r-bioc-dupradar

BioConductor assessment of duplication rates in RNA-Seq datasets

This BioConductor package provides assessment of duplication rate quality control for RNA-Seq datasets.

r-bioc-affy

BioConductor methods for Affymetrix Oligonucleotide Arrays

This is part of the BioConductor GNU R suite. The package contains functions for exploratory oligonucleotide array analysis.

r-bioc-all

Bioconductor data package used by several bioc tools

Data of T- and B-cell Acute Lymphocytic Leukemia from the Ritz Laboratory at the DFCI (includes Apr 2004 versions)

r-bioc-basilisk

freezing Python dependencies inside Bioconductor packages

Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session.

r-bioc-biocviews

Categorized views of R package repositories

Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages.

r-bioc-hsmmsinglecell

Single-cell RNA-Seq for differentiating human skeletal muscle myoblasts

Skeletal myoblasts undergo a well-characterized sequence of morphological and transcriptional changes during differentiation. In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum- induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell.