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Software Tools for High-Throughput Analysis and Archiving Tissue Microarrays

a comprehensive system for high-throughput analysis and storage of TMA immunostaining data, using a combination of commercially available systems and novel software application. Staining results are recorded directly into an Excel worksheet and are reformatted by a novel program (TMA-Deconvoluter) into a format suitable for hierarchical clustering analysis or other statistical analysis.cations.

Sunday,Jul 12,2009 Source: Stanford University

Quantitative Parasitology 2.0

provides statistically correct ways to describe parasitic infection of a sample of hosts, and to compare parasitic infections between (among) different samples of hosts. It is free for distribution and use in education and science

Sunday,Jul 12,2009 Source: Reiczigel, J. & Rózsa, L

QAlign

the Java-based free program allows users to align more than a dozen sequences and dynamically view, manipulate, and refine those alignments. It can also automatically and dynamically creates phylogenetic trees from the alignments.

Sunday,Jul 12,2009 Source: RIDOM bioinformatics

TreeView

Graphically browse results of clustering and other analyses from Cluster. Supports tree-based and image based browsing of hierarchical trees. Multiple output formats for generation of images for publications

Sunday,Jul 12,2009 Source: Eisen lab, Lawrence Berkeley N

Maple Tree

Maple Tree is a java-based, open source, cross-platform visualization tool to graphically browse the results of clustering analyses from our Cluster and Fuzzy K clustering software, and many other clustering and analysis programs.

Sunday,Jul 12,2009 Source: Eisen lab, Lawrence Berkeley N

GMEP

Compute genome-mean expression profiles from expression and sequence data.

Sunday,Jul 12,2009 Source: Eisen lab, Lawrence Berkeley N

Cluster

Perform a variety of types of cluster analysis and other types of processing on large microarray datasets. Currently includes hierarchical clustering, self-organizing maps (SOMs), k-means clustering, principal component analysis.

Sunday,Jul 12,2009 Source: Eisen lab, Lawrence Berkeley N

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