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Several recent studies indicate that mammals and other organisms produce large numbers of RNA transcripts that do not correspond to known genes. It has been suggested that these transcripts do not encode proteins, but may instead function as RNAs. However, discrimination of coding and noncoding transcripts is not straightforward, and different laboratories have used different methods, whose ability to perform this discrimination is unclear. In this study, we examine ten bioinformatic methods that assess protein-coding potential and compare their ability and congruency in the discrimination of noncoding from coding sequences, based on four underlying principles: open reading frame size, sequence similarity to known proteins or protein domains, statistical models of protein-coding sequence, and synonymous versus nonsynonymous substitution rates. Despite these different approaches, the methods show broad concordance, suggesting that coding and noncoding transcripts can, in general, be reliably discriminated, and that many of the recently discovered extra-genic transcripts are indeed noncoding. Comparison of the methods indicates reasons for unreliable predictions, and approaches to increase confidence further. Conversely and surprisingly, our analyses also provide evidence that as much as ~10% of entries in the manually curated protein database Swiss-Prot are erroneous translations of actually noncoding transcripts.

There is a current imperative to unravel the hierarchy of molecular pathways that drive the transition of early to established disease in rheumatoid arthritis (RA). Herein, we report a comprehensive RNA sequencing analysis of the molecular pathways that drive early RA progression in the disease tissue (synovium), comparing matched peripheral blood RNA-seq in a large cohort of early treatment-naive patients, namely, the Pathobiology of Early Arthritis Cohort (PEAC). We developed a data exploration website (https://peac.hpc.qmul.ac.uk/) to dissect gene signatures across synovial and blood compartments, integrated with deep phenotypic profiling. We identified transcriptional subgroups in synovium linked to three distinct pathotypes: fibroblastic pauci-immune pathotype, macrophage-rich diffuse-myeloid pathotype, and a lympho-myeloid pathotype characterized by infiltration of lymphocytes and myeloid cells. This is suggestive of divergent pathogenic pathways or activation disease states. Pro-myeloid inflammatory synovial gene signatures correlated with clinical response to initial drug therapy, whereas plasma cell genes identified a poor prognosis subgroup with progressive structural damage.