These secondary data have been produced from the gene expression data generated from duodena of mice exposed to hexavalent chromium in drinking water and deposited in the GEO repository under accession number GSE87259. They include (i) inferred upstream regulators responsible for the observed changes in gene expression, (ii) genes significantly differentially expressed between duodena of exposed and control mice, and (iii) list of CFTR gene variants associated with cancers in COSMIC tumor repository.

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

Data Set Description

These data are collected from a total of 70 participants (47 adult; 23 pediatric), all of which had relapsed or primary resistant acute myeloid leukemia. The data, which here are separated into an adult and a pediatric dataset, were generated as part of a study by Stratmann et. al. (https://doi.org/10.1182/bloodadvances.2021004962). The Stratmann et. al. study is currently pre-published here: https://ashpublications.org/bloodadvances/article/doi/10.1182/bloodadvances.2021004962/477210/Transcriptomic-analysis-reveals-pro-inflammatory Please note that separate applications are necessary for the adult and pediatric dataset, respectively. When applying for access, please indicate which of the datasets that the application applies for. The adult dataset contains transcriptome sequencing (RNA-seq) data from 25 diagnosis (D), 45 relapse (R1/R2/R3) and five (5) primary resistant (PR) leukemic samples from 47 patients, as well as five (5) normal CD34+ bone marrow control samples. The pediatric dataset contains RNA-seq data from 18 diagnosis (D), 22 relapse (R1/R2), six (6) persistent relapse (R1/2-P) and one (1) primary resistant (PR) leukemic samples from 23 patients, as well as five (5) normal CD34+ bone marrow control samples. The leukemic samples originate from bone marrow or peripheral blood. The normal RNA samples originate from purified CD34+ bone marrow cells from five different healthy individuals. Further details regarding the samples are available in the Supplemental Information part of Stratmann et. al. (https://doi.org/10.1182/bloodadvances.2021004962). RNA-seq libraries and associated next-generation sequencing were carried out by the SNP&SEQ Technology platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Libraries were prepared using the TruSeq stranded total RNA library preparation kit with ribosomal depletion by RiboZero Gold (Illumina). Sequencing of adult samples was carried out on the Illumina HiSeq2500 platform, generating paired-end 125bp reads using v4 sequencing chemistry. Sequencing of pediatric samples was carried out on the Illumina NovaSeq6000 platform (S2 flowcell), generating paired-end 100bp reads using the v1 sequencing chemistry. The CD34+ bone marrow control samples were sequenced using both platforms (Illumina HiSeq2500 and NovaSeq6000). Further, all of these acute myeloid leukemia samples have also been characterized by whole genome sequencing or whole exome sequencing, with the datasets available under controlled access through doi.org/10.17044/scilifelab.12292778. Terms for accessThe adult and pediatric datasets are only to be used for research that is seeking to advance the understanding of the influence of genetic and transcriptomic factors on human acute myeloid leukemia etiology and biology. Use of the protected pediatric dataset is only for research projects that can merely be conducted using pediatric acute myeloid leukemia data, and for which the research objectives cannot be accomplished using data from adults. Applications intending various method development would thus not be considered as acceptable for use of the pediatric dataset. Further, the pediatric dataset may not be used for research investigating predisposition for acute myeloid leukemia based on germline variants.

For conditional access to the adult and/or pediatric dataset in this publication, please contact datacentre@scilifelab.se

Dataset for Harrill, J.A. et al., 'Signature Analysis of High-Throughput Transcriptomics Screening Data for Mechanistic Inference and Chemical Grouping' published in Toxicological Sciences, https://doi.org/10.1093/toxsci/kfae108 This dataset contains gene expression profiles and gene signature concentration-response modeling results for 1751 unique chemicals. The chemicals were tested in MCF7 cells using an exposure duration of six hours. The datasets also contains the results of molecular target enrichment and chemotype enrichment analyses performed downstream of the gene signature concentration-response modeling. Descriptions of each data file can be found in the supplementary material of the published article that is hosted by the journal. This dataset is associated with the following publication: Harrill, J., L. Everett, D. Haggard, L. Word, J. Bundy, B. Chambers, D. Harris, C. Willis, R. Thomas, I. Shah, and R. Judson. Signature Analysis of High-Throughput Transcriptomics Screening Data for Mechanistic Inference and Chemical Grouping. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 202(1): 103-122, (2024).

This dataset contains the transcriptomic data analysis results for groups WT 3nRR and tyr-mutated 3nRR, including differential expression and functional enrichment analyses.

To understand the molecular mechanisms affected by spaceflight it is essential to achieve high quality sample preservation on-orbit for downstream gene expression analysis. However sample preservation protocols must also be compatible with available equipment and crew time. NASA s Rodent Research (RR) missions have used various methods for euthanasia carcass preservation and tissue preservation. This study extends the sample preservation study performed by GeneLab in GLDS-49 which examined conditions used for the RR-1 mission to include conditions used for multiple RR missions and is designed to help determine factors which may confound data analysis. To determine whether these various factors affect changes in gene expression this ground-based study generated gene expression profiles measured by RNAseq from the quadriceps of 20-21 week-old female C57BL/6J mice. Multiple interacting factors were investigated: 1) To understand how euthanasia protocols affect gene expression when mouse carcasses are slow frozen mice were euthanized by either euthasol injection ketamine/xylazine injection or CO2 inhalation and carcasses slow frozen on dry-ice mimicking carcass preservation in the MELFI on the ISS. Carcasses were thawed and RNA extracted from quadriceps; 2) To understand how carcass preservation protocols affect gene expression mice were euthanized with euthasol and carcasses preserved by flash freezing in liquid nitrogen slow freezing on dry ice or immersion in RNAlater following three-way segmentation. Carcasses were thawed and RNA extracted from quadriceps; 3) To understand how tissue preservation protocols affect gene expression mice were euthanized with euthasol and quadriceps immediately dissected and preserved by flash freezing in liquid nitrogen slow freezing on dry ice or immersion in RNAlater.

The JAXA MHU-2 mission had two objectives: 1) To increase understanding of effects of spaceflight on the gut environment (microbiota and metabolites) and immune system using multi-omics based analysis; 2) To evaluate whether fructo-oligosaccharides added to the diet as prebiotics improve the gut environment and immune function during spaceflight. Twelve 16-18 week old male C57BL/6J mice were singly housed in the JAXA Habitat Cage Units (HCUs) on the ISS for 30 days. Six flight mice were housed in microgravity while six were exposed to simulated 1g by centrifugation. These two flight groups were further divided in half so that three mice in each group received standard JAXA chow while the other three were fed chow supplemented with fructooligosaccharides (FOS). Mice were returned live and euthanized and dissected <1 day after splashdown. Ground controls (n=6) were asynchronous and housed in HCUs. Vivarium controls (n=6) were asynchronous and housed in standard habitats. Three ground control and three vivarium animals received standard chow while the other three each ground control and vivarium animals received FOS-supplemented chow. Ground and vivarium samples were dissected by a separate dissection team than flight samples. Femoral skin was dissected 30 minutes after euthanasia and snap frozen in liquid nitrogen. Total RNA was extracted and sequenced at a target depth of 60 M clusters per sample (ribodepleted paired end 150). Study Factor Levels: 1)Spaceflight ug Std. Chow: 3; 2)Spaceflight ug FOS: 3; 3) Spaceflight Artificial 1g Std. Chow: 3; 4)Spaceflight Artificial 1g FOS: 3; 5)Ground 1g Std. Chow: 3; 6)Ground 1g FOS: 3; 7)Vivarium 1g Std. Chow: 3; 8)Vivarium 1g FOS: 3

