Package versions can also be found online: https://github.com/5c077/ExpressionDB/tree/master/data.

This example includes two tissues with three replicates apiece downloaded from GTEx. Complete.csv file here: https://github.com/5c077/ExpressionDB/tree/master/data.

A tool for mapping transcriptome data and for creating a database with an overview of the entire pathway, a web-based resource consisting of a web-application for the visualization of complex omics data onto KEGG pathways to overview all entities in the context of cellular pathways, and databases created with the software to visualize a series of microarray data. The web-application accepts transcriptome, proteome, metabolome, or the combination of these data as input, and because of this scalability it is advantageous for the visualization of cell simulation results. Several databases of transcriptome data obtained at Mori Laboratory, Nara Institute of Science and Technology, Japan, are also presented.

The microarray analysis market encompasses a range of products and services, including: Consumables: Reagents, buffers, probes, and microarray slides Software: Tools for microarray data acquisition, analysis, and visualization Services: Microarray design, sample preparation, and data analysis outsourcing Recent developments include: August 2019 Lionheart Technologies LLC ("BioTek"), a renowned leader in the production, design, and sale of cutting-edge life science instruments, was purchased by Agilent Technologies, Inc. Cell imaging systems, microplate readers, washers, dispensers, automated incubators, and stackers are among its wide range of products., September 2018 To offer complete single nucleotide polymorphism (SNP) assay development, Novacyt Group cooperated with Applied Microarrays, Inc. on the production of point-of-care (POC) and high throughput DNA and protein arrays as well as customized microarrays.. Key drivers for this market are: GROWING APPLICATION AREAS OF MICROARRAYS, INCREASING INCIDENCE OF CANCER; FUNDING FOR GENOMIC AND PROTEOMIC RESEARCH. Potential restraints include: PRESENCE OF SUBSTITUTES AND LACK OF SKILLED PROFESSIONALS. Notable trends are: Increasing applications of microarray analysis are driving market growth..

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Many Laboratories chose to design and print their own microarrays. At present, the choice of the genes to include on a certain microarray is a very laborious process requiring a high level of expertise. Onto-Design database is able to assist the designers of custom microarrays by providing the means to select genes based on their experiment. Design custom microarrays based on GO terms of interest. User account required. Platform: Online tool

This example comprises human annotations downloaded from Entrez Gene. Complete.csv files in appropriate format for many common organisms studied can be downloaded here:https://github.com/5c077/ExpressionDB/tree/master/data.

'The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. '

Bio Resource for array genes is a free online resource for easy access to collective and integrated information from various public biological resources for human, mouse, rat, fly and c. elegans genes. The resource includes information about the genes that are represented in Unigene clusters. This resource provides interactive tools to selectively view, analyze and interpret gene expression patterns against the background of gene and protein functional information. Different query options are provided to mine the biological relationships represented in the underlying database. Search button will take you to the list of query tools available. This Bio resource is a platform designed as an online resource to assist researchers in analyzing results of microarray experiments and developing a biological interpretation of the results. This site is mainly to interpret the unique gene expression patterns found as biological changes that can lead to new diagnostic procedures and drug targets. This interactive site allows users to selectively view a variety of information about gene functions that is stored in an underlying database. Although there are other online resources that provide a comprehensive annotation and summary of genes, this resource differs from these by further enabling researchers to mine biological relationships amongst the genes captured in the database using new query tools. Thus providing a unique way of interpreting the microarray data results based on the knowledge provided for the cellular roles of genes and proteins. A total of six different query tools are provided and each offer different search features, analysis options and different forms of display and visualization of data. The data is collected in relational database from public resources: Unigene, Locus link, OMIM, NCBI dbEST, protein domains from NCBI CDD, Gene Ontology, Pathways (Kegg, Genmapp and Biocarta) and BIND (Protein interactions). Data is dynamically collected and compiled twice a week from public databases. Search options offer capability to organize and cluster genes based on their Interactions in biological pathways, their association with Gene Ontology terms, Tissue/organ specific expression or any other user-chosen functional grouping of genes. A color coding scheme is used to highlight differential gene expression patterns against a background of gene functional information. Concept hierarchies (Anatomy and Diseases) of MESH (Medical Subject Heading) terms are used to organize and display the data related to Tissue specific expression and Diseases. Sponsors: BioRag database is maintained by the Bioinformatics group at Arizona Cancer Center. The material presented here is compiled from different public databases. BioRag is hosted by the Biotechnology Computing Facility of the University of Arizona. 2002,2003 University of Arizona.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 2nd,2023. TMAD stores raw and processed data from Tissue Microarray experiments along with their corresponding stained tissue images. In addition, TMAD provides methods for data retrieval, grouping of data, analysis and visualization as well as export to standard formats. Researchers at the Stanford University School of Medicine and their collaborators worldwide have constructed many tissue microarrays for use in basic research.

