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25,372 results for “Transcriptomics”
Transcriptomics Reveal Specific Molecular Mechanisms Underlying Transgenerational Immunity in Manduca sexta
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Midgut transcriptome assessment of the cockroach-hunting wasp Ampulex compressa (Apoidea: Ampulicidae)
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NFAT transcriptome in T-ALL
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Cinchona pubescens Transcriptome
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Timing of blood sample processing affects the transcriptomic and epigenomic profiles in CD4+ T-cells of atopic subjects
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Single cell transcriptomic analyses reveal the impact of bHLH factors on human retinal organoid development
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Comparative transcriptomics of a monocotyledonous geophyte reveals shared molecular mechanisms of underground storage organ formation
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Systematic profiling of full-length immunoglobulin and T-cell receptor repertoire diversity in rhesus macaque through long read transcriptome sequencing
<p>Using long read sequencing, we sequenced four Indian-origin rhesus macaque tissues. From raw full-length, non-chimeric circular consensus sequencing (CCS) reads, we obtained high quality, full-length sequences for over 6,000 unique immunoglobulin and T-cell receptor transcripts, without the need for sequence assembly.</p>
Supplementary Data for Publication Titled "Sex differences in regulating the cardiac transcriptome within a murine model for hypertrophic cardiomyopathy"
<p>Supplementary data for publication titled "Sex differences in regulating the cardiac transcriptome within a murine model for hypertrophic cardiomyopathy".</p>
Data analysis scripts for the publication - Differences in the transcriptomic response of Campylobacter coli and Campylobacter lari to heat stress (Riedel et al.)
<p>Data analysis scripts and data for the publication "Differences in the transcriptomic response of Campylobacter coli and Campylobacter lari to heat stress" by Riedel <em>et al.</em>.</p>
Progress Towards Plant Community Transcriptomics: Pilot RNA-Seq Data from 24 Species of Vascular Plants at Harvard Forest
<p>Assembled transcriptomes of 24 vascular plant species from Harvard Forest. Transcriptomes for each species were sequenced and assembled as described below. Additional details available in the associated manuscript: https://doi.org/10.1101/2020.03.31.018945. Raw reads for each available at NCBI SRA SRP127805 and BioProject PRJNA422719.</p> <p><strong>Taxon selection and sampling </strong></p> <p>The Harvard Forest Flora <a href="https://paperpile.com/c/nMUYEV/hd5w">(Jenkins et al., 2008)</a> was used to select taxa to represent each category (native/invasive, diploid/polyploid). Invasive species status was determined from the Harvard Forest Flora Database <a href="https://paperpile.com/c/nMUYEV/g5Tw">(Jenkins and Motzkin, 2009)</a>. Putative diploids and neo-polyploid species were identified from chromosome counts obtained from the Chromosome Counts Database <a href="https://paperpile.com/c/nMUYEV/6Vzo">(Rice et al., 2015)</a>. Congeneric species pairs were selected based on their phylogenetic relatedness. The Harvard Forest Flora Database was used to assesscalculate recent encounter rates of each target species, and locate sampling sites. </p> <p>Tissue from mature leaves was collected from an individual representing each target species at two time points (July and August) during the 2016 growing season. The same individual was sampled at both time points for perennial individuals, and the same population was sampled for annuals. Field sampling for plant RNA-seq followed the protocol described in Yang et al. 2017 <a href="https://paperpile.com/c/nMUYEV/n5lK">(Yang et al., 2017)</a>. Leaf tissues were flash frozen in liquid nitrogen in the field, and shipped on dry ice to the University of Arizona for RNA extraction.</p> <p> </p> <p><strong>RNA extraction and RNA-seq</strong></p> <p>Total RNA was extracted from leaf tissue collected at each time point for all species using the Spectrum Plant Total RNA Kit (Sigma-Aldrich Co., St. Louis, MO, USA) following Protocol A. RNA was used to prepare cDNA using Nugen’s Ovation RNA-Seq System via single primer isothermal amplification (Catalogue # 7102-A01) and automated on the Apollo 324 liquid handler (Wafergen). cDNA was quantified on the Nanodrop (Thermo Fisher Scientific) and was sheared to approximately 300 bp fragments using the Covaris M220 ultrasonicator. Libraries were generated using Kapa Biosystem’s library preparation kit (KK8201). Fragments were end repaired and A-tailed, and individual indexes and adapters (Bioo, catalogue #520999) were ligated on each separate sample. The adapter ligated molecules were cleaned using AMPure beads (Agencourt Bioscience/Beckman Coulter, A63883), and amplified with Kapa’s HIFI enzyme (KK2502). Each library was then analyzed for fragment size on an Agilent’s Tapestation, and quantified by qPCR (KAPA Library Quantification Kit, KK4835) on Thermo Fisher Scientific’s Quantstudio 5 before multiplex pooling (13-16 samples per lane) and paired-end sequencing at 2x150 bp on the Illumina NextSeq500 platform at Arizona State University’s CLAS Genomics Core facility. Raw read quality was assessed using fastQC <a href="https://paperpile.com/c/nMUYEV/mvKN">(Andrews, 2010)</a>.