SEA: a novel computational and GUI software pipeline for detecting activated biological sub-pathways

With the ever increasing amount of high-throughput molecular profile data, biologists need versatile tools to enable them to quickly and succinctly analyze their data. Furthermore, pathway databases have grown increasingly robust with the KEGG database at the forefront. Previous tools have color-coded the genes on different pathways using differential expression analysis. Unfortunately, they do not adequately capture the relationships of the genes amongst one another. Structure Enrichment Analysis (SEA) thus seeks to take biological analysis to the next level. SEA accomplishes this goal by highlighting for users the sub-pathways of a biological pathways that best correspond to their molecular profile data in an easy to use GUI interface

Efficient Deadlock Avoidance for Streaming Computation with Filtering

In this report, we show that deadlock avoidance for streaming computations with filtering can be performed efficiently for a large class of DAG topologies. We first give efficient algorithms for dummy interval computation in series-parallel DAGs, then generalize our results to a larger graph family, the CS4DAGs, in which every undirected cycle has exactly one source and one sink. Our results show that, for a large set of application topologies that are both intuitively useful and formalizable, the streaming model with filtering can be implemented safely with reasonable compilation overhead

Teak: A Novel Computational And Gui Software Pipeline For Reconstructing Biological Networks, Detecting Activated Biological Subnetworks, And Querying Biological Networks.

As high-throughput gene expression data becomes cheaper and cheaper, researchers are faced with a deluge of data from which biological insights need to be extracted and mined since the rate of data accumulation far exceeds the rate of data analysis. There is a need for computational frameworks to bridge the gap and assist researchers in their tasks. The Topology Enrichment Analysis frameworK (TEAK) is an open source GUI and software pipeline that seeks to be one of many tools that fills in this gap and consists of three major modules. The first module, the Gene Set Cultural Algorithm, de novo infers biological networks from gene sets using the KEGG pathways as prior knowledge. The second and third modules query against the KEGG pathways using molecular profiling data and query graphs, respectively. In particular, the second module, also called TEAK, is a network partitioning module that partitions the KEGG pathways into both linear and nonlinear subpathways. In conjunction with molecular profiling data, the subpathways are ranked and displayed to the user within the TEAK GUI. Using a public microarray yeast data set, previously unreported fitness defects for dpl1 delta and lag1 delta mutants under conditions of nitrogen limitation were found using TEAK. Finally, the third module, the Query Structure Enrichment Analysis framework, is a network query module that allows researchers to query their biological hypotheses in the form of Directed Acyclic Graphs against the KEGG pathways