: Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present MiClip,a novel model-based approach to identify high-confidence protein-RNA binding sites in CLIP-Seq datasets. This approach assigns confidence value to each binding site on a probabilistic basis. The MiClip package can be flexibly applied to analyze both HITS-CLIP data and PAR-CLIP data.

Authors@R: c(person("Elena", "Filatova", role=c("aut", "cre"), email="filatova@nniiem.ru"), person("Nikolai", "Sakharnov", role="dtc"), person("Dmitry", "Knyazev", role="dtc"), person("Oleg", "Utkin", role="dtc"), person("Blokhina Scientific Research Institute of Epidemiology and Microbiology of Nizhny Novgorod, Russia", role="fnd"))
Description: Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR multiple comparison and the definition of potential biological markers.
Imports: caret, gbm, genefilter, graphics, limma, preprocessCore, pROC,
Blokhina Scientific Research Institute of Epidemiology and Microbiology

Description: Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers.

Title: Metabolomics-Based Models for Imputing Risk
Description: Provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [<doi:10.1016/j.ebiom.2021.103764>, <doi:10.1161/CIRCGEN.119.002610>, <doi:10.1038/s41467-019-11311-9>, <doi:10.7554/eLife.63033>, <doi:10.1161/CIRCULATIONAHA.114.013116>, <doi:10.1007/s00125-019-05001-w>]; and calibrate the metabolic surrogate values to a desired dataset.
Imports: caret, DT, foreach, ggplot2, glmnet, heatmaply, matrixStats,
pkgload, plotly, plyr, pROC, purrr, shiny, shinycssloaders,
shinyFiles, shinydashboard, shinyjs, shinyWidgets, stats,
Packaged: 2022-04-14 12:20:48 UTC; danielebizzarri
Author: Daniele Bizzarri [aut, cre] (<https://orcid.org/0000-0002-6881-273X>),
Marcel Reinders [aut, ths] (<https://orcid.org/0000-0002-1148-1562>),
Erik van den Akker [aut, ths] (<https://orcid.org/0000-0002-7693-0728>)