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8,068 results for “Transcriptome analysis”
Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks
<p>This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".</p>
Transcriptome analysis of the effect of over-expressing H2A.J mutants in proliferating WI38 fibroblasts for the paper entitled: The H2A.J histone variant contributes to Interferon-Stimulated Gene expression in senescence by its weak interaction with H1 and the derepression of repeated DNA sequences
<p>Abstract for overall study:</p> <p>The histone variant H2A.J was previously shown to accumulate in senescent human fibroblasts with persistent DNA damage to promote inflammatory gene expression, but its mechanism of action was unknown. We show that H2A.J accumulation contributes to weakening the association of histone H1 to chromatin and increasing its turnover. Decreased H1 in senescence is correlated with increased expression of some repeated DNA sequences, increased expression of STAT/IRF transcription factors, and transcriptional activation of Interferon-Stimulated Genes (ISGs). The H2A.J-specific Val-11 moderates the transcriptional activity of H2A.J, and H2A.J-specific Ser-123 can be phosphorylated in response to DNA damage with potentiation of its transcriptional activity by the phospho-mimetic S123E mutation. Our work demonstrates the functional importance of H2A.J-specific residues and potential mechanisms for its function in promoting inflammatory gene expression in senescence.</p> <p>Specific description for this dataset:</p> <p>H2A.J differs from canonical H2A only by a valine at position 11 instead of alanine, and the 7 C-terminal amino acids containing a potential minimal phosphorylation site SQ for DNA-damage response kinases. To test the functional importance of these H2A.J-specific sequences, we mutated Val-11 to Ala as is found in all canonical H2A sequences, and we mutated Ser-123 to either Glu to mimic a phospho-serine residue or to Ala to prevent phosphorylation. We also substituted the C-terminus of H2A.J with the C-terminus of H2A. These mutants, WT-H2A.J and canonical H2A-type1 were ectopically expressed in proliferating fibroblasts, and their microarray transcriptomes were compared to that of proliferating and senescent fibroblasts without ectopic histone expression. Genome-wide transcriptome analysis indicated that senescent fibroblasts clustered distinctly from proliferating fibroblasts, and proliferating fibroblasts expressing the H2A.J-V11A and H2A.J-S123E mutants clustered distinctly from fibroblasts expressing the other H2A.J mutants, WT-H2A.J, and H2A. Hallmark gene set enrichment analysis of the transcriptomes of fibroblasts expressing H2A.J-V11A or H2A.J-S123E versus control proliferating fibroblasts indicated that they showed the same highly significant enrichment for the Epithelial-Mesenchyme Transition, TNF-Alpha Signaling Via NF-kB, and Inflammatory Response gene sets. Notable inflammatory genes including IL1A, IL1B, IL6, CXCL8, and CCL2 are contained in these gene sets and are often induced in senescence as part of the senescence-associated secretory phenotype. Heat maps showed that the H2A.J-V11A and H2A.J-S123E mutants were particularly apt at activating the expression of these inflammatory genes in proliferating fibroblasts</p>
Data from A functional transcriptomics analysis in the relict marsupial Dromiciops gliroides reveals adaptive regulation of protective functions during hibernation
<p>This dataset contains files with the differentially expressed genes, raw counts, DESeq2 analyses and assembled transcriptome of D. gliroides. This information is linked to the manuscript published in Molecular Ecology.</p>
Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Genome alignments for data generated in the project "<em>Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol.</em>" Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs:</p> <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> <p>This data can be analysed using the "CAGEr" software package available from Bioconductor. The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.</p>
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </p>
Data from "Corset: enabling differential gene expression analysis for de novo assembled transcriptomes"
<p>This dataset contains de novo transcriptome assemblies for three publicly available RNA-seq dataset (SRA055442, SRR453566-SRR453571 and GSE37704 ). For each assembly we also provide a table with the read counts per contig, the output from corset (clusters and counts), and the results from a genome-based analysis. This dataset was used to assess the performance of the corset software. More detail is provided in the paper: Nadia M Davidson and Alicia Oshlack,<strong> </strong>Corset: enabling differential gene expression analysis for de novo assembled transcriptomes, <em>Genome Biology</em> 2014, <strong>15</strong>:410. http://genomebiology.com/2014/15/7/410/abstract</p>
