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Experimental design software tools | RNA sequencing data analysis

RNA-seq is widely used to determine differential expression of genes or transcripts as well as identify novel transcripts, identify allele-specific expression, and precisely measure translation of transcripts. Thoughtful experimental design and choice of analysis tools are critical to ensure high-quality data and interpretable results. Important considerations for experimental design include number of replicates, whether to collect paired-end or single-end reads, sequence length, and sequencing depth.

Source text:
(Williamset al., 2014) RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis. Curr Protoc Hum Genet.

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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.
Flux Simulator
Simulates RNA-Seq experiments. Flux Simulator is a computer pipeline that comprises explicit models for the processes that determine abundance and distribution of read tags according to the specified experimental protocol. The software breaks down heterogeneous sample preparation protocols into their atomic steps and implements these steps as modules that can be flexibly joined, allowing simulation of arbitrary protocols. It also provides optional steps to model the final library preparation.
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.
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.
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.
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.
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