RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

The size of the Bioinformatics Platforms Market market was valued at USD 16.36 Million in 2023 and is projected to reach USD 27.93 Million by 2032, with an expected CAGR of 7.94% during the forecast period. The Bioinformatics Platforms Market includes the software and tools required to understand biological data that contain genomic, proteomic, or metabolic data. These platforms include support for various applications like drug discovery, individualized medicine, and clinically related diagnostics through helps of data integration, statistical analysis and visualization. Some of the emerging trends that are driving the bioinformatics market are cloud-based bioinformatics solutions to support scalability and collaboration, advanced machine learning and artificial intelligence (AI) technologies to accurately analyze raised significance of multi-omics data integration for profound tumor bioinformatics analysis. Such factors pulling the market ahead include increasing volume of biological data in facets like research and clinical trials, evolving sequencing technologies, along with the increasing requirement for enhanced data management and analysis in genomics and proteomics. Further, the rising usage of bioinformatics for customized treatment and the growing number of research studies in genomics complement the market’s growth. Recent developments include: In June 2022, California's biotechnology research startup LatchBio launched an end-to-end bioinformatics platform for handling big biotech data to accelerate scientific discovery., In March 2022, ARUP launched Rio, a bioinformatics pipeline and analytics platform for better, faster next-generation sequencing test results.. Key drivers for this market are: Increasing Demand for Nucleic Acid and Protein Sequencing, Increasing Initiatives from Governments and Private Organizations; Accelerating Growth of Proteomics and Genomics; Increasing Research on Molecular Biology and Drug Discovery. Potential restraints include: Lack of Well-defined Standards and Common Data Formats for Integration of Data, Data Complexity Concerns and Lack of User-friendly Tools. Notable trends are: Sequence Analysis Platform Segment is Expected Hold a Significant Share Over the Forecast Period.

The global Biological Data Analysis Services market is experiencing robust growth, driven by the increasing volume of biological data generated from high-throughput technologies like next-generation sequencing and advanced imaging techniques. The market's expansion is further fueled by the rising demand for personalized medicine, the growing adoption of bioinformatics tools and cloud-based solutions, and increasing investments in research and development across various sectors including pharmaceutical, biotechnology, and academic research. Key application areas such as biomarker identification, biological modeling, and image analysis are witnessing significant traction, contributing substantially to the market's overall growth. The diverse range of services offered, encompassing statistical data analysis and programming, data visualization, and structural biology, caters to the varied needs of researchers and organizations. Segments like biomarker identification and biological modeling are anticipated to exhibit faster growth compared to others owing to their crucial role in drug discovery and development. North America and Europe currently dominate the market, owing to established research infrastructure and higher healthcare expenditure, but the Asia-Pacific region is projected to show rapid growth due to increasing investments in life sciences research and development, and the expanding biotechnology sector. Competitive landscape analysis reveals a mix of large multinational corporations and specialized service providers. While established players like Eurofins Scientific leverage their extensive network and resources, smaller specialized companies are focusing on niche areas such as specific bioinformatics solutions or particular biological data types, offering innovative and tailored services. This competition is driving innovation and improvement in the quality and accessibility of biological data analysis services. Restraints to market growth include the high cost of advanced analytical tools and the need for specialized expertise to handle complex datasets. However, ongoing technological advancements and the development of user-friendly software are mitigating these challenges. Over the forecast period (2025-2033), continued innovation, particularly in AI and machine learning driven analysis, is expected to further fuel market expansion, leading to improved efficiency and affordability of biological data analysis.

The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives. Earlier version presented at ASIS&T and ISSI Pre-Conference: Symposium on Informetrics and Scientometrics 2009

Presentation on teaching introductory bioinformatics with Jupyter notebook-based active learning at the 2019 Great Lakes Bioinformatics Conference

This is the companion web site for publication:Barrera L, Benner C, Tao YC, Winzeler E, Zhou Y. Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays. BMC Bioinformatics. 2004 Apr 20;5:42. doi: 10.1186/1471-2105-5-42. PMID: 15099405; PMCID: PMC411067.Download and unzip the file, open ProbeStatistics/index.html to browse the self-contained web site.

This is the companion web site for publication:König R, Chiang CY, Tu BP, Yan SF, DeJesus PD, Romero A, Bergauer T, Orth A, Krueger U, Zhou Y, Chanda SK. A probability-based approach for the analysis of large-scale RNAi screens. Nat Methods. 2007 Oct;4(10):847-9. doi: 10.1038/nmeth1089. Epub 2007 Sep 9. PMID: 17828270.Download and unzip the file, open RSA/index.html to browse the self-contained web site.

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

This dataset contains R-scripts to perform bioinformatic and statistical analysis on acorn associated fungal communities (Fort et al., Maternal effects and environmental filtering shape seed fungal communities in oak trees. Submitted). Bioinformatic script was applied to raw sequences after paired-end sequences were joined using PEAR v0.9.10 (Zhang et al., 2014). Raw sequences are available at https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA551388. Metadata and ASV tables are also provided allowing to use statistical analysis scripts without running the bioinformatic steps.