</p> <p> </p> <p><strong><em>De novo</em> transcriptome assembly</strong></p> <p>Raw sequence reads were processed using the SnoWhite pipeline <a href="https://paperpile.com/c/nMUYEV/4el87+4OvN">(Barker et al., 2010a; Dlugosch et al., 2013)</a>, which included trimming adapter sequences and bases with a quality score below 20 from the 3' ends of all reads, removing reads that are entirely primer and/or adapter fragments using TagDust <a href="https://paperpile.com/c/nMUYEV/4I38S">(Lassmann et al., 2009)</a>, and removing polyA/T tails with SeqClean (<a href="https://sourceforge.net/projects/seqclean/">https://sourceforge.net/projects/seqclean/</a>). The cleaned reads from each sample time point were merged together by pairs, and pooled to assemble a reference de novo transcriptome for each species. All transcriptomes were assembled with SOAPdenovo-Trans v1.03 <a href="https://paperpile.com/c/nMUYEV/3Szww">(Xie et al., 2014)</a> using a k-mer of 57.</p>
Transcriptome and Metabolome Reprogramming in Tomato Plants by Trichoderma Harzianum strain T22 Primes and Enhances Defense Responses Against Aphids
<p><strong>Figure 1</strong></p> <p>Effect of <em>T. harzianum</em> T22 on aphid survival over time. Survival curves (percentage) of <em>M. euphorbiae</em> reared on the untreated water control and the <em>T. harzianum</em> T22 treated tomato plants are significantly different, <em>p</em> < 0.05 (LogRank test).</p> <p>For the aphid longevity assay, 10 plants for each CTRL or T22 treatment were infested with 5 newly born first instar nymphs of <em>M. euphorbiae</em>. The presence of aphids and of shed exuviae, as an indicator of molting occurrence, was daily monitored. Survival curves were compared by LogRank analysis.</p>
The transcriptomic profiling of COVID-19 compared to SARS, MERS, Ebola, and H1N1
<p><strong>COVID-19 </strong>pandemic is a global crisis that threatens our way of life. As of April 29, 2020, COVID-19 has claimed more than 200,000 lives, with a global mortality rate of ~7% and recovery rate of ~30%. Understanding the interaction of cellular targets to the SARS-CoV2 infection is crucial for therapeutic development. Therefore, the aim of this study was to perform a comparative analysis of transcriptomic signatures of infection of COVID-19 compared to different respiratory viruses (Ebola, H1N1, MERS-CoV, and SARS-CoV), to determine unique anti-COVID1-19 gene signature. We identified for the first time molecular pathways for Heparin-binding, RAGE, miRNA, and PLA2 inhibitors, to be associated with SARS-CoV2 infection. The <em>NRCAM</em> and <em>SAA2</em> that are involved in severe inflammatory response, and <em>FGF1</em> and <em>FOXO1</em>genes, which are associated with immune regulation, were found to be associated with a cellular gene response to COVID-19 infection. Moreover, several cytokines, most significantly the <em>IL-8</em>, <em>IL-6</em>, demonstrated key associations with COVID-19 infection. Interestingly, the only response gene that was shared between the five viral infections was <em>SERPINB1</em>. The PPI study sheds light on genes with high interaction activity that COVID-19 shares with other viral infections. The findings showed that the genetic pathways associated with Rheumatoid arthritis, AGE-RAGE signaling system, Malaria, Hepatitis B, and Influenza A were of high significance. We found that the virogenomic transcriptome of infection, gene modulation of host antiviral responses, and GO terms of both COVID-19 and Ebola are more similar compared to SARS, H1N1, and MERS. This work compares the virogenomic signatures of highly pathogenic viruses and provides valid targets for potential therapy against COVID-19.</p> <p><strong>Supplementary tables and figures</strong></p> <p><strong>Figure 1</strong> : Significant DEGs across the five transcriptomic profiles , corresponding genes, chromosome locations, gene expression and significance scores. The DEGs related genes and chromosomal location (A). The DEGs information regarding host response to COVID-19 (B), Ebola (C), MERS-CoV (D) , H1N1 (E) and SARS-CoV (F) viral infections. The pvalues were scaled were scaled across gene profiles according to maximum and minimum values (ppvalue). The circles size and color is linked to DEGs significance and gene expression (LogFC) scores, respectively.