Reference gene determination software tools | Quantitative PCR data analysis
To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. Several programs help you to choose the best combination of reference genes for your own experiment.
Determines the most appropriate standards and combines them into an index. BestKeeper is an Excel based tool using pair-wise correlations. This application is able to compare expression levels of up to ten housekeeping genes (HKG) together with ten target genes (TG), each in up to hundred biological samples. All data processing operations are based on crossing points.
An online app from Genevestigator that allows users to search for genes that are most stable across a chosen set of samples based on microarray data. This set of samples can be chosen according to experimental conditions or tissue types. For example, if you are performing a RT-qPCR experiment on mouse liver samples, you can use RefGenes to identify the set of genes that are most stable across all microarrays done on mouse liver in Genevestigator.
Provides tools for uploading RT-qPCR data into R, looks for the optimal reference genes, and normalises the data using the ΔΔCq method. NormqPCR allows the user to read RT-qPCR data into R, to deal with undetermined Cq values, to find a suitable reference gene or genes for a given experiment using a method for optimal reference gene selection and to normalise the data via the ΔCq and 2−ΔΔCqnormalisation methods. The user can also use a number of existing bioconductor packages and functions to perform quality control on their data, and can check the adequacy of reference genes visually. Implementing popular optimal reference gene finding algorithms as NormqPCR in the widely-used statistical software for genomic analysis, R, represents an important contribution to the RT-qPCR community, and increases the available options for the analysis of this type of data.
Recognizes a small set of miRNAs to use as reference for data normalization in view of subsequent validation studies. NqA employs high-throughput quantitative real-time polymerase chain reaction (qPCR) data to proceed. It can serve to assess promising miRNAs during the discovery phase based on qPCR high-throughput data. This tool returns frequency distribution of evaluated miRNAs according to the comparison group, a list of the N miRNAs expressed in all the samples.
Evaluates and selects reference genes from extensive experimental datasets. RefFinder exploits computational programs (such as BestKeeper, geNorm, Normfinder or the comparative delta-ct method) to rank and compare candidate reference genes. This software measures the geometric mean of the attributed weights for the overall final ranking.