Supplementary File 7 from: Rapier-Sharman N et. al., Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers, 2024
<p>Supplementary Materials File 7. Please note that though the order of the supplementary materials has changed since initial upload (File S7 was previously File S9 or S10), the contents of this zipped folder remain the same.</p>
Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes
<p>Abnormal gene expression and splicing play a key role in hematologic malignancy. Here, we provide a catalog of transcriptomic and genomic aberrations of 3,760 hematologic malignancy samples spanning 24 disease entities. This version contains 19,732 protein-coding genes from GRCh37 annotated by Gencode (v33b).</p> <p>doi: <a href="https://doi.org/10.1101/2023.08.08.23293420">https://doi.org/10.1101/2023.08.08.23293420</a></p>
Data from: Transcriptome analysis of apical meristem enriched bud samples for size dependent flowering commitment in Crocus sativus reveal role of sugar and auxin signalling
<p><strong>Background</strong></p> <p>Cultivation of <em>Crocus sativus</em> (saffron) faces challenges due to inconsistent flowering patterns and variations in yield. Flowering takes place in a graded way with smaller corms unable to produce flowers. Enhancing the productivity requires a comprehensive understanding of the underlying genetic mechanisms that govern this size based flowering initiation and commitment. Therefore, samples enriched with non-flowering and flowering apical buds from small (<6g) and large (>14g) corms were sequenced. </p> <p><strong>Methods and Results</strong></p> <p>Apical bud enriched samples from small and large corms were collected immediately after break of dormancy in July. RNA sequencing was performed using Illumina Novaseq 6000. <em>De-novo</em> transcriptome assembly and analysis using flowering committed buds from large corms at post-dormancy and their comparison with vegetative shoot primordia from small corms pointed out the major role of Auxin and ABA hormonal regulation. Many genes with known dual responses in flowering development and circadian rhythm like Flowering locus T and Cryptochrome 1 along with a transcript showing homology with small auxin upregulated RNA (SAUR) exhibited induced expression in flowering buds. Thorough prediction of <em>Crocus sativus</em> non-coding RNA repertoire has been carried out for the first time. Enolase was found to be acting as a major hub with protein-protein interaction analysis using Arabidopsis counterparts.</p> <p><strong>Conclusion</strong></p> <p>Transcripts belong to key pathways including phenylpropanoid biosynthesis, hormone signaling and carbon metabolism were found significantly modulated. KEGG assessment and protein-protein interaction analysis confirm the expression data. Findings unravel the genetic determinants driving the size-dependent flowering in <em>Crocus sativus</em>.</p>
Corallorhiza maculata genomic and transcriptomic analysis
<p>Novoplasty assemblies of plastid genomes from two different Corallorhiza maculata plants (Circularized_assembly_1_CM_1A.fasta and Circularized_assembly_1_CM_2A.fasta).</p> <p>Spades assembly of total cellular genomic DNA from one Corallorhiza maculata plant (scaffolds.fasta.gz). This assembly includes scaffolds of mitochondrial origin (as well as plastid and nuclear).</p> <p>Trinity assembly of rRNA-depleted RNA-seq reads from one Corallorhiza maculata plant (trinity_out_dir.Trinity.fasta.gz).</p>
Comparative host transcriptomics as a tool to identify candidate biomarkers for immune reactions in leprosy: A meta-analysis study
<p>The dataset consists of R source code for the individual dataset analysis of the studies and their meta-analysis. It also contains supplementary tables and figure.</p>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
Transcriptomic analysis of CTC at different timepoints
<p>This repository contains processed transcriptomics data, large data sets and additional files related to the Diamantopoulou et al. (2022).</p>
Transcriptomic analysis of deceptively pollinated Arum maculatum (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction
<p>A compressed folder containing the R script and input files required to replicate the results in our manuscript entitled "Transcriptomic analysis of deceptively pollinated <em>Arum maculatum</em> (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction".</p> <p>Note: Raw Illumina sequencing files associated with this study have been uploaded to NCBI SRA, under the BioProject accession PRJNA856436.