Identification of significant miRNA-mRNA pairs using association measures based on unmatched and matched data.

<p>Identification of significant miRNA-mRNA pairs using association measures based on unmatched and matched data.</p

Generic table for measuring association between a miRNA-mRNA pair using unmatched data.

<p>Generic table for measuring association between a miRNA-mRNA pair using unmatched data.</p

Number of mRNAs associated with different biological processes in the RB deletion group.

<p>Number of mRNAs associated with different biological processes in the RB deletion group.</p

Relative expression levels of hsa-miR-320 and two of its predicted targets in samples with RB deletion.

<p>Relative expression levels of hsa-miR-320 and two of its predicted targets in samples with RB deletion.</p

Association measures for co-analysis of miRNA and mRNA expression profiles.

<p>Association measures for co-analysis of miRNA and mRNA expression profiles.</p

Significant miRNA-mRNA pairs obtained using unmatched data.

<p>The labels on the X-axis correspond to biological conditions and the labels on the Y-axis correspond to miRNA-mRNA pairs. Blue indicates that the miRNA-mRNA pair was statistically significant in the relevant condition.</p

HAL-HAS Software

HAL-HAS is a program package, comprising HAL-BU, HAL-TD, and HAS, that was written to allow users to infer an optimal model of evolution for a given data set, a given tree

Hetero Software

Hetero was written to facilitate the development and testing of phylogenetic methods

Supplementary_material.tar.bz2

This supplementary material contains three files: 1 - a README file, with further details about the other two files. 2 - Rokas_2.phy (an alignment of a data set used to illustrate the methods). 3 - Yeast_tree.nwk (the corresponding tree used to illustrate the methods)

Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data

<div><p>Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.</p></div