</p> <p><strong>Figure 2 : </strong>Analysis of the gene enrichment of DEGs correlated with the host response to COVID-19. Categories of GO terms (A), significance scores (-10log-pvalue) (B), and number of associated DEGs (C). The COVID-19-associated DEGs (D), status across the studied infectious diseases (E), and selected linked GO terms (F).</p> <p><strong>Figure 3: </strong>The Venn diagram of viral associated genes. The number of uniquely shared genes associated with the host response to COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Figure 4: </strong>The Venn diagram of viral associated GO terms. The number of uniquely shared GO terms of DEGs associated with the host response across COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Figure 5: </strong>The PPIs network of DEGs associated with COVID-19. The PPI of host expressed DEGs under COVID-19 infection. DEGs shared between COVID-19 and Ebola, H1N1, MERS-CoV, and SARS-CoV are color-coded according to kind of infection. The gene node size is relative to its interaction activity. DEGs are collected in different groups according to their level of interaction activity.<br> </p> <p><strong>Figure 6: </strong>The PPIs network and gene enrichment analysis of highly interactive genes associated with COVID-19.</p> <p><strong>Figure S1 :</strong> The PPI network and gene enrichment analysis of the 173 genes that characterized the host response of COVID-19.</p> <p><strong>Figure S2: </strong>The PPI network and gene enrichment analysis of the 58 genes that are uniquely shared between COVID -19 and Ebola viral infections .</p> <p><strong>Figure S3 : </strong>The PPI network and gene enrichment analysis of the 51 genes that are uniquely shared between COVID-19 and MERS-CoV viral infections.</p> <p><strong>Figure S4 : </strong>The PPI network and gene enrichment analysis of the 31 genes that are uniquely shared between COVID-19, Ebola, and MERS-CoV viral infections.</p> <p><strong>Figure S5</strong> : The gene expression heatmap of genes COVID-19 shares with different viral infections.</p> <p><strong>Figure S6 : </strong>The PPI network and gene enrichment analysis of genes that are differentially expressed across studied viral infections and shared with COVID-19.</p> <p><strong>Table </strong><strong>S</strong><strong>1 : </strong>The data information used in this study.</p> <p><strong>Table S</strong><strong>2</strong>: The information of DEGs associated the host response of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Table S</strong><strong>3</strong>: The Venn analysis results of DEGs and GO terms uniquely shared across of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Table S</strong><strong>4</strong>: Selected gene enrichment analysis of uniquely shared group of genes across the host response of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Table S</strong><strong>5</strong>: The gene expression information of DEGs that COVID-19 share with the studied infectious diseases.</p> <p><strong>Table S</strong><strong>6</strong>: Selected gene enrichment analysis of uniquely shared group of GO terms across the host response of COVID-19 and studied viral infections.</p>
PhenomeXcan: Mapping the genome to the phenome through the transcriptome
<p>Data generated as part of the PhenomeXcan project, which contains processed results for S-MultiXcan, S-PrediXcan, fastENLOC and the integration of S-MultiXcan results with ClinVar. Please, refer to the Github repository for documentation on these files and related source code (<a href="https://github.com/hakyimlab/phenomexcan">https://github.com/hakyimlab/phenomexcan</a>).</p> <p>This latest version of the dataset contains the fixed results for fastENLOC (with shrinkage of alpha1 values).</p> <p>bioRxiv preprint: https://doi.org/10.1101/833210</p>
Single cell transcriptomes of of primary tumors and normal endometrial derived organoids treated with DBZ
<p>Endometrial carcinoma, the most common gynecologic cancer, develops from endometrial epithelium which is composed of secretory and ciliated cells. Pathologic classification is unreliable and there is a need for prognostic tools. We used single cell sequencing to study organoid model systems derived from normal endometrial endometrium to discover novel markers specific for endometrial ciliated or secretory cells. We performed 10X based single cell sequencing on both normal and DBZ treated organoids, and on endometrial and ovarian tumours.</p>
Proteo-transcriptomic analysis identifies potential novel toxins secreted by the predatory, prey-piercing ribbon worm Amphiporus lactifloreus
