microarray software

Ocimum Biosolutions is a life sciences R&D enabling company with three focus areas, BioIT, Microarrays and Research services. The Microarray division of Ocimum has been recently acquired from MWG Biotech. These include Catalog “OciChip”, Custom “OciChip” and microarray services.

Ocimum biosolutions offers an                       microarray analysis software called Genowiz, An evaluation copy of the software is avaialble at http://www.coimumbio.com

Genowiz™ Genowiz™ is a powerful gene expression analysis program that has been designed to store, process and visualize gene expression data efficiently. It includes a suite of advanced analysis methods and allows researchers to select analysis methods appropriate for their dataset. Genowiz™ allows researchers to organize experimental information (MIAME), import data files quickly and easily, work with multiple experiments at the same time, import gene annotation files, pre-process and normalize data, perform cluster analysis, classify and view gene information, perform functional classification and track down intricate correlations in data by performing pathway analysis. All analysis done is tracked, saved into a database and can be retrieved at any point of time.   What’s New • Import Affymetrix Raw Data • Merge Clusters • One Sample t-Test • SVM • View and Update NetAffx™ Annotations • Annotation Views • URL Editor • Search for Gene Ontology and Pathway terms • Regulatory, Signal Transduction and Disease Pathways Data and Gene List Import Genowiz™ supports a wide range of data formats pertaining to cDNA and Affymetrix data. Users can directly import .CEL and .CDF files into Genowiz™. Users also have an option to upload data in customized formats. Customized uploader allows users to add and save new data formats. One-Click Uploader can then identify these formats. Gene List files for annotating genes can also be imported. MIAME Minimum Information About a Microarray Experiment (MIAME) facilitates adoption of standards for microarray experiment annotation and data representation. Genowiz™ focuses on establishing standard microarray experimental data repositories and information sharing within the scientific community. Researchers can also exchange MIAME data by using MAGE ML document exchange format. Data Transformation, Normalization and Filtration In any type of expression analysis, pre-processing of data to reduce undesirable variation among datasets and to bring data to a common platform is a vital step. Genowiz™ provides users with a wide range of data transformation, normalization and filtration tools. These include: • Data transformation options such as imputation of missing values, log transformations, mean/median, Z-transformation, subtract control, divide by control, scaling etc. • Normalization techniques such as normalization for dye swap replicates, cDNA raw data normalization options (cDNA Loess and Print tip Loess) and quantile normalization. Separate normalization techniques are provided for cDNA and Affymetrix arrays. Normalization can be done using all genes or control genes. • Filter data based on replicate genes, fold change, mean, standard deviation, calls and missing values. Replicate samples are handled using various parametric/non-parametric tests. Multiple testing correction can be applied to reduce false positives. Data Analysis and Visualization Genowiz™ comes equipped with several data analysis tools. Complete with excellent graphics, it is an excellent tool for interpretation of biologically meaningful results. Some of these tools include partition clustering, hierarchical clustering, SOM, PCA, gene shaving and discriminant PCA and SVM. Option for merging, clusters of interest has also been provided. • Partition Clustering (K-means, Forgy’s) This tool classifies genes or samples in user-defined groups using distance parameters. The obtained clusters can be re-clustered. Re-clustering utility helps scientists pick a set of genes of their interest. A 2D PCA view shows the distribution of genes in various clusters. • Hierarchical Clustering One of the most important tools for studying relations between genes, this tool creates a dendrogram based on the relative distance between genes. The different optional parameters help the user in correctly determining the relationship between two genes. Models of analysis include single linkage, complete linkage and average linkage clustering. Genes, samples, or both together can be clustered. • Self Organizing Maps A two-way classification of genes into clusters based on novel artificial neural networks is an integral feature of data clustering tools in Genowiz™. This gives a deeper insight into clusters, as neighboring clusters are very similar to each other. • Principal Component Analysis This tool involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. These provide an insight into existent variability in the data. • Gene Shaving This method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other methods of gene expression analysis in that genes may belong to more than one cluster. Classification Classification algorithms are used to classify samples, based on information from similar samples with known classes that are available in training data. In Genowiz™, Support Vector Machines (SVM) and Discriminant PCA are used to predict classes for unclassified samples. Biological Analysis Genowiz™ annotates genes and classifies them into functional categories (Gene Ontology). Option of importing annotation files is also provided. Integrated pathways module aids researchers in understanding metabolic pathways in relation to expression data. Pathway maps edited/created can be associated with author details too. Coupled with biological information and gene ontological classification, it forms an excellent tool in understanding biological systems. Search can be performed on the gene ontology and pathway tree to look for ontologies or pathways of interest. Utilities Several utility options are present to add value to the analysis performed: • Gene List Comparison: Subtle relations among datasets can be probed using this feature. • Pattern Simulation: An expression pattern can be defined and Genowiz™ lists out all genes with a similar expression pattern. This gene list can be saved and exported. • Gene Tracking: Important genes or genes of interest can be tagged and tracked throughout the analysis. View and Update NetAffx™ annotations Annotations for the uploaded data can be viewed by connecting to NetAffx™ database. Connecting to the NetAffx™ database and selecting a corresponding chip will retrieve annotations from that chip. Flexibility to update annotation information for existing chips and add annotation information for new chips is also present, thus enabling researchers to view updated annotations for chips. Technical Support Ocimum’s technical support staff is available 24 hours, five days a week, to answer your questions about Genowiz™ over phone, e-mail and web chat. All questions previously answered by the support staff are available on the website for visitors. Sytem Requirements:   Request FREE trial