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6,225 results for “expression analysis”
Co-expression trans-eQTL analysis data
<p><strong>Sample metadata:</strong></p> <ul> <li>sample_metadata.tsv - tab separated file with metadata for samples used in this analysis.</li> </ul> <p><strong>Gene expression matrix:</strong></p> <ul> <li>Merged_ENSG_expression.tsv - integrated gene expression matrix used in the co-expression analysis (Ensembl IDs in rows and sample IDs in columns). The matrix can be split into sub matrices using the metadata from sample_metadata.tsv for separate analysis of cell types.</li> </ul> <p><strong>Genes in the modules:</strong></p> <ul> <li>ICA_modules.tsv - data for assigning genes to modules from ICA method. Column 'loading' shows the contribution value of the gene to the module. Column 'approach' indicates if the module was detected from integrated or separate data. Column 'qtl_group' is an identifier for the cell type/condition.</li> <li>PEER_modules.tsv - data for assigning genes to modules from PEER method. Column 'loading' shows the contribution value of the gene to the module. </li> <li>PLIER_modules.tsv - data for assigning genes to modules from PLIER method. Column 'loading' shows the contribution value of the gene to the module.</li> <li>WGCNA_modules.tsv - data for assigning genes to modules from WGCNA method. Column 'loading' shows the contribution value of the gene to the module. Column 'loading' has value 1 as the method assigns genes to modules without defining their contributions. </li> <li>funcExplorer_modules.tsv - data for assigning genes to modules from funcExplorer method. Column 'loading' has value 1 as the method assigns genes to modules without defining their contributions. </li> </ul> <p><strong>Module eigenvectors used for the trans-eQTL analysis</strong></p> <ul> <li>eigenvectors_integrated.tsv - module eigenvectors for all the co-expression analysis methods applied to integrated data</li> <li>eigenvectors_separate.tsv- module eigenvectors for all the co-expression analysis methods applied to cell types separately</li> </ul> <p><strong>SuSiE fine mapping credible sets </strong></p> <ul> <li>SuSiE_credible_sets_integrated.tsv</li> <li>SuSiE_credible_sets_separate.tsv</li> </ul>
Squamous cell carcinoma of the lung: gene expression and network analysis during carcinogenesis (additional data)
<p>Additional files regarding the results of the research.</p>
Data from: Comprehensive transcriptome analysis of Crocus sativus for discovery and expression of genes involved in apocarotenoid biosynthesis
Background: Crocus sativus stigmas form rich source of apocarotenoids like crocin, picrocrocin and saffranal which besides imparting color, flavour and aroma to saffron spice also have tremendous pharmacological properties. Inspite of their importance, the biosynthetic pathway of Crocus apocarotenoids is not fully elucidated. Moreover, the mechanism of their stigma specific accumulation remains unknown. Therefore, deep transcriptome sequencing of Crocus stigma and rest of the flower tissue was done to identify the genes and transcriptional regulators involved in the biosynthesis of these compounds. Results: Transcriptome of stigma and rest of the flower tissue was sequenced using Illumina Genome Analyzer IIx platform which generated 64,604,402 flower and 51,350,714 stigma reads. Sequences were assembled de novo using trinity resulting in 64,438 transcripts which were classified into 32,204 unigenes comprising of 9853 clusters and 22,351 singletons. A comprehensive functional annotation and gene ontology (GO) analysis was carried out. 58.5 % of the transcripts showed similarity to sequences present in public databases while rest could be specific to Crocus. 5789 transcripts showed similarity to transcription factors representing 76 families out of which Myb family was most abundant. Many genes involved in carotenoid/apocarotenoid pathway were identified for the first time in this study which includes zeta-carotene isomerase and desaturase, carotenoid isomerase and lycopene epsilon-cyclase. GO analysis showed that the predominant classes in biological process category include metabolic process followed by cellular process and primary metabolic process. KEGG mapping analysis indicated that pathways involved in ribosome, carbon and starch and sucrose metabolism were highly represented. Differential expression analysis indicated that key carotenoid/apocarotenoid pathway genes including phytoene synthase, phytoene desaturase and carotenoid cleavage dioxygenase 2 are enriched in stigma thereby providing molecular proof for stigma to be the site of apocarotenoid biosynthesis. Conclusions: This data would provide a rich source for understanding the carotenoid/apocarotenoid metabolism in Crocus. The database would also help in investigating many questions related to saffron biology including flower development.
Data from: De novo transcriptome assembly and analysis of differential gene expression in response to drought in European beech
Despite the ecological and economic importance of European beech (Fagus sylvatica L.) genomic resources of this species are still limited. This hampers an understanding of the molecular basis of adaptation to stress. Since beech will most likely be threatened by the consequences of climate change, an understanding of adaptive processes to climate change-related drought stress is of major importance. Here, we used RNA-seq to provide the first drought stress-related transcriptome of beech. In a drought stress trial with beech saplings, 50 samples were taken for RNA extraction at five points in time during a soil desiccation experiment. De novo transcriptome assembly and analysis of differential gene expression revealed 44,335 contigs, and 662 differentially expressed genes between the stress and normally watered control group. Gene expression was specific to the different time points, and only five genes were significantly differentially expressed between the stress and control group on all five sampling days. GO term enrichment showed that mostly genes involved in lipid- and homeostasis-related processes were upregulated, whereas genes involved in oxidative stress response were downregulated in the stressed seedlings. This study gives first insights into the genomic drought stress response of European beech, and provides new genetic resources for adaptation research in this species.
Data from: Differential Expression Analysis for Pathways
Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.
Database of expression analysis of samples of the 1st trial (A22) of sheep breeds in Latvia within the framework of the project of the Latvian Council of Science LZP-2021/1-0489 project
<p><span>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>”.</span></p> <p><span>The <strong>aim of the project</strong> is</span><span> to determine whether the feed efficiency status of <span>Latvian meat sheep breeds</span> could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent FE status in a training population of lambs when fed the same diet.</span></p> <p><strong><span>Novelty</span></strong><span>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs. </span></p> <p><strong><span>About the project:</span></strong></p> <p><span>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two most significant components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development<span>.</span> <span>Feed efficiency </span>in growing lambs (i.e., the animal’s ability to reach a market or adult body weight (BW) with the least feed intake) is a critical factor in the sheep industry. <span>Improving FE reduces production costs. Improving FE by 5% can bring economic benefits up to four times higher than a 5% increase in average daily gain (ADG).</span></span></p> <p><span>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in <span>feed efficiency </span>that they were used as part of a routine improvement program. According to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. <span>The physiological determinants of feed efficiency or putative biomarkers used to analyse animal-to-animal variation in live lambs could be a cost-effective and rapid tool for genetic selection or management decisions.</span></span></p> <p><strong><span>About the data of the project:</span></strong></p> <p><span>LZP-2021/1-0489 project data on lamb samples of the year 2022, or the A22 group, which consists of 76 intensively fattened lambs from six breeds. The blood samples for RNA extraction were taken after intensive fattening at an average body weight of 45 – 50 kg.</span></p> <p><span>Gene expression levels were determined using qPCR methodology using the GAPDH gene as a reference. Three genes were analysed in the project: MTOR (The mechanistic mammalian target of rapamycin), CAST (Calpastatin) and UPC3 (uncoupling protein 3).</span></p> <p><span>The data obtained will be used to determine the changes in expression levels related to gene variations (MTOR: <a href="https://doi.org/10.5281/zenodo.8146731">https://doi.org/10.5281/zenodo.8146731</a>; CAST: <a href="https://doi.org/10.5281/zenodo.8146372">https://doi.org/10.5281/zenodo.8146372</a>; UPC3: <a href="https://doi.org/10.5281/zenodo.8146778">https://doi.org/10.5281/zenodo.8146778</a>); <span> </span>as well as to analyze the relationship of expression levels with biochemical parameters (<a href="https://doi.org/10.5281/zenodo.8143225">https://doi.org/10.5281/zenodo.8143225</a>) and feed efficiency indicators (<a href="https://doi.org/10.5281/zenodo.8143244">https://doi.org/10.5281/zenodo.8143244</a>) of lambs.</span></p> <p><strong><span> </span></strong></p> <p><span>The data is the<strong><span> joint property</span></strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</span></p>