CGH Microarray Software Market Outlook

The global CGH microarray software market size was valued at approximately USD 450 million in 2023 and is projected to reach USD 850 million by 2032, growing at a CAGR of 7.2% over the forecast period. This growth is driven by the increasing demand for advanced genomic research tools, rising incidences of genetic disorders, and advancements in personalized medicine. The market's upward trajectory can be attributed to the integration of innovative technologies, which enhance the accuracy and efficiency of genomic data analysis.

One of the primary growth factors propelling the CGH microarray software market is the heightened focus on personalized medicine. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, and CGH microarray technology plays a crucial role in this field by enabling detailed genetic analysis. The ability to identify specific genetic mutations and variations allows for more precise diagnosis and treatment plans. As healthcare providers and researchers increasingly adopt personalized medicine approaches, the demand for sophisticated CGH microarray software is expected to rise significantly.

Another significant driver of market growth is the increase in research activities related to genetic disorders and cancer. Genetic research has gained substantial momentum over the past decade, with scientists striving to understand the genetic basis of various diseases. CGH microarray software facilitates the analysis of complex genetic data, enabling researchers to identify genetic abnormalities and variations with high accuracy. This, in turn, accelerates the development of targeted therapies and diagnostic tools. The growing prevalence of genetic disorders and cancers, coupled with the need for advanced research tools, is expected to fuel the demand for CGH microarray software.

Technological advancements in data analysis and management software are also contributing to market growth. The development of user-friendly and highly efficient software solutions has revolutionized the way genetic data is analyzed and interpreted. Modern CGH microarray software offers advanced features such as automated data analysis, real-time visualization, and integration with other genomic databases. These features not only enhance the accuracy of data interpretation but also streamline the workflow for researchers and clinicians. As technology continues to evolve, the capabilities of CGH microarray software are expected to expand, further driving market growth.

The Microarray Biochip Analyzer is an innovative tool that has significantly enhanced the field of genomic research. This advanced technology allows for the simultaneous analysis of thousands of genetic sequences, providing researchers with comprehensive insights into genetic variations and mutations. By utilizing microarray biochip analyzers, scientists can efficiently identify genetic markers associated with various diseases, facilitating the development of targeted therapies and personalized medicine approaches. The integration of this technology into research workflows has streamlined the process of genetic analysis, making it more accessible and efficient for researchers across the globe. As the demand for precise genetic data continues to grow, the role of microarray biochip analyzers in advancing genomic research becomes increasingly critical.

Regionally, the North American market is poised to dominate the CGH microarray software market, owing to the presence of leading biotechnology firms, advanced research infrastructure, and substantial investments in genomic research. Europe is also anticipated to witness significant growth, driven by increasing government funding for genetic research and a strong focus on precision medicine. The Asia Pacific region is expected to emerge as a lucrative market, fueled by rising healthcare expenditure, growing awareness about genetic disorders, and expanding research initiatives. Latin America and the Middle East & Africa are likely to experience moderate growth, supported by improving healthcare infrastructure and increasing adoption of advanced genomic tools.

Product Type Analysis

The CGH microarray software market is segmented based on product type into data analysis software, data management software, visualization software, and others. Each of these product types plays a pivotal role in the comprehensive analysis and interpretation of genetic data. Da

'Java Treeview - An Open Source, Extensible Viewer for Microarray Data in the PCL or CDT format '

NA: not available.Performance metrics of classifiers on the lung cancer test set (subtype and stage classification).

Data analysis server / software designed to test statistical significance of gene microarray data, visualize the results, and provide links to clone information and gene index. Several public datasets are also available.

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Microarray Biochips Market Size 2024-2028

The microarray biochips market size is forecast to increase by USD 17.28 billion, at a CAGR of 22.2% between 2023 and 2028.