<p>Nemerteans (ribbon worms) employ toxins to subdue their prey, but research thus far has focused on the small-molecule components of mucus secretions and few protein toxins have been characterized. We carried out a preliminary proteotranscriptomic analysis of putative toxins produced by the hoplonemertean <em>Amphiporus lactifloreus </em>(Hoplonemertea, Amphiporidae). We did not find any variants of known nemertean-specific toxin proteins (neurotoxins, cytotoxins, parbolysins or nemertides) but we identified several toxin-like transcripts expressed strongly in the proboscis, including putative metalloproteinases and sequences resembling sea anemone actitoxins, crown-of-thorn sea star plancitoxins, and multiple classes of inhibitor cystine knot/knottin family proteins. Some of these products were also directly identified in the mucus proteome, supporting their preliminary identification as secreted toxin components. We identified two new nemertean-typical toxin candidates and named them U-nemertotoxin-1 and U-nemertotoxin-2. Our findings provide insight into the largely overlooked venom system of nemerteans and support a hypothesis in which the nemertean proboscis evolved in several steps, from a flesh-melting organ in scavenging nemerteans to a flesh-melting and toxin-secreting venom apparatus in hunting hoplonemerteans.</p>
Supporting transcriptomic data for: "Human transcriptomic response to the VSV-vectored Ebola vaccine"
<p>This is the complete dataset of transcriptomic data obtained from whole blood RNA of volunteers vaccinated with a high dose of the rVSV-ZEBOV vaccine against Ebola virus disease in the Geneva clinical trial.</p> <p>The Counts_Table.csv file contains gene expression data (counts) for genes in the Ion Ampliseq human Gene expression kit panel. </p> <p>The Descriptive_Table.csv contains descriptive data of the subjects for differential expression analysis.</p>
Transcriptome profiling of potato (Solanum tuberosum L.) responses to root-knot nematode (Meloidogyne javanica) infection during a compatible interaction
<p>Supplementary data</p>
Integrative analyses of single-cell transcriptome and immune profiling reveal clonal expansion of T cells in the blood and cerebrospinal fluid of Parkinson's disease
<p>An increasing number of studies has indicated that the immune system plays important roles in the pathogenesis of Parkinson's disease. However, little is known about the contribution of adaptive immune responses in Parkinson's disease. Here, we performed comprehensive integrative analyses of single-cell transcriptome and immune profiling of the blood of 8 Parkinson's patients and 13 healthy controls as well as the cerebrospinal fluid of 6 Parkinson's patients, 4 Alzheimer's patients, 5 mild cognitive impairment (MCI) patients and 9 healthy controls. In total, 22 T cell subsets with distinct functions and clonalities were identified from 121,402 T cells. We observed significant clonal expansion of effector CD8+ T cells in Parkinson's patients, which formed a gradient of transcriptional states from central memory CD8+ T cells to early effector CD8+ T cells followed by terminal effector CD8+ T cells. Shared TCRs in this progression suggest TCRs may be involved in the state transition of CD8+ T cells stimulated by antigens. Notably, we also found that a group of clonally expanded cytotoxic CD4+ T cells were significantly increased in Parkinson's patients compared to controls, suggesting their cytotoxic roles in Parkinson's disease. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of patients with Parkinson's disease. These results reveal an adaptive immune response in the blood and cerebrospinal fluid of Parkinson's disease and provide novel evidence of clonal, antigen-experienced T cells patrolling in the blood and cerebrospinal fluid of Parkinson's disease.</p>
Data from: Transcriptome analysis of two radiated Cycas species and its utilization on species delimitation in Cycas taiwaniana complex
Premise of the study: Cycas is an important gymnosperm with the most diverse of all cycad genera. The taxa within Cycas taiwaniana complex are morphologically similar and difficult to be distinguished by a lack of genomic resources. Methods: We characterized transcriptomes of two closely related and endangered Cycas species endemic to Hainan, China: C. hainanensis and C. changjiangensis. Furthermore, we sequenced three single copy nuclear genes for the Cycas taiwaniana complex developed from transcriptome. Then we evaluated species boundaries based on the multispecies coalescent method implemented in BPP. Results: We obtained 68,184 and 81,561 unigenes for C. changjiangensis and C. hainanensis, respectively. The estimated divergence time revealed the two related species diverged more recently. Six positively selected genes are mainly involved in stimulus responses, suggesting that environmental adaptation may play an important role in the divergence of the two species. The similar peak at 1.0 of Ks distributions for paralogs indicated a common whole-genome duplication event. Results of species delimitation indicated the Cycas taiwaniana complex consisted of three distinct lineages, which corresponds to morphological differentiation. Discussion: Our study provides evidence from transcriptome for taxonomical treatment of the Cycas taiwaniana complex and new insights into evolution of this living fossil genus.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.