This dataset contains Spatial Transcriptomics (ST) data matching with Matrix Assisted Laser Desorption/Ionization - Mass Spetrometry Imaging (MALDI-MSI). This data is complementary to data contained in the same project. FIles with the same identifiers in the two datasets originated from the very same tissue section and can be combined in a multimodal ST-MSI object. For more information about the dataset please see our manuscript posted on BioRxiv (doi: https://doi.org/10.1101/2023.01.26.525195). This dataset includes ST data from 19 tissue sections, including human post-mortem and mouse samples. The spatial transcriptomics data was generated using the Visium protocol (10x Genomics). The murine tissue sections come from three different mice unilaterally injected with 6-OHDA. 6-OHDA is a neurotoxin that when injected in the brain can selectively destroy dopaminergic neurons. We used this mouse model to show the applicability of the technology that we developed, named Spatial Multimodal Analysis (SMA). Using our technology on these mouse brain tissue sections we were able to detect both dopamine with MALDI-MSI and the corresponding gene expression with ST. This dataset includes also one human post-mortem striatum sample that was placed on one Visium slide across the four capture areas. This sample was analyzed with a different ST protocol named RRST (Mirzazadeh, R., Andrusivova, Z., Larsson, L. et al. Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples. Nat Commun 14, 509 (2023). https://doi.org/10.1038/s41467-023-36071-5), where probes capturing the whole transcriptome are first hybridized in the tissue section and then spatially detected. Each tissue section contained in the dataset has been given a unique identifier that is composed of the Visium array ID and capture area ID of the Visium slide that the tissue section was placed on. This unique identifier is included in the file names of all the files relative to the same tissue section, including the MALDI-MSI files published in the other dataset included in this project. In this dataset you will find the following files for each tissue section: - raw files: these are the read one fastq files (containing the pattern *R1*fastq.gz in the file name), read two fastq files (containing the pattern *R1*fastq.gz in the file name) and the raw microscope images (containing the pattern Spot.jpg in the file name). These are the only files needed to run the Space Ranger pipeline, which is freely available for any user (please see the 10x Genomics website for information on how to install and run Space Ranger); - processed data files: we provide processed data files of two types: a) Space Ranger outputs that were used to produce the figures in our publication; b) manual annotation tables in csv format produced using Loupe Browser 6 (csv tables with file names ending _RegionLoupe.csv, _filter.csv, _dopamine.csv, _lesion.csv, _region.csv patterns); c) json files that we used as input for Space Ranger in the cases where the automatic tissue detection included in the pipeline failed to recognize the tissue or the fiducials. Using these processed files the user can reproduce the figures of our publication without having to restart from the raw data files. The MALDI-MSI analyses preceding ST was performed with different matrices in different tissue section. We used 1) 9-aminoacridine (9-AA) for detection of metabolites in negative ionization mode, 2) 2,5-dihydroxybenzoic acid (DHB) for detection of metabolites in positive ionization mode, 3) 4-(anthracen-9-yl)-2-fluoro-1-ethylpyridin-1-ium iodide (FMP-10), which charge-tags molecules with phenolic hydroxyls and/or primary amines, including neurotransmitters. The information about which matrix was sprayed on the tissue sections and other information about the samples is included in the metadata table. We also used three types of control samples: - standard Visium: samples processed with standard Visium (i.e. no matrix spraying, no MALDI-MSI, protocol as recommended by 10x Gemomics with no exeptions) - internal controls (iCTRL): samples not sprayed with any matrix, neither processed with MALDI-MSI, but located on the same Visium slide were other samples were processed with MALDI-MSI - FMP-10-iCTRL: sample sprayed with FMP-10, and then processed as an iCTRL. This and other information is provided in the metadata table.

Here we include the marmoset brain and mouse gut STARmap data introduced in the corresponding manuscript, "Mitigating autocorrelation during spatially resolved transcriptomics data analysis". We also include the mouse brain STARmap PLUS data that was used to demonstrate cross-species spatial integration and was previously published in Shi, He, Zhou et al. 2022.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 6/12/25. ITTACA is a database created for Integrated Tumor Transcriptome Array and Clinical data Analysis. ITTACA centralizes public datasets containing both gene expression and clinical data and currently focuses on the types of cancer that are of particular interest to the Institut Curie: breast carcinoma, bladder carcinoma, and uveal melanoma. ITTACA is developed by the Institut Curie Bioinformatics group and the Molecular Oncology group of UMR144 CNRS/Institut Curie. A web interface allows users to carry out different class comparison analyses, including comparison of expression distribution profiles, tests for differential expression, patient survival analyses, and users can define their own patient groups according to clinical data or gene expression levels. The different functionalities implemented in ITTACA are: - To test if one or more gene, of your choice, is differentially expressed between two groups of samples exhibiting distinct phenotypes (Student and Wilcoxon tests). - The detection of genes differentially expressed (Significance Analysis of Microarrays) between two groups of samples. - The creation of histograms which represent the expression level according to a clinical parameter for each sample. - The computation of Kaplan Meier survival curves for each group. ITTACA has been developed to be a useful tool for comparing personal results to the existing results in the field of transcriptome studies with microarrays.