The market is characterized by a growing number of collaborations among key players, which is expanding market presence and driving innovation. This strategic approach is essential in the capital-intensive market, where significant investments are required for research and development. A notable trend in the market is the emergence of Label-One-Component (LOC) technology, offering advantages such as improved sensitivity and specificity. However, the high cost of microarray biochips remains a significant challenge for market growth. Companies seeking to capitalize on opportunities must navigate this obstacle by focusing on cost reduction through economies of scale and process optimization. Additionally, collaborations and partnerships can help share research and development costs and accelerate time-to-market for innovative products. The strategic landscape of the market is dynamic, with ongoing advancements in technology and a growing demand for personalized medicine, creating opportunities for companies to differentiate themselves and gain a competitive edge.

What will be the Size of the Microarray Biochips Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in technology and expanding applications across various sectors. Protein microarray technology, a crucial component, enables high-throughput analysis of protein-protein interactions and antibody discovery. Reproducibility metrics and spot morphology analysis ensure consistency and accuracy in data generation. Label incorporation methods, such as biotinylated target cDNA and reverse transcription PCR, facilitate efficient probe attachment. Gene ontology enrichment and pathway analysis tools provide insights into biological functions and molecular interactions. Data mining algorithms, including clustering algorithms and fold change calculations, facilitate pattern recognition and discovery. Microarray data normalization techniques, such as CDNA microarray platforms and genomic DNA extraction, ensure data consistency. Microarray experimental design, hybridization kinetics, and high-throughput screening are essential for optimizing data generation and analysis. Single nucleotide polymorphism (SNP) detection and comparative genomic hybridization offer valuable insights into genetic variations. Data quality assessment, signal-to-noise ratios, and background correction methods ensure data accuracy and reliability. In situ hybridization and fluorescence detection methods facilitate visualization and analysis of gene expression at the cellular level. Differential gene expression analysis provides insights into disease mechanisms and therapeutic targets. Microarray scanner systems and image analysis software facilitate efficient and accurate data analysis. DNA microarray technology continues to evolve, offering exciting possibilities for research and diagnostic applications.

How is this Microarray Biochips Industry segmented?

The microarray biochips industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ApplicationDrug discovery and developmentDiagnostics and treatmentsResearch and consumablesForensic medicinesOthersGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By Application Insights

The drug discovery and development segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth due to its increasing application in drug discovery, driven by the rising preference for personalized medicines. With the global population aging, the demand for better healthcare solutions is escalating, leading manufacturers to continually innovate and improve microarray technology. In genomics and proteomics, microarray biochips are increasingly utilized, further fueling market growth. Advancements in protein microarray technology ensure greater reproducibility and accuracy, while spot morphology analysis and label incorporation enhance data reliability. Gene ontology enrichment and pathway analysis tools enable deeper insights into biological processes, and clustering algorithms facilitate the identification of complex relationships between genes. Genomic DNA extraction and microarray data normalization are crucial steps in ensuring data quality, while high-throughput screening and single nucleotide polymorphism analysis accelerate research. Image analysis software, biotinylated target cDNA, rever

Alternative splicing of pre-mRNA generates protein diversity and has been linked to cancer progression and drug response. Exon microarray technology enables genome-wide quantication of expression levels for the majority of exons and facilitates the discovery of alternative splicing events. Analysis of exon array data is more challenging than gene expression data and there is a need for reliable quantication of exons and alternative spliced variants. We introduce a novel, computationally efficient methodology, MEAP, for exon array data preprocessing, analysis and visualization. We compared MEAP with other preprocessing methods, and validation of the results show that MEAP produces reliable quantication of exons and alternative spliced variants. Analysis of data from head and neck squamous cell carcinoma (HNSCC) cell lines revealed several variants associated with 11q13 amplication, which is a predictive marker of metastasis and decreased survival in HNSCC patients. Together these results demonstrate the utility of MEAP in suggesting novel experimentally testable predictions. Thus, in addition to novel methodology to process large-scale exon array data sets, our results provide several HNSCC candidate genes for further studies. We analyzed 15 samples using the Affymetrix Human Exon 1.0 ST platform, of which 7 samples have 11q13 amplification. Array data was preprocessed by using Multiple Exon Array Processing (MEAP).

THIS RESOURCE IS NO LONGER IN SERVICE; REPLACED BY NEPHROSEQ; A growing database of publicly available renal gene expression profiles, a sophisticated analysis engine, and a powerful web application designed for data mining and visualization of gene expression. It provides unique access to datasets from the Personalized Molecular Nephrology Research Laboratory incorporating clinical data which is often difficult to collect from public sources and mouse data.

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independently of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. We demonstrated the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer data sets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms (Draghici et.al, Genomics, 81(2), 2003). Other results obtained with Onto-Express can be found in Khatri et.al., Genomics. 79(2), 2002. Custom level of abstraction of the Gene Ontology. User account required. Platform: Online tool

A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.

