MOTIVATION: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. SUMMARY: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations. AVAILABILITY: A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx.

MOTIVATION: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30,000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44,460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1). AVAILABILITY: The program is freely available and can be downloaded from http://www.amdcc.org/bioinformatics/bioinformatics.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: During the last decade, the use of microarrays to assess the transcriptome of many biological systems has generated an enormous amount of data. A common technique used to organize and analyze microarray data is to perform cluster analysis. While many clustering algorithms have been developed, they all suffer a significant decrease in computational performance as the size of the dataset being analyzed becomes very large. For example, clustering 10000 genes from an experiment containing 200 microarrays can be quite time consuming and challenging on a desktop PC. One solution to the scalability problem of clustering algorithms is to distribute or parallelize the algorithm across multiple computers. RESULTS: The software described in this paper is a high performance multithreaded application that implements a parallelized version of the K-means Clustering algorithm. Most parallel processing applications are not accessible to the general public and require specialized software libraries (e.g. MPI) and specialized hardware configurations. The parallel nature of the application comes from the use of a web service to perform the distance calculations and cluster assignments. Here we show our parallel implementation provides significant performance gains over a wide range of datasets using as little as seven nodes. The software was written in C# and was designed in a modular fashion to provide both deployment flexibility as well as flexibility in the user interface. CONCLUSION: ParaKMeans was designed to provide the general scientific community with an easy and manageable client-server application that can be installed on a wide variety of Windows operating systems.

MOTIVATION: The National Cancer Institute's Center for Bioinformatics (NCICB) has developed a Java based data management and information system called caCORE. One component of this software suite is the object oriented API (caBIO) used to access the rich biological datasets collected at the NCI. This API can access the data using native Java classes, SOAP requests or HTTP calls. Non-Java based clients wanting to use this API have to use the SOAP or HTTP interfaces with the data being returned from the NCI servers as an XML data stream. Although the XML can be read and manipulated using DOM or SAX parsers, one loses the convenience and usability of an object oriented programming paradigm. caBIONet is a set of .NET wrapper classes (managers, genes, chromosomes, sequences, etc.) capable of serializing the XML data stream into local .NET objects. The software is able to search NCICB databases and provide local objects representing the data that can be manipulated and used by other .NET programs. The software was written in C# and compiled as a .NET DLL.

MOTIVATION: The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. RESULTS: We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. AVAILABILITY: This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.