Prediction of potent shRNAs with a sequential classification algorithm.

We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration.

An optimized microRNA backbone for effective single-copy RNAi.

Short hairpin RNA (shRNA) technology enables stable and regulated gene repression. For establishing experimentally versatile RNAi tools and minimizing toxicities, synthetic shRNAs can be embedded into endogenous microRNA contexts. However, due to our incomplete understanding of microRNA biogenesis, such "shRNAmirs" often fail to trigger potent knockdown, especially when expressed from a single genomic copy.

Simultaneous detection of multiple mRNA markers CK19, CEA, c-Met, Her2/neu and hMAM with membrane array, an innovative technique with a great potential for breast cancer diagnosis.

The objective of this study was mainly to develop and evaluate a membrane array-based method simultaneously detecting the expression levels of a multiple mRNA marker panel in the peripheral blood for used in complementary breast cancer diagnosis. The mRNA markers employed included cytokeratin 19 (CK-19), carcinoembryonic antigen (CEA), c-Met, Her2/neu, and mammaglobin (hMAM). The specimens of peripheral blood were collected from 80 healthy women and 102 female patients with breast cancer.