BACKGROUND: High-throughput transcriptomics has matured into a very well established and widely utilised research tool over the last two decades. Clinical datasets generated on a range of different platforms continue to be deposited in public repositories provide an ever-growing, valuable resource for reanalysis. Cost and tissue availability normally preclude processing samples across multiple technologies, making it challenging to directly evaluate performance and whether data from different platforms can be reliably compared or integrated. METHODS: This study describes our experiences of nine new and established mRNA profiling techniques including Lexogen QuantSeq, Qiagen QiaSeq, BioSpyder TempO-Seq, Ion AmpliSeq, Nanostring, Affymetrix Clariom S or U133A, Illumina BeadChip and RNA-seq of formalin-fixed paraffin embedded (FFPE) and fresh frozen (FF) sequential patient-matched breast tumour samples. RESULTS: The number of genes represented and reliability varied between the platforms, but overall all methods provided data which were largely comparable. Crucially we found that it is possible to integrate data for combined analyses across FFPE/FF and platforms using established batch correction methods as required to increase cohort sizes. However, some platforms appear to be better suited to FFPE samples, particularly archival material. CONCLUSIONS: Overall, we illustrate that technology selection is a balance between required resolution, sample quality, availability and cost.

MOTIVATION: The RNA-seq paired-end read (PER) protocol samples transcript fragments longer than the sequencing capability of today's technology by sequencing just the two ends of each fragment. Deep sampling of the transcriptome using the PER protocol presents the opportunity to reconstruct the unsequenced portion of each transcript fragment using end reads from overlapping PERs, guided by the expected length of the fragment. METHODS: A probabilistic framework is described to predict the alignment to the genome of all PER transcript fragments in a PER dataset. Starting from possible exonic and spliced alignments of all end reads, our method constructs potential splicing paths connecting paired ends. An expectation maximization method assigns likelihood values to all splice junctions and assigns the most probable alignment for each transcript fragment. RESULTS: The method was applied to 2 x 35 bp PER datasets from cancer cell lines MCF-7 and SUM-102. PER fragment alignment increased the coverage 3-fold compared to the alignment of the end reads alone, and increased the accuracy of splice detection. The accuracy of the expectation maximization (EM) algorithm in the presence of alternative paths in the splice graph was validated by qRT-PCR experiments on eight exon skipping alternative splicing events. PER fragment alignment with long-range splicing confirmed 8 out of 10 fusion events identified in the MCF-7 cell line in an earlier study by (Maher et al., 2009). AVAILABILITY: Software available at http://www.netlab.uky.edu/p/bioinfo/MapSplice/PER.