</p> <p><strong>ABSTRACT</strong></p> <p>Deceptive pollination often involves volatile organic compound (VOC) emissions that mislead insects into performing non-rewarding pollination. Among deceptively pollinated plants, <em>Arum maculatum</em> is particularly well-known for its potent dung-like VOC emissions and specialized floral chamber, which traps pollinators – mainly <em>Psychoda phalaenoides</em>and <em>P. grisescens</em> – overnight. However, little is known about the genes underlying the production of many <em>A. maculatum</em>VOCs, and their influence on variation in pollinator attraction rates. Therefore, we performed <em>de novo</em> transcriptome sequencing of <em>A. maculatum</em> appendix and male floret tissue collected during- and post-anthesis, from ten natural populations across Europe. These RNA-seq data were paired with GC-MS analyses of floral scent composition and pollinator data collected from the same inflorescences. Differential expression analyses revealed candidate transcripts in appendix tissue linked to malodourous VOCs including indole, <em>p</em>-cresol, and 2-heptanone. Additionally, we found that terpene synthase expression in male floret tissue during anthesis significantly covaried with sex- and species-specific attraction of <em>Psychoda phalaenoides</em> and <em>P.</em> <em>grisescens</em>. Taken together, our results provide the first insights into molecular mechanisms underlying pollinator attraction patterns in <em>A. maculatum</em>, and highlight floral chamber sesquiterpene (<em>e.g.</em>bicyclogermacrene) synthases as interesting candidate genes for further study.</p>
Supplementary data: Medicago transcriptomics DRMN analysis
<p>Summary of DRMN per-gene module assignments, module motif enrichments, inferred network edge weights, and MTG-LASSO predictions, Supplementary data tables 1-4 of this submission, respectively. </p>
The Supplementary Material for the article entitled "Comparative analysis of global transcriptomes in nontyphoidal Salmonella clinical isolates from pediatric patients with and without bacteremia after infecting human intestinal epithelium in vitro"
<p>The Supplementary Material (Additional files 1-5, including Table S1-S4 and Figure S1) for this article.</p> <p> </p> <p><strong>Table S1.</strong> Upregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S2.</strong> Downregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S3. </strong>The enriched GO terms in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S4. </strong>The enriched KEGG pathways in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Figure S1. </strong>The enriched GO terms and KEGG pathways in Group B relative to Group A. Bar charts show the enriched GO terms (A) and the enriched KEGG pathways (B) by significance power. Color of bars indicate power of significance and length in x axes of bar indicate number of annotated genes in the particular term of pathway. Cnetplots show the relationship between GO term (C) and KEGG pathways (D). Dot size representing GO terms and KEGG pathways indicates number of significantly changed and its annotated genes. The GO terms or KEGG pathways connected through their common and annotated genes. </p>
Meta-analysis of diurnal transcriptomics reveals strong patterns of concordance and discordance in mouse liver: processed data
<p>The accumulation of public transcriptomic timeseries data enables robust meta-analyses that were not possible until recently. To assess the consistency of biological rhythms across studies, 43 public mouse liver tissue timeseries totaling 805 RNA-seq samples were obtained and analyzed. Only the control groups of each study were included, to create comparable data. Technical factors in RNA-seq library preparation were the largest contributors to transcriptome-level differences, beyond biological or experiment-specific factors such as lighting conditions. Core clock genes were remarkably consistent in phase across all studies, while phase distributions of other periodic genes were generally less consistent. Overlap of genes identified as rhythmic across studies was generally low, with around 50% between some of the highest sample count studies. Distributions of phases of significant genes were remarkably inconsistent across studies, but genes consistently identified as rhythmic clustered near ZT0 and ZT12 in acrophase. Data was integrated across studies in a JIVE analysis, which showed that the top two components of joint within-study variation are determined by time of day. A shape-invariant model with random effects was fit to the genes to identify the underlying shape of the rhythms, consistent across all studies. This revealed the extent of asymmetric and multimodal genes.<br> <br> This supplemental file provides preprocessed RNA-seq quantifications of all reviewed datasets, as well as results of multiple analyses.</p>
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</p>
Figure A1 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure A1: Principal components analysis of variation (A) and correlation coefficient analysis (B) among sequenced transcriptomes to show correlation among samples (control, CK1-3 and the treated samples, P1-3).
Figure A3 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure A3: Observation of the normal PWNs (A) and punicalagin-treated PWNs twisting abnormally (B) under microscope.
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