Our study aims to comprehensively understand effects induced by the space environment on mammals. To achieve this aim we analyze the male mice housed under environments as the artificial gravity and the microgravity (space environment) in Japanese Experiment Module JEM) of the International Space Station (ISS) on orbit for 35 days. After recovered these mice on the ground transcriptome analysis by next-generation sequencing technology is performed about spleen to examine alteration of gene expression in the space.

U-2 OS MERFISH data set prepared by the Han lab at UIUC based off of procedures developed in Moffitt et al. Proc. Natl. Acad. Sci. USA 113 (39), 11046–11051. Data is comprised of ~2 million spots from 130 genes with x,y,z location, cell assignment, and correction status.

Clinical interventions and inflammatory signaling shape the transcriptional and cellular architecture of the early postnatal lung

Spatial Transcriptomics was performed using the 10X Xenium Platform with a 480 custom-designed probe set on 1 tissue section from 5 distinct early postnatal lung specimens. CSV files contain cell type identities as determined by label transfer.

.zip files should be unzipped to the same directory and can be viewed with Xenium Explorer.

.csv files contain cell type annotations as determined by label transfer to hand annotated single nuclei RNA-sequencing data from early postnatal lung. They can be added as a custom cell group in Xenium Explorer.

Code used in analysis of this data is available at: http://github.com/jason-spence-lab/Frum-et-al.-2025a.git

METHODS
Tissue Preparation for Xenium Spatial Transcriptomics Analysis
Xenium slides were removed from -20°C storage and allowed to come to room temperature for 30 minutes and then were placed on a 42ºC slide warmed and coated with DNAse/RNAse free water (Corning, Cat# 46000CM). Small sections from multiple specimens were carefully placed within the sample placement area. Most of the water was removed when sections had completely flattened. Slides dried on the slide warmer for three hours before transport to the Advanced Genomics Core. Xenium slides were processed by the Advanced Genomics Core using the Xenium In SituGene Expression with Cell Segmentation workflow (10X, #CG000749).

Xenium Data Analysis
Preprocessing/QC Filtering
Centroids and Segmentation coordinates and Gene Expression counts were determined by Xenium Onboard Analysis v4.0 and imported into R using Seurat::ReadXenium(). Gene Expression counts were converted to a Seurat object using Seurat::CreateSeuratObject(). Coordinates for centroids and segmentations were first converted into a field of view using Seurat::CreateFOV() and then appended to the Seurat object. Segmentations with less than 25 gene expression counts were excluded from the analysis.

Label Transfer
To align low-complexity 480 probe Xenium data with higher complexity snRNA-seq data the reference data was transformed using Seurat::SCTransform() with 3000 variable features. Each specimen was processed individually, also undergoing SCTransformation using 250 variable features. Any Xenium probes expressed in over 95% of cells were excluded from analysis. Anchors between each specimen and the snRNA-seq reference were calculated using FindTransferAnchors() using the SCT assay of both datasets, 20 dimensions, k.filter = 200, and considering only the variable features from the Xenium specimen. Cell type annotations from the snRNA-seq data were then transferred to the Xenium specimen using TransferData(), with anchors weighted by the PCs of the Xenium specimen.

Recent advances in bulk, single-cell, and spatial transcriptomics have transformed biological discovery, yet fragmented tools hinder cross-modal integration. OmicVerse is a unified Python framework linking bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics through a standardized API and data model. It supports preprocessing, gene selection, cell type annotation, trajectory inference, batch correction, spatial mapping, and bulk-to-single-cell deconvolution with GPU–CPU acceleration. A novel Agent-based architecture enables adaptive analysis: dynamically benchmarking algorithms, optimizing pipelines, and integrating foundation models (e.g., scGPT, Geneformer, CellPLM) for embeddings, annotation, and conversational workflows. OmicVerse further offers publication-ready visualization, MOFA+ multi-omics integration, and pathway enrichment. This protocol guides installation, configuration, and execution from raw data to integrative, AI-augmented analyses, establishing a scalable standard for reproducible, cross-modal transcriptomics.

