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           Affymetrix compatable smicroarray software            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

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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.

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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.

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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.

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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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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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.

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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.

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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.

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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.

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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.

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Sytem Requirements:

 

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microarray from ocimum biosolutions

Ocimum biosolutions offers microarays and bioinformatis sofftware to researchers across the world, The microaray division of ocimum offers the MWG chips which were earl;ier manufactured by MWG biotech of Germany

for more detail visit http://www.ocimumbio.com

OciChip™ Design – Route to a perfect Array

OciChip™ design and production concept ensure the highest microarray quality.

First, sophisticated bioinformatics design chips of the highest specificity and sensitivity – an advantage customers of other chip manufacturers do not have.

Second, the calculated oligonucleotides are produced and purified with strict quality control procedures including MALDI-TOF MS. Huge production capacities, know-how and process automation guarantee high quality and fast service at affordable prices. Moreover, our microarray experts and their cooperation partners do functional validation of each of the catalog arrays. Of course, every individual batch of arrays is quality controlled extensively.

Oligos4Array

The backbone of the array probe design are sophisticated bioinformatics tools such as the Oligos4Array software and our proprietary non-redundant CodeSeq databases. These software tools are part of our unique computational platform called BioGIST® that allows establishment of completely automated workflows; e.g., for oligo probe design and microarray production. Each individual oligo is designed using proprietary design algorithms that ensure absolute gene specificity with one probe per gene.

Oligos4Array – several steps for designing the ideal oligo for every single gene:

1. Design of an oligo probe begins with defining physical parameters such as

  • Length
  • GC Content
  • Secondary structures
  • Overlap between selected oligos
  • Dimer formation

Extensive R&D efforts proved that 50mers meet the requirements for specificity and sensitivity the best. The default GC content setting is between 40 and 60%, but will be adjusted as required depending on the project. Obviously, the algorithms exclude oligos that show secondary structures, overlaps, and primer dimer formation. Thresholds can be defined individually.

2. Extensive alignments use our non-redundant database CodeSeq to sort out unspecific sequences

In order to find gene specific probes, Oligos4Array compares suggested 50mer sequences with those of all known coding regions of the species of interest (BLAST and Smith-Waterman analysis). For that purpose, a CodeSeq database containing all known coding regions of the respective species is generated (based on redundant public and proprietary databases). As submission of identical sequence information several times to the same public database is quite common today, these databases are redundant information sources. However, only databases that store each sequence once exclusively, i.e., non-redundant databases, can ensure automated high throughput design. The reason is that for efficient comparisons between oligo sequences and sequences stored in databases, the parameter “each oligo sequence is allowed to occur once only” is clear without ambiguity. Therefore, we establish and update regularly our proprietary CodeSeq databases for each organism of interest.

First, all sequences available in public and proprietary databases for a specific organism are clustered, whereby each cluster represents one unique gene. Secondly, a consensus sequence or contig generated from each cluster is entered into our CodeSeq database and thus forms the basis for gene specific and automated oligo probe design.

3. Application dependent selection of gene specific oligonucleotide probes (e.g. exon specific, strain specific or alternative splicing)

4. Recent publications on the ideal length of oligonucleotide probes, as well as experimental evidence from our own research and development indicate that 40-50 mer oligonucleotide probes show an optimal balance of sensitivity and specificity.

Oligo-Production

5. Our laboratory information and management system Biotracker™ controls the fully automated production of the designed oligos in superior quality. Our oligonucleotides are

  • Full length products purified from n-1 products and failure products
  • Free of salt and metal ions
  • Standard QC and identity checked by MALDI-TOF analysis

This constant high quality allows standardized microarray production from batch to batch, and from array to array

6. Complete order process automation and high capacity guarantee an error free and fast service.

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