The Bioinformatics Services market has emerged as a critical component of modern biological research, applying computational tools and statistical methods to analyze complex biological data. This rapidly evolving field is instrumental in various sectors, including pharmaceuticals, biotechnology, agriculture, and env

This dataset contains R-scripts to perform bioinformatic and statistical analysis on leaf-associated fungal communities of wild plant populations (Ma et al. Leaf-associated fungal and viral communities of wild plant populations differ between cultivated and natural ecosystems. Submitted). Raw sequences are available at https://doi.org/10.15454/X23KJF. Metadata and ASV tables are also provided allowing to use statistical analysis scripts without running the bioinformatic steps.

Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development) and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA) and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 ?g/L) of ethynylestradiol (EE2) and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs), which were subjected to over-representation analysis methods (ORA). Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and lowest p-value of the statistical workflow output. The methodology can be generalized to other (non-model) species and various types of microarray platforms.

The size and share of this market is categorized based on Data Analysis Tools (Bioinformatics Software, Statistical Analysis Tools, Visualization Tools, Data Management Solutions, Cloud-based Analysis Tools) and Applications (Clinical Diagnostics, Drug Discovery, Personalized Medicine, Agrigenomics, Forensic Science) and End Users (Pharmaceutical Companies, Biotechnology Firms, Academic and Research Institutes, Hospitals and Clinics, Contract Research Organizations (CROs)) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

The Bioinformatics Software market has emerged as a pivotal component in the fields of genomics, proteomics, and molecular biology, driving significant advancements in research and development. As researchers and healthcare professionals increasingly turn to big data for insights into genetic information, bioinforma

The Single Cell Bioinformatics Software and Service market is rapidly evolving as a crucial segment within the field of genomics and bioinformatics, focusing on the analysis of individual cells to uncover insights that traditional bulk methods often miss. This market, which has gained significant traction over the p

We have developed a framework for analyzing image data from High Content Screening (HCS) experiments. The Kolomogorov-Smirnov Statistic is used to identify statistically significant image parameters for use in K-means clustering. Clusters that are underrepresented in drug-treated cell populations can be "enriched" via normalizing by the control clusters. This general methodology can be applied at different drug treatment conditions to identify "interesting" clusters. We demonstrate how the resulting clusters of morphologies aid in the understanding of the underlying biology of drug-treated cell populations PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

This data set provides Supplementary files referenced in the thesis titled "Visual-analytics-driven bioinformatics methods for the analysis of biomolecular data".

In particular, this data set consists of the following files (Details are also provided in an included README.txt file):

Description of files in this data set:

1. Supplementary File 4.1. Supplementary File 4.1 - URL and variants schema.pdf. Graphical Backus-Naur schema of the variant syntax recognized by Aquaria.
2. Supplementary File 4.2. Supplementary File 4.2 - Schema.json. Aquaria feature set schema. This schema can be utilized in conjunction with user-specified JSON files for validation in online tools such as https://www.jsonschemavalidator.net/ (see Section 4.5.5).
3. Supplementary File 6.1. Supplementary File 6.1 - Illumina and complete genome IDs.xlsx. NCBI SRA accession identifiers of 673 Illumina (short-read length) and 673 PacBio sequenced genomes (long-read length), corresponding to 673 isolates sequenced using two technologies.
4. Supplementary File 6.2. Supplementary File 6.2 - Distribution of IS in complete genomes.xlsx. ISs in complete genomes.
5. Supplementary File 6.3. Supplementary File 6.3 - QUAST analysis of assemblies.xlsx. Summary of SPAdes and SKESA assembly quality statistics, generated using QUAST.
6. Supplementary File 6.4. Supplementary File 6.4 - WiIS performance metrics.xlsx. WiIS performance metrics for each genome.
7. Supplementary File 6.5. Supplementary File 6.5 - Correlation of performance metrics and assembly statistics.xlsx. Correlation of WiIS performance metrics with SPAdes and SKESA assembly quality statistics.
8. Supplementary File 6.6. Supplementary File 6.6 - IS insertions found by all tools.xlsx. IS insertions found by all tools for each of the 673 short-read sequenced genome.
9. Supplementary File 6.7. Supplementary File 6.7 - IS insertions found by all tools (20 base pair distance threshold).xlsx. IS insertions found by all tools, with a buffer length of 20 base pairs, for each of the 673 short-read sequenced genome.
10. Supplementary File 6.8. Supplementary File 6.8 - WiIS SPAdes IS insertions found with respect.xlsx. Summary of IS insertions found by WiIS (SPAdes) with respect to Tohama I (including the counts of insertions identified by WiIS, but not in Tohama I).
11. Supplementary File 6.9. Wiis.zip. WiIS code.

The Bioinformatics Cloud Platform market has emerged as a pivotal sector within the life sciences and healthcare industries, facilitating advanced data analysis and storage capabilities for genomic research, drug development, and personalized medicine. These platforms enable researchers to harness vast amounts of bi

The Bioinformatics Data Integration market has emerged as a crucial component of the biotechnology and healthcare industries, facilitating the cohesive synthesis of diverse biological data sets. With the exponential growth of genomic data, proteomics, and metabolomics, organizations are increasingly reliant on bioin