The size of the U.S. Spatial Genomics & Transcriptomics Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 12.7 % during the forecast period. Recent developments include: In March 2024, 10x Genomics, Inc. launched its Visium HD Spatial Gene Expression product, allowing researchers to analyze the entire transcriptome from FFPE tissue sections at a single cell-scale resolution. This innovative assay provides a comprehensive understanding of gene expression patterns within tissues, offering researchers a powerful tool to explore cellular biology in detail. , In March 2024, Johns Hopkins biomedical engineers invented an innovative computational technique to accurately align ST data across various samples, resolutions, and technologies, empowering researchers to delve deeper into cellular biology. This novel method, known as STalign, enhances their capacity to effectively compare spatial single-cell data, facilitating comprehensive insights into cellular organization and function. .

To understand the molecular mechanisms affected by spaceflight it is essential to achieve high quality sample preservation on-orbit for downstream gene expression analysis. However sample preservation protocols must also be compatible with available equipment and crew time. NASA s Rodent Research (RR) missions have used various methods for euthanasia carcass preservation and tissue preservation. This study extends the sample preservation study performed by GeneLab in GLDS-49 which examined conditions used for the RR-1 mission to include conditions used for multiple RR missions and is designed to help determine factors which may confound data analysis. To determine whether these various factors affect changes in gene expression this ground-based study generated gene expression profiles measured by RNAseq from the livers of 20-21 week-old female C57BL/6J mice. Multiple interacting factors were investigated: 1) To understand how euthanasia protocols affect gene expression when mouse carcasses are slow frozen mice were euthanized by either euthasol injection ketamine/xylazine injection or CO2 inhalation and carcasses slow frozen on dry-ice mimicking carcass preservation in the MELFI on the ISS. Carcasses were thawed and RNA extracted from livers; 2) To understand how carcass preservation protocols affect gene expression mice were euthanized with euthasol and carcasses preserved by flash freezing in liquid nitrogen slow freezing on dry ice or immersion in RNAlater following three-way segmentation. Carcasses were thawed and RNA extracted from livers; 3) To understand how tissue preservation protocols affect gene expression mice were euthanized with euthasol and livers dissected and processed immediately or preserved by flash freezing in liquid nitrogen slow freezing on dry ice or immersion in RNAlater. Liver samples that were processed immediately were homogenized in RLT buffer and then either immediately further processed for RNA extraction or were stored for 70 days at -80C post-homogenization in sample RLT buffer prior to RNA extraction.

Additional file 1: Supplementary Tables. Table S1. Summary for the scRNA-seq data with cell type annotation for different tissue in human and mouse. Table S2. Summary for the maker information for different tissue in human. Table S3. Computational consumption for three case studies. Table S4. Summary for association between genes associated with EMT and pseudo-time.

Transcriptomics Market Outlook

According to our latest research, the global transcriptomics market size reached USD 8.9 billion in 2024, demonstrating robust expansion driven by technological advancements and increasing demand for high-throughput sequencing. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, reaching an estimated value of USD 26.7 billion by 2033. This impressive growth trajectory is primarily attributed to escalating investments in genomics research, the rising prevalence of chronic diseases, and the growing adoption of personalized medicine worldwide. As per our latest research, the transcriptomics market is poised for substantial growth, underpinned by continuous innovation and expanding applications in both research and clinical settings.

One of the primary growth factors propelling the transcriptomics market is the rapid evolution and adoption of next-generation sequencing (NGS) technologies. These advancements have revolutionized the ability to analyze gene expression at a transcriptome-wide level, enabling researchers to obtain more accurate and comprehensive data in less time. The decreasing cost of sequencing has democratized access to these technologies, allowing a broader range of academic institutions, biotechnology firms, and healthcare providers to leverage transcriptomics in their research and clinical workflows. Moreover, the integration of artificial intelligence and machine learning tools with transcriptomic data analysis has further enhanced the precision and scalability of these studies, making it possible to uncover novel biomarkers and therapeutic targets with unprecedented efficiency.

Another significant driver of market growth is the increasing incidence of complex diseases such as cancer, neurological disorders, and cardiovascular diseases, which require advanced molecular profiling for effective diagnosis and therapy. Transcriptomics plays a pivotal role in the identification and validation of disease-specific biomarkers, facilitating early detection and the development of targeted therapies. Pharmaceutical and biotechnology companies are increasingly investing in transcriptomic approaches for drug discovery and development, as these methods provide critical insights into gene expression patterns and regulatory mechanisms underlying disease progression. The growing emphasis on personalized and precision medicine has further amplified the demand for transcriptomics, as clinicians and researchers strive to tailor interventions based on individual molecular profiles.