Identification of Potential Functional Modules and Diagnostic Genes for Crohn's Disease Based on Weighted Gene Co-expression Network Analysis and LASSO Algorithm
<p>Table S1 651 DEGs between CD and control samples</p> <p>Table S2 381 ME turquoise module genes</p> <p>Table S3 Coefficients of the eight module genes analyzed by LASSO regression</p>
Bar Chart of the Analysis of the Mean Number of SCUBE3 and TGF-B Expressing Cells in the Treatment Group
<p>The following diagrams present the results of the analysis of SCUBE3 and TGF-b gene expression. These diagrams are divided into eight main groups based on sample preparation size and incubation time. These groups consist of diagrams that present the relationship between treatment groups in 1x1 cm and 2x2 cm preparations, which have 24-hour and 72-hour incubation times for SCUBE3 and TGF-b, respectively. </p>
The raw data about the research of identification of biomarkers related to systemic sclerosis with or without pulmonary hypertension via co-expression analysis
<p>This file includes some of necessary raw data and code about the research of identification of biomarkers related to systemic sclerosis with or without pulmonary hypertension via co-expression analysis.</p> <p> </p>
Dataset for "Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine"
<p>Dataset:</p> <p>Data1. Host gene expression profile</p> <p>Data2. Microbiome gene expression profile</p>
Results of differential expression analysis for the manuscript "N-of-one differential gene expression without control samples using a deep generative model"
<p>Output files for the differential gene expression analysis in the manuscript "N-of-one differential gene expression without control samples using a deep generative model". The zip file contains three folders with the results for DESeq2 and the DGD (named NB-GMM in the folders).</p><p>- The folder False_positives_Fig4B contains the results presented in figure 4B of the manuscript. The file False_positive_single_vs_all.csv contains the results for the one-vs-all experiments. The file False_positive_5_vs_all.csv contains the results for the 5-vs-all experiments.</p><p>- The folder enrichment_plots contains the results presented in figure 4C of the manuscript. Each subfolder (e.g. Basal, HER2E, LumA, LumB) contain the differential expression analysis results for each breast cancer subtype.</p><p>- The folder fig5 contains the results presented in figure 5 of the manuscript. The results for each cancer are presented in a folder.</p>
Analysis of Expression of Specific Markers in Hepatocellular Carcinoma
ClinicalTrials.gov study NCT00858000. IPD Sharing: Not stated. Countries: 1. Publications: 0.
S9313A Protein Expression Analysis of Breast Cancer Tissue Microarrays From Clinical Trial SWOG-9313
ClinicalTrials.gov study NCT00896727. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Harnessing Analysis RNA Expression and Molecular Subtype to Optimize Novel TherapY MBCA
ClinicalTrials.gov study NCT03769415. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Gene Expression and DNA Variation Analysis of Sacs to Identify the Pathophysiology of Indirect Inguinal Hernia
ClinicalTrials.gov study NCT04634032. IPD Sharing: NO. Countries: 1. Publications: 0.
Microarray Analysis of Gene Expression and Identification of Progenitor Cells in Lung Carcinoma
ClinicalTrials.gov study NCT00568906. IPD Sharing: Not stated. Countries: 1. Publications: 0.
SynapDx Autism Gene Expression Analysis Study
ClinicalTrials.gov study NCT01716845. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detection and Analysis of MBC With Heterogeneous ER Expression
ClinicalTrials.gov study NCT05392985. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Analysis of miRNAs Expression in Vasoplegic Syndrome After On-pump Coronary Artery Bypass Surgery
ClinicalTrials.gov study NCT05354193. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Analysis of Oculo-motor Deficiencies Associated With FMR1 Gene Expression (Genetic Abnormality Predisposing to a Neurodegenerative Disease)
ClinicalTrials.gov study NCT00763191. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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