We introduce a new technique to associate a spanning tree to the average linkage cluster analysis.We term this tree as the Average Linkage Minimum Spanning Tree. We also introduce a technique to associate a value of reliability to links of correlation based graphs by using bootstrap replicas of data. Both techniques are applied to the portfolio of the 300 most capitalized stocks traded at New York Stock Exchange during the time period [2001][2002][2003]. We show that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimum Spanning Tree does. We also show that the average reliability of links in the Minimum Spanning Tree is slightly greater than the average reliability of links in the Average Linkage Minimum Spanning Tree.

ComiR is a web tool for combinatorial microRNA (miRNA) target prediction. Given an messenger RNA (mRNA) in human, mouse, fly or worm genomes, ComiR computes the potential of being targeted by a set of miRNAs, each of which can have zero, one or more targets on its 3′untranslated region. In determining the regulatory potential of an mRNA from a set of miRNAs, ComiR uses user-provided miRNA expression levels in a combination of appropriate thermodynamic modeling and machine learning techniques to make more accurate predictions. For each gene, ComiR returns the probability of being a functional target of a set of miRNAs, which depends on the relative miRNA expression levels. The tool provides a user-friendly interface to input a miRNA expression table containing many sample information and filter out the most relevant miRNAs. ComiR results can be downloaded or visualized on a table, which can then be used to select the most relevant targets and to compare the results obtained with different miRNA expression input. ComiR is freely available for academic use at http://www.benoslab.pitt.edu/comir/.

Early heart development takes place through a complex series of steps, including the induction of cardiac mesoderm, formation of the cardiovascular progenitor cells and the commitment of cardiovascular lineage cells, such as cardiomyocytes (CMs), smooth muscle cells (SMCs) and endothelial cells (ECs). Recently, microRNAs, a family of endogenous, small non-coding RNAs, have been implicated as critical regulators at the posttranscriptional level in cardiogenesis as well as cardiovascular disease. Previous studies demonstrated that microRNA-1 (miR-1) could promote cardiac differentiation from mouse and human embryonic stem (ES) cells. However, the underlying mechanism largely remained unclear. We performed microRNA deep sequencing among human ES cells, ES cell derived-multipotent cardiovascular progenitors (MCPs), and MCP-specified CMs, ECs, and SMCs. A specific enrichment of miR-1 was found in CMs, not in SMCs or ECs, implying a key role of miR-1 in determining cardiovascular commitment from MCPs. When overexpressed in human pluripotent stem cells, miR-1 enhanced the expression of key cardiac transcriptional factors and sarcomeric genes. Importantly, we found miR-1 promoted CM differentiation and suppressed EC commitment from MCPs by modulating the activities of WNT and FGF signaling pathways. FZD7 and FRS2 were confirmed as miR-1 targets using luciferase reporter assay and western blot. Overall, this study reveals a switch role of miR-1 at early human cardiovascular commitment stage via suppressing both WNT and FGF signaling pathways.