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.
Helps investigators optimally design RNA-Seq experiments. RNASeqPower can predict the effect of adding more sequencing depth or biological replicates on calling differential expressed genes. It also allows users to ask one of two questions: How many samples do I need per group? and how small of fold change can I detect given a fixed number of samples?
Allows users to optimize the replicate number and read depth. Scotty is a simple web-based tool that was developed to maximize the statistical power achieved, while excluding configurations. It uses prototype data to quantify the rate at which new RNAs are measured and the degree of variability between replicates. It also enables users to run power analyses using pre-loaded publicly available datasets as prototypes.
Allows users to simulate RNA-Seq experiments in silico. The FLUX CAPACITOR is a pipeline that consists of flexible modules to determine abundance and distribution of read tags according to user-desired experimental protocol, including arbitrary protocols. It also provides an assortment of optional steps for modeling the final library preparation, comprising in silico ligation of adapter sequences and polymerase chain reaction (PCR) amplification.
Provides assistance for internal controls that can assess almost all stages of the RNA-seq workflow. Sequins supports library preparation, sequencing, split-read alignment, transcript assembly, gene expression and alternative splicing. This software is appropriate to evaluate downstream bioinformatic steps, enhance the optimization parameter choice and can be used as normalization factors to compare multiple sample.
Implements a general pilot data-based method for power and sample size determination for high-dimensional genomic data. SSPA allows users to read data as a vector of test statistics and to process the desired estimates. This software offers functions to ease interpretation of results. It can deal with any type of test statistic distribution family so long as both null and alternative are known.
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.
Performs subsampling sequencing reads with binomial sampling. After constructing subsamples and performing an analysis on each, subSeq calculates and visualizes summary metrics about each sequencing depth. It reports metrics representing (i) the power to detect differential expression or abundance, (ii) the accuracy of effect size estimation and (iii) the estimated rate of false discoveries relative to the full experiment.