Jeremy Gollub

GO: :TermFinder---open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

SUMMARY GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology (GO) information and evaluating and visualizing the collective annotation of a list of genes to GO terms. It can be used to draw conclusions from microarray and other biological data, calculating the statistical significance of each annotation. GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. AVAILABILITY The full source code and documentation for GO::TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/.

The Stanford Microarray Database: data access and quality assessment tools

The Stanford Microarray Database (SMD; http://genome-www.stanford.edu/microarray/) serves as a microarray research database for Stanford investigators and their collaborators. In addition, SMD functions as a resource for the entire scientific community, by making freely available all of its source code and providing full public access to data published by SMD users, along with many tools to explore and analyze those data. SMD currently provides public access to data from 3500 microarrays, including data from 85 publications, and this total is increasing rapidly. In this article, we describe some of SMDs newer tools for accessing public data, assessing data quality and for data analysis.

Decoding global gene expression programs in liver cancer by noninvasive imaging

Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer. Combinations of twenty-eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by noninvasive imaging, thereby enabling noninvasive, serial and frequent molecular profiling for personalized medicine.

The Stanford Microarray Database accommodates additional microarray platforms and data formats

The Stanford Microarray Database (SMD) (http://smd.stanford.edu) is a research tool for hundreds of Stanford researchers and their collaborators. In addition, SMD functions as a resource for the entire biological research community by providing unrestricted access to microarray data published by SMD users and by disseminating its source code. In addition to storing GenePix (Axon Instruments) and ScanAlyze output from spotted microarrays, SMD has recently added the ability to store, retrieve, display and analyze the complete raw data produced by several additional microarray platforms and image analysis software packages, so that we can also now accept data from Affymetrix GeneChips (MAS5/GCOS or dChip), Agilent Catalog or Custom arrays (using Agilents Feature Extraction software) or data created by SpotReader (Niles Scientific). We have implemented software that allows us to accept MAGE-ML documents from array manufacturers and to submit MIAME-compliant data in MAGE-ML format directly to ArrayExpress and GEO, greatly increasing the ease with which data from SMD can be published adhering to accepted standards and also increasing the accessibility of published microarray data to the general public. We have introduced a new tool to facilitate data sharing among our users, so that datasets can be shared during, before or after the completion of data analysis. The latest version of the source code for the complete database package was released in November 2004 (http://smd.stanford.edu/download/), allowing researchers around the world to deploy their own installations of SMD.

The Stanford Microarray Database: implementation of new analysis tools and open source release of software

The Stanford Microarray Database (SMD; ) is a research tool and archive that allows hundreds of researchers worldwide to store, annotate, analyze and share data generated by microarray technology. SMD supports most major microarray platforms, and is MIAME-supportive and can export or import MAGE-ML. The primary mission of SMD is to be a research tool that supports researchers from the point of data generation to data publication and dissemination, but it also provides unrestricted access to analysis tools and public data from 300 publications. In addition to supporting ongoing research, SMD makes its source code fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD. In this article, we describe several data analysis tools implemented in SMD and we discuss features of our software release.

Microarray data quality analysis: Lessons from the AFGC project

Genome-wide expression profiling with DNA microarrays has and will provide a great deal of data to the plant scientific community. However, reliability concerns have required the development data quality tests for common systematic biases. Fortunately, most large-scale systematic biases are detectable and some are correctable by normalization. Technical replication experiments and statistical surveys indicate that these biases vary widely in severity and appearance. As a result, no single normalization or correction method currently available is able to address all the issues. However, careful sequence selection, array design, experimental design and experimental annotation can substantially improve the quality and biological of microarray data. In this review, we discuss these issues with reference to examples from the Arabidopsis Functional Genomics Consortium (AFGC) microarray project.

Clustering microarray data.

Even a simple, small-scale, microarray experiment generates thousands to millions of data points. Clearly, spreadsheets or plotting programs do not suffice for analysis of such large volumes of data, and comprehensive analysis requires systematic methods for selection and organization of data. This chapter focuses on the concepts and algorithms of hierarchical clustering and the most commonly employed methods of partitioning or organizing microarray data, and freely available software that implements these algorithms.

The Stanford Microarray Database: a user's guide.

The Stanford Microarray Database (SMD) is a DNA microarray research database that provides a large amount of data for public use. This chapter describes the use of the primary tools for searching, browsing, retrieving, and analyzing data available for SMD. With this introduction, researchers and students will be able to examine and analyze a large body of gene expression and other experiments. Additional tools for depositing, annotating, sharing, and analyzing data, available only to registered users, are also described. SMD is available for installation as a local database.

Iterative Linear Regresssion by Sector

Two-color DNA microarray data has proven valuable in high-throughput expression profiling. However microarray expression ratios (logbase2ratios) are subject to measurement error from multiple causes. Transcript abundance is expected to be a linear function of signal intensity (y = x) where the typical gene is non-responsive. Once linearity is confirmed, applying the model by fitting log-scale data with simple linear regression reduces the standard deviation of the logbase2ratios, after which fewer genes are selected by filtering methods. Comparing the residuals of regression to leverage measures can identify the best candidate genes. Spatial bias in logbase2ratio, defined by printing pin and detected by ANOVA, can be another source of measurement error. Independently applying the linear normalization method to the data from each pin can easily eliminate this error. Less easily addressed is the problem of cross-homology which is expected to correlate to cross-hybridization. Pair-wise comparison of genes demonstrate that genes with similar sequences are measured as having similar expression. While this bias cannot be easily eliminated, the effect of this probable cross-hybridization can be minimised in clustering by weighting methods introduced here.