Biological Data Visualization Market Outlook

The global biological data visualization market size was valued at approximately USD 800 million in 2023 and is expected to reach USD 2.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12%. The rising volume of biological data generated through various research activities and the increasing need for advanced analytical tools are key factors driving this market's growth. The integration of artificial intelligence and machine learning in data visualization tools, combined with the growing application of biological data visualization in personalized medicine, are also significant growth drivers.

One of the primary growth factors of the biological data visualization market is the exponential increase in biological data generation due to advancements in high-throughput technologies such as next-generation sequencing (NGS), mass spectrometry, and microarray technology. These technologies produce vast amounts of data that require sophisticated visualization tools for proper analysis and interpretation. Without effective visualization, the potential insights and discoveries within this data may remain untapped, underscoring the market's critical role in modern biological research.

Additionally, the increasing prevalence of complex diseases and the subsequent demand for personalized medicine are fueling the demand for advanced data visualization tools. Personalized medicine relies heavily on the analysis of genetic, proteomic, and other biological data to tailor treatments to individual patients. Effective visualization tools facilitate the interpretation of this complex data, enabling healthcare providers to make informed clinical decisions. This trend is expected to drive substantial growth in the biological data visualization market over the forecast period.

Moreover, there is a growing adoption of cloud-based visualization solutions. Cloud deployment offers significant advantages, including scalability, cost-effectiveness, and accessibility from various locations. This is particularly beneficial for academic and research institutions and smaller biotech companies with limited resources. The integration of cloud computing with advanced visualization tools is expected to further propel market growth, as it allows for more efficient handling and analysis of large datasets.

From a regional perspective, North America currently holds the largest market share, driven by significant investments in research and development, advanced healthcare infrastructure, and high adoption rates of advanced technologies. Europe follows closely, with substantial growth attributed to government support for research initiatives and a strong presence of pharmaceutical and biotech companies. The Asia Pacific region is anticipated to witness the highest CAGR, owing to increasing investments in biotech research, growing healthcare infrastructure, and expanding adoption of advanced technologies in countries like China and India.

In the realm of Life Sciences Analytics, the role of data visualization is becoming increasingly pivotal. Life Sciences Analytics involves the use of data-driven insights to enhance research and development, clinical trials, and patient care. By leveraging advanced visualization tools, researchers and healthcare professionals can gain a deeper understanding of complex biological data, leading to more informed decisions and innovative solutions. The integration of Life Sciences Analytics with data visualization not only facilitates the interpretation of vast datasets but also accelerates the discovery of new patterns and correlations, ultimately advancing the field of personalized medicine.

Component Analysis

The biological data visualization market by component is segmented into software and services. Software solutions constitute the bulk of the market, providing tools that are essential for processing and visually representing complex biological data. These software tools range from basic data plotting programs to advanced systems incorporating machine learning algorithms for predictive modeling. The demand for these tools is driven by their ability to handle large datasets, provide user-friendly interfaces, and offer real-time data visualization capabilities, which are crucial for both research and clinical applications.

In contrast, the services segment, although smaller, plays a crucial role in the market. Services include co

An experiment in web-database access to large multi-dimensional data sets using a standardized experimental platform to determine if the larger scientific community can be given simple, intuitive, and user-friendly web-based access to large microarray data sets. All data in PEPR is also available via NCBI GEO. The structure and goals of PEPR differ from other mRNA expression profiling databases in a number of important ways. * The experimental platform in PEPR is standardized, and is an Affymetrix - only database. All microarrays available in the PEPR web database should ascribe to quality control and standard operating procedures. A recent publication has described the QC/SOP criteria utilized in PEPR profiles ( The Tumor Analysis Best Practices Working Group 2004 ). * PEPR permits gene-based queries of large Affymetrix array data sets without any specialized software. For example, a number of large time series projects are available within PEPR, containing 40-60 microarrays, yet these can be simply queried via a dynamic web interface with no prior knowledge of microarray data analysis. * Projects in PEPR originate from scientists world-wide, but all data has been generated by the Research Center for Genetic Medicine, Children''''s National Medical Center, Washington DC. Future developments of PEPR will allow remote entry of Affymetrix data ascribing to the same QC/SOP protocols. They have previously described an initial implementation of PEPR, and a dynamic web-queried time series graphical interface ( Chen et al. 2004 ). A publication showing the utility of PEPR for pharmacodynamic data has recently been published ( Almon et al. 2003 ).