Statistical Genomics and Bioinformatics

Linkage analysis in tetraploid potato using dosage information

Next generation sequencing has revolutionized molecular biology in recent years by allowing scientists to sequence complete genomes far quicker and cheaper than previously. Applying this technology to RNA (RNAseq), usually reverse transcribed to cDNA, it also offers an interesting alternative to microarrays as a tool for transcriptomic analysis. As part of the International Barley Genome Sequencing Consortium (IBSC), the James Hutton Institute has conducted an RNAseq experiment comparing RNA sampled from the barley cultivar Morex at different parts and developmental stages of growing plants. BioSS has been involved in analysing these data and particularly in comparing them to microarray data obtained from the same samples.

One of the interesting issues here is which of the two technologies gives more reliable information when the expression of genes/transcripts is low. For this purpose we calculated the correlation between RNAseq and microarray measurements for each of 48000 genes that were measured in both datasets. We then subdivided the genes into 20 classes according to their overall intensity in either RNAseq or microarray samples and averaged the correlation values across all genes in a class. We found that correlations between the two technologies was higher when the classes were defined by the microarray data then when they were defined by the RNAseq data, suggesting that at least for this experiment measurements on the low end of the scale were more reliable for this new technology than they were for microarrays.

average correlationAverage correlation between microarray (blue) and RNAseq (red) data in 20 5%-tiles indicates that reliability of RNAseq data is less dependent on intensity than it is for microarray data.


Further details from: Claus Mayer

Article date 2013


Statistical Genomics and Bioinformatics

Process and Systems Modelling

Statistical Methodology

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