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Background Physical molecular interactions are the basis of intracellular signalling and gene regulatory networks, and comprehensive, accessible databases are needed for their discovery. Highly correlated transcripts may reflect important functional associations, but identification of such associations from primary data are cumbersome. Therefore, we have constructed a user-friendly online browser supporting identification of putative macromolecular interactions in human peripheral blood based on significant correlations at the transcriptional level. Methods The blood transcriptome was characterized by quantification of 17,328 RNA species, including ~341 mature microRNAs in 105 clinically well-characterized postmenopausal women. Intercorrelation of detected transcripts signal levels generated a matrix with >150 million correlations recognizing the human blood RNA interactome. The correlations with calculated adjusted p-values were made easily accessible by a freely available online browser. Results We document that significant transcript correlations within the giant matrix reflect experimentally documented interactions between select ubiquitous blood relevant transcription factors CREB1, GATA1, and the glucocorticoid receptor (GR, NR3C1). Their responsive genes recapitulated up to 91% of these as significant correlations, and were replicated in a separate cohort of 1204 individual blood samples from the Framingham Heart Study. Furthermore, experimentally documented mRNAs/miRNA associations were also reproduced in the matrix, and their predicted functional co-expression described. The blood transcript correlation browser is freely available at http://app.uio.no/med/klinmed/correlation-browser/blood/index.php and works on all commonly used internet browsers. Conclusions The matrix and web browser ought to be a valuable tool for easy access to the human blood transcriptome and molecular interactions documented as significant correlations at the RNA-level. This application should also be useful as a hypothesis generating tool for identification of regulatory mechanisms.