NMPP: a user-customized NimbleGen microarray data processing pipeline

NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module. AVAILABILITY: http://plantgenomics.biology.yale.edu/nmpp     

GenePicker: replicate analysis of Affymetrix gene expression microarrays

SUMMARY: GenePicker allows efficient analysis of Affymetrix gene expression data performed in replicate, through definition of analysis schemes, data normalization, t-test/ANOVA, Change-Fold Change-analysis and yields lists of differentially expressed genes with high confidence. Comparison of noise and signal analysis schemes allows determining a signal-to-noise ratio in a given experiment. Change Call, Fold Change and Signal mean ratios are used in the analysis. While each parameter alone yields gene lists that contain up to 30% false positives, the combination of these parameters nearly eliminates the false positives as verified by northern blotting, quantitative PCR in numerous independent experiments as well as by the analysis of spike-in data. AVAILABILITY: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html. SUPPLEMENTARY INFORMATION: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html.     

DNA array analysis in a Microsoft Windows environment.

Microsoft Windows-based computers have evolved to the point that they provide sufficient computational and visualization power for robust analysis of DNA array data. In fact, smaller laboratories might prefer to carry out some or all of their analyses and visualization in a Windows environment, rather than alternative platforms such as UNIX. We have developed a series of manually executed macros written in Visual Basic for Microsoft Excel spreadsheets, that allows for rapid and comprehensive gene expression data analysis. The first macro assigns gene names to spots on the DNA array and normalizes individual hybridizations by expressing the signal intensity for each gene as a percentage of the sum of all gene intensities. The second macro streamlines statistical consideration of the confidence in individual gene measurements for sets of experimental replicates by calculating probability values with the Student's t test. The third macro introduces a threshold value, calculates expression ratios between experimental conditions, and calculates the standard deviation of the mean of the log ratio values. Selected columns of data are copied by a fourth macro to create a processed data set suitable for entry into a Microsoft Access database. An Access database structure is described that allows simple queries across multiple experiments and export of data into third-party data visualization software packages. These analysis tools can be used in their present form by others working with commercial E. coli membrane arrays, or they may be adapted for use with other systems. The Excel spreadsheets with embedded Visual Basic macros and detailed instructions for their use are available at http://www.ou.edu/microarray.     

MEGO: gene functional module expression based on gene ontology

Existing analysis tools to study the collective properties of gene functional modules cannot return highly homogeneous modules and do not provide quantitative measures of module activity level. By partitioning genes according to multiple gene functional categorization principles and summarizing gene expression values into module expression values, MEGO (module expression based on gene ontology), a standalone microarray data analysis program, is able to extract highly activated gene functional modules that are of much interest to microarray experimenters. With multiple functional categorization principles simultaneously introduced in MEGO, the partition of genes is more delicate, and the collective property of a group of genes is sharpened and easier to capture. The quantitative measures of module activity levels returned by MEGO give users a quick impression of the direction and degree of module regulation. MEGO efficiently determines the answers to frequently asked questions, such as which functional classes have been induced or repressed under a specific experiment and to which levels these functional classes have been affected. MEGO is available free of charge for academic use and may be downloaded from http://www.dxy.cn/mego/MEGOInstall.EXE. Supplementary information can be found on the authors' web page at http://www.dxy.cn/mego/ and at the BioTechniques' web site at http://www. BioTechniques.com/February2005/TuSupplementary.html.     

Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.     

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu     

ADGO: analysis of differentially expressed gene sets using composite GO annotation

MOTIVATION: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. RESULTS: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. AVAILABILITY: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. CONTACT: chu@kribb.re.kr SUPPLEMENTARY INFORMATION: http://array.kobic.re.kr/ADGO.     

BioGRID REST Service, BiogridPlugin2 and BioGRID WebGraph: new tools for access to interaction data at BioGRID

The Biological General Repository for Interaction Datasets (BioGRID) representational state transfer (REST) service allows full URL-based access to curated protein and genetic interaction data at the BioGRID database. Appending URL parameters allows filtering of data by various attributes including gene names and identifiers, PubMed ID and evidence type. We also describe two visualization tools that interface with the REST service, the BiogridPlugin2 for Cytoscape and the BioGRID WebGraph. Availability and implementation: BioGRID data and applications are completely free for commercial and non-commercial use. http://webservice.thebiogrid.org/resources/interactions (REST Service), http://wiki.thebiogrid.org/doku.php/biogridrest(REST Service parameter list and help), http://webservice.thebiogrid.org/resources/application.wadl(REST Service WADL), http://thebiogrid.org/download.php (BiogridPlugin2, v2.1 download), http://wiki.thebiogrid.org/doku.php/biogridplugin2 (BiogridPlugin2 help) and http://tyerslab.bio.ed.ac.uk/tools/BioGRID_webgraph.php(BioGRID WebGraph).     

Towards an integrated computational tool for spatial analysis in macroecology and biogeography

Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of sam 's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/ ).     

