r-bioc-scran

BioConductor methods for single-cell RNA-Seq data analysis

Implements functions for low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.

srain

graphical IRC client based on GTK+

Srain is a modern IRC client with a GTK+ GUI that is: * Fully open source * RFC 1459, 2812 compatible * Partial IRCv3 support * Multi-platform support (Linux, Windows, macOS and BSD) Capabilities of a modern graphical IRC client: * Convenient connection panel * Interactive channel search * Forward message in one click * URL preview * Desktop notification * Special optimization for bridge/relay bot Power-user capabilities: * Anything can be done via commands * Fine-grained configuration with hot update support * Regex based message render and filter mechanisms

staticsite

Static site generator

Static site generator based on markdown and jinja2.

r-bioc-shortread

GNU R classes and methods for high-throughput short-read sequencing data

This BioConductor module is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources.

r-bioc-tfbstools

GNU R Transcription Factor Binding Site (TFBS) Analysis

TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software.

r-bioc-titancna

Subclonal copy number and LOH prediction from whole genome sequencing

Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data.