1 - 19 of 19 results

SSA / Submodular Selection of Assays

Chooses a diverse panel of genomic assays that leverages methods from submodular optimization. SSA serves as a model for how submodular optimization can be applied to other discrete problems in biology. This method is computationally efficient, results in high-quality panels according to several quality measures, and is mathematically optimal under some assumptions. It can be used partway through the investigation of a cell type, when several assays are already available. The tool can determine the most informative next experiments to perform.


Simulates and evaluates differential expression from bulk and especially single-cell RNA-seq data. powsimR can not only estimate sample sizes necessary to achieve a certain power, but also informs about the power to detect differential expression (DE) in a data set at hand. This module integrates estimated and simulated expression differences to calculate marginal and conditional error matrices. To calculate these matrices, the user can specify nominal significance levels, methods for multiple testing correction and gene filtering schemes.

ssizeRNA / Sample Size Calculation for RNA-Seq Experimental Design

Implements a procedure for sample size calculation while controlling false discovery rate (FDR) for RNA-sequencing experimental design. ssizeRNA procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. It can calculate sample size to achieve a desired average power while controlling FDR.

BwB / BioDepot-workflow-Builder

Assists users in creating bioinformatics workflows. BwB provides multiple interchangeable and encapsulated widgets that allow users to test alternative algorithms and to obtain different outputs. It uses Docker containers as individual components of the workflows. The widgets, with the help of a Docker container, can execute software tools that can be written in another programming language, requires different system configurations and/or be developed by other research groups.


Aids biologists in correctly designing their RNA-seq experiments. RNAtor is a mobile application for Android platforms a biologist-friendly and easy-to-use platform to design RNA-seq experiments based on certain user inputs. Where sample is limiting, RNAtor provides guidelines to produce required number of reads to detect differentially expressed transcripts. The recommendations provided by RNAtor are based on an exhaustive combination of simulation studies and validation with real RNA-seq datasets.

RSPS / RNA-Seq Power Simulation

Uses an efficient simulation algorithm to empirically determine statistical power and necessary sample size for RNA-Seq studies. RSPS simulates the data from Poisson (no overdispersion) or Negative Binomial distribution (overdispersion). The package allows one to monitor the progress of the function when the power is being computed. There are two functions for providing plots of the estimated power for given sample size and estimated sample size to achieve desired power.

RNAseqPS / RnaSeqSampleSize

Provides a sample size estimation method based on the distributions of gene read counts and dispersions from real data. RnaSeqSampleSize uses datasets from the user’s preliminary experiments or The Cancer Genome Atlas (TCGA) as reference. The read counts and their related dispersions will be selected randomly from the reference based on their distributions, and from that, the power and sample size will be estimated and summarized. RnaSeqSampleSize is available as a Bioconductor package for local use and as an user friendly web interface.


Captures the dispersion in the data. RNASeqPowerCalculator is an analysis tool that can realistically reveal the relationships among parameters relevant to the power analysis. It can serve as a practical reference under the budget constraint of RNA-Seq experiments. Finally, it can be applied more generally to complex multi-factor designs that can be modelled through the general linear model (GLM) framework, such as time series, multi-level designs and blocking designs.


Designs, documents and reproduces biological experiments. ProtocolNavigator provides an interactive environment where experimentalists can emulate their laboratory practice on a virtual laboratory bench. The software consists of three panels with linked functionality and display: Inventory panel, Bench panel, and Map panel. It introduces an intuitive mechanism for identifying key factors for reproducibility as well as the foundation to convey best practices in quantitative terms.