Government initiatives and funding for genomics research have also provided a substantial boost to the transcriptomics market. Several countries across North America, Europe, and Asia Pacific have launched national genomics projects and established collaborative research frameworks to accelerate advancements in transcriptomics and related fields. These initiatives have not only expanded the infrastructure for high-throughput sequencing and data analysis but have also fostered public-private partnerships, driving innovation and commercialization of transcriptomics technologies. Additionally, the increasing awareness and acceptance of molecular diagnostics in clinical practice, coupled with favorable regulatory environments, are expected to sustain the market’s momentum over the forecast period.

Regionally, North America continues to dominate the global transcriptomics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high concentration of leading pharmaceutical and biotechnology companies, robust research infrastructure, and substantial government funding in genomics research underpin North America’s leadership. Europe benefits from strong collaborative networks and significant investments in precision medicine, while Asia Pacific is witnessing rapid growth due to expanding healthcare infrastructure, increasing research activities, and rising adoption of advanced molecular technologies. Latin America and the Middle East & Africa are also experiencing gradual growth, driven by improving healthcare systems and growing awareness of the benefits of transcriptomic analysis.

• This dataset provides a complete end-to-end bioinformatics workflow built around the GSE57691 gene expression dataset.
• It includes all core steps typically used in transcriptomic data analysis for differential gene expression studies.
• Raw and intermediate files are organized to demonstrate a reproducible and transparent pipeline structure.
• The dataset covers data acquisition, preprocessing, normalization, and probe-to-gene annotation steps.
• Quality control analyses such as boxplots, density plots, PCA plots, and sample clustering are included.
• Differential expression analysis is performed using standard statistical methods suitable for microarray platforms.
• Outputs include tables of significantly upregulated and downregulated genes with adjusted p-values and fold changes.
• Multiple visualization assets are included to help interpret biological significance of detected expression changes.
• Plots include volcano plots, heatmaps, MA plots, and exploratory QC figures.
• The workflow demonstrates how to identify meaningful gene expression patterns between experimental groups.
• The dataset is structured so that users can understand and replicate a complete analysis starting from raw data.
• It is suitable for learners, researchers, or anyone wanting a practical reference for bioinformatics pipelines.
• The files can be used for training, project demonstrations, or teaching reproducible data analysis principles.
• This dataset provides clear examples of how to format results for downstream interpretation and publication use.
• It showcases how biological insights can be extracted from the GSE57691 dataset using standard bioinformatics tools.
• All generated outputs represent steps commonly used in modern transcriptomics research and analysis workflows.

NOTE: This dataset is no longer publicly available. This database houses over 500,000 sequences that were generated and assembled into approximately 15,000 contigs, annotated and functionally mapped to Gene Ontology (GO) terms. Blueberry (Vaccinium corymbosum) is a major berry crop in the United States. Next generation sequencing methodologies, such as 454, have been demonstrated to be successful and efficient in producing a snap-shot of transcriptional activities during an organism’s developmental stage(s) or its response to biotic or abiotic stresses. Such application of this new sequencing technique allows for high-throughput, genome-wide experimental verification of known and novel transcripts. We have applied a high-throughput pyrosequencing technology (454 EST sequencing) for transcriptome profiling of blueberry during different stages of fruit development to gain an understanding of the genes that are up or down regulated during this process. We have also sequenced flower buds at four different stages of cold acclimation to gain a better understanding of the genes and biochemical pathways that are up- or down-regulated during cold acclimation, since extreme low temperatures are known to reduce crop yield and cause major losses to US farmers. We have also sequenced a leaf sample to compare its transcriptome profile with that of bud and fruit samples. Over 500,000 sequences were generated and assembled into approximately 15,000 contigs and were annotated and functionally mapped to Gene Ontology (GO) terms. A database was developed to house these sequences and their annotations. A web based interface was also developed to allow collaborators to search\browse the data and aid in the analysis and interpretation of the data. The availability of these sequences will allow for future advances, such as the development of a blueberry microarray to study gene expression, and will aid in the blueberry genome sequencing effort that is underway. This work was supported by grant 2008-51180-04861 from the USDA - Cooperative State Research, Education, and Extension Service (CSREES) Specialty Crop Research Initiative program.