Detection and correction of probe-level artefacts on microarrays

ABSTRACT: BACKGROUND: A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to quality assessment, artefact detection is not widely used in standard gene expression analysis pipelines. Furthermore, although approaches have been proposed to detect diverse types of spatial noise on arrays, the correction of these artefacts is mostly left to either summarization methods or the corresponding arrays are completely discarded. RESULTS: We show that state-of-the-art robust summarization procedures are vulnerable to artefacts on arrays and cannot appropriately correct for these. To address this problem, we present a simple approach to detect artefacts with high recall and precision, which we further improve by taking into account the spatial layout of arrays. Finally, we propose two correction methods for these artefacts that either substitute values of defective probes using probeset information or filter corrupted probes. We show that our approach can identify and correct defective probe measurements appropriately and outperforms existing tools. CONCLUSIONS: While summarization is insufficient to correct for defective probes, this problem can be addressed in a straightforward way by the methods we present for identification and correction of defective probes. As these methods output CEL files with corrected probe values that serve as input to standard normalization and summarization procedures, they can be easily integrated into existing microarray analysis pipelines as an additional pre-processing step. An R package is freely available from http://www.bio.ifi.lmu.de/artefact-correction.     

DNA Data Bank of Japan (DDBJ) for genome scale research in life science

The DNA Data Bank of Japan (DDBJ, http://www.ddbj.nig.ac.jp) has made an effort to collect as much data as possible mainly from Japanese researchers. The increase rates of the data we collected, annotated and released to the public in the past year are 43% for the number of entries and 52% for the number of bases. The increase rates are accelerated even after the human genome was sequenced, because sequencing technology has been remarkably advanced and simplified, and research in life science has been shifted from the gene scale to the genome scale. In addition, we have developed the Genome Information Broker (GIB, http://gib.genes.nig.ac.jp) that now includes more than 50 complete microbial genome and Arabidopsis genome data. We have also developed a database of the human genome, the Human Genomics Studio (HGS, http://studio.nig.ac.jp). HGS provides one with a set of sequences being as continuous as possible in any one of the 24 chromosomes. Both GIB and HGS have been updated incorporating newly available data and retrieval tools.     

Procalcitonin and cellulitis: correlation of procalcitonin blood levels with measurements of severity and outcome in patients with limb cellulitis

Background: Procalcitonin levels may be raised in bacterial infections and have been used to guide antibiotic therapy. There is little data on procalcitonin and limb cellulitis.

Objectives: Within a clinical trial of antibiotic therapy, we examined the correlation between clinical observations, blood tests and local measurements of skin damage, with serum procalcitonin levels.

Methods: The data is from a subset of the patients recruited into a clinical trial of antibiotic therapy for cellulitis (clindamycin for cellulitis, NCT01876628) whose procalcitonin levels were correlated with clinical and laboratory measurements. We selected the variables strongly correlated with procalcitonin and evaluated the predictive value of the baseline procalcitonin on the primary trial outcome.

Results: 136 patients provided 307 procalcitonin levels which were correlated with 8 variables. The strongest correlations (correlation coefficient of >0.5) with procalcitonin were the affected skin area (0.537), C-reactive protein (0.574) and neutrophil:lymphocyte ratio (0.567). Receiver operator characteristic curves demonstrated poor sensitivity and specificity of procalcitonin in predicting primary outcome. Procalcitonin baseline levels were low but decreased as patients recovered.

Conclusions: Procalcitonin levels are generally low in limb cellulitis and cannot be used to confirm the diagnosis or the need for antibiotic therapy. Procalcitonin is a poor predictor of early improvement.     

CNVDetector: locating copy number variations using array CGH data

CNVDetector is a program for locating copy number variations (CNVs) in a single genome. CNVDetector has several merits: (i) it can deal with the array comparative genomic hybridization data even if the noise is not normally distributed; (ii) it has a linear time kernel; (iii) its parameters can be easily selected; (iv) it evaluates the statistical significance for each CNV calling. AVAILABILITY: CNVDetector (for Windows platform) can be downloaded from http:www.csie.ntu.edu.tw/~kmchao/tools/CNVDetector/. The manual of CNVDetector is also available.     

Missing value estimation for DNA microarray gene expression data: local least squares imputation

MOTIVATION: Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process. RESULTS: The proposed local least squares imputation method (LLSimpute) represents a target gene that has missing values as a linear combination of similar genes. The similar genes are chosen by k-nearest neighbors or k coherent genes that have large absolute values of Pearson correlation coefficients. Non-parametric missing values estimation method of LLSimpute are designed by introducing an automatic k-value estimator. In our experiments, the proposed LLSimpute method shows competitive results when compared with other imputation methods for missing value estimation on various datasets and percentages of missing values in the data. AVAILABILITY: The software is available at http://www.cs.umn.edu/~hskim/tools.html CONTACT: hpark@cs.umn.edu     

Mfuzz: a software package for soft clustering of microarray data

For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface. AVAILABILITY: The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license.     

NetAtlas: a Cytoscape plugin to examine signaling networks based on tissue gene expression

Graphical methods are useful for visualizing signaling networks derived from the synthesis of large bodies of literature information or large-scale experimental measurements. Software tools to filter and organize these networks allow the exploration of their inherent biological and structural properties. We have developed NetAtlas, an open-source, Java-based Cytoscape plugin for examining signaling networks in the context of tissue gene expression patterns. The tissue gene expression data available through NetAtlas consists of 79 human tissues, 61 mouse tissues, and 44 combined tissues from 3 rat strains. Users may also import their own tissue gene expression data. The NetAtlas plugin allows the creation of tissue-defined signaling networks by identifying which components are expressed in particular tissues, which components show tissue-specific expression, and which components within the network are coordinately expressed across tissues. The NetAtlas plugin is available at http://sourceforge.net/projects/netatlas/.     

BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.     

GEPAS: A web-based resource for microarray gene